Induction Motor (IM) speed control is an area of research that has been in prominence for some time now. In this paper, a nonlinear controller is presented for IM drives. The nonlinear controller is designed based on input-output feedback linearization control technique, combined with sliding mode control (SMC) to obtain a robust, fast and precise control of IM speed. The input-output feedback linearization control decouples the flux control from the speed control and makes the synthesis of linear controllers possible. To validate the performances of the proposed control scheme, we provided a series of simulation results and a comparative study between the performances of the proposed control strategy and those of the feedback linearization control (FLC) schemes. Simulation results show that the proposed control strategy scheme shows better performance than the FLC strategy in the face of system parameters variation.
This work presents a Fuzzy based adaptive Sliding Mode Control scheme to deal with control problem of full vehicle active suspension system and take into consideration the nonlinearities of the spring and damper, unmodeled dynamics as well as the external disturbances. The control law of fuzzy based adaptive Sliding Mode Control scheme will update the parameters of fuzzy sliding mode control by using the stability analysis of Lyapunov criteria such that the convergence in finite time and the stability of the closed loop are ensured. The proposed control scheme consists of four similar subsystems used for the four sides of the vehicle. The sub control scheme contains two loops, the outer loop is built using sliding mode controller with fuzzy estimator to approximate and estimate the unknown parameters in the system. In the inner loop, a controller of type Fractional Order PID (FOPID) is utilized to create the required actuator force. All parameters in the four sub control schemes are optimized utilizing Artificial Bee Colony (ABC) algorithm in order to improve the performance. The results indicate the effectiveness and good achievement of the proposed controller in providing the best ability to limit the vibration with good robustness properties in comparison with passive suspension system and using sliding mode control method. The controlled suspension system shows excellent results when it was tested with and without typical breaking and bending torques.
The presented research introduces a control strategy for a three-phase grid-tied LCL-filtered quasi-Z-source inverter (qZSI) using a Lyapunov-function-based method and cascaded proportional-resonant (PR) controllers. The suggested control strategy ensures the overall stability of the closed-loop system and eliminates any steady-state inaccuracy in the grid current. The inverter current and capacitor voltage reference values of qZSI are created by the utilization of cascaded coupled proportional-resonant (PR) controllers. By utilizing synchronous reference frame and Lyapunov function- based control, the requirement to perform derivative operations and anticipate inductance and capacitance are avoided, resulting in achieving the goal of zero steady-state error in the grid current. The qZSI can accomplish shoot-through control by utilizing a simple boost control method. Computer simulations demonstrate that the suggested control strategy effectively achieves the desired control objectives, both in terms of steady-state and dynamic performance.
A Matlab/Simulink model for the Finite Control Set Model Predictive current Control FCS-MPC based on cost function optimization, with current limit constraints for four-leg VSI is presented in this paper, as a new control algorithm. The algorithm selects the switching states that produce minimum error between the reference currents and the predicted currents via optimization process, and apply the corresponding switching control signals to the inverter switches. The new algorithm also implements current constraints which excludes any switching state that produces currents above the desired references. Therefore, the system response is enhanced since there is no overshoots or deviations from references. Comparison is made between the Space Vector Pulse Width Modulation SVPWM and the FCS-MPC control strategies for the same load conditions. The results show the superiority of the new control strategy with observed reduction in inverter output voltage THD by 10% which makes the FCS-MPC strategy more preferable for loads that requires less harmonics distortion.
Control of Induction Motor (IM) is well known to be difficult owing to the fact the models of IM are highly nonlinear and time variant. In this paper, to achieve accurate control performance of rotor position control of IM, a new method is proposed by using adaptive inverse control (AIC) technique. In recent years, AIC is a very vivid field because of its advantages. It is quite different from the traditional control. AIC is actually an open loop control scheme and so in the AIC the instability problem cased by feedback control is avoided and the better dynamic performances can also be achieved. The model of IM is identified using adaptive filter as well as the inverse model of the IM, which was used as a controller. The significant of using the inverse of the IM dynamic as a controller is to makes the IM output response to converge to the reference input signal. To validate the performances of the proposed new control scheme, we provided a series of simulation results.
A model reference adaptive control of condenser and deaerator of steam power plant is presented. A fuzzy-neural identification is constructed as an integral part of the fuzzy-neural controller. Both forward and inverse identification is presented. In the controller implementation, the indirect controller with propagating the error through the fuzzy-neural identifier based on Back Propagating Through Time (BPTT) learning algorithm as well as inverse control structure are proposed. Simulation results are achieved using Multi Input-Multi output (MIMO) type of fuzzy-neural network. Robustness of the plant is detected by including several tests and observations.
Microgrids (ℳ-grids) can be thought of as a small-scale electrical network comprised of a mix of Distributed Generation (DG) resources, storage devices, and a variety of load species. It provides communities with a stable, secure, and renewable energy supply in either off-grid (grid-forming) or on-grid (grid-following) mode. In this work, a control strategy of coordinated power management for a Low Voltage (LV) ℳ-grid with integration of solar Photovoltaic (PV), Battery Energy Storage System (BESS) and three phase loads operated autonomously or connected to the utility grid has been created and analyzed in the Matlab Simulink environment. The main goal expressed here is to achieve the following points: (i) grid following, grid forming modes, and resynchronization mode between them, (ii) Maximum Power Point Tracking (MPPT) from solar PV using fuzzy logic technique, and active power regulator based boost converter using a Proportional Integral (PI) controller is activated when a curtailment operation is required, (iii) ℳ-grid imbalance compensation (negative sequence) due to large single-phase load is activated, and (iv) detection and diagnosis the fault types using Discrete Wavelet Transform (DWT). Under the influence of irradiance fluctuation on solar plant, the proposed control technique demonstrates how the adopted system works in grid- following mode (PQ control), grid- formation, and grid resynchronization to seamlessly connect the ℳ-grid with the main distribution system. In this system, a power curtailment management system is introduced in the event of a significant reduction in load, allowing the control strategy to be switched from MPPT to PQ control, permitting the BESS to absorb excess power. Also, in grid-following mode, the BESS's imbalance compensation mechanism helps to reduce the negative sequence voltage that occurs at the Point of Common Coupling (PCC) bus as a result of an imbalance in the grid's power supply. In addition to the features described above, this system made use of DWT to detect and diagnose various fault conditions.
Energy limitations have become fundamental challenge for designing WSNs. Network lifetime is the most interested and important metric in WSNs. Many works have been developed for prolonging networks lifetime, in which one of the important work is the control of transmission power. This paper proposes a new fuzzy transmission power control technique that operate together with routing protocols for prolonging WSNs lifetime. Dijkstra shortest path routing is considered as the main routing protocol in this work. This paper mainly focuses on transmission power control scheme for prolonging WSNs lifetime. A performance comparison is depicted for maximum and controlled transmission power. Simulation results show an increase in network lifetime equals to 3.4776 for the proposed fuzzy control. The performance of the proposed fuzzy control technique involves a good improvement and contribution in the field of prolonging networks lifetime by using transmission power control.
Precise power sharing considered is necessary for the effective operation of an Autonomous microgrid with droop controller especially when the total loads change periodically. In this paper, reactive power sharing control strategy that employs central controller is proposed to enhance the accuracy of fundamental reactive power sharing in an islanded microgrid. Microgrid central controller is used as external loop requiring communications to facilitate the tuning of the output voltage of the inverter to achieve equal reactive power sharing dependent on reactive power load to control when the mismatch in voltage drops through the feeders. Even if central controller is disrupted the control strategy will still operate with conventional droop control method. additionally, based on the proposed strategy the reactive power sharing accuracy is immune to the time delay in the central controller. The developed of the proposed strategy are validated using simulation with detailed switching models in PSCAD/EMTDC.
Load Frequency Control (LFC) is a basic control strategy for proper operation of the power system. It ensures the ability of each generator in regulating its output power in such way to maintain system frequency and tie-line power of the interconnected system at prescribed levels. This article introduces comprehensive comparative study between Chaos Optimization Algorithm (COA) and optimal control approaches, such as Linear Quadratic Regulator (LQR), and Optimal Pole Shifting (OPS) regarding the tuning of LFC controller. The comparison is extended to the control approaches that result in zero steady-state frequency error such as Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Ziegler-Nicholas method is widely adopted for tuning such controllers. The article then compares between PI and PID controllers tuned via Ziegler-Nicholas and COA. The optimal control approaches as LQR and OPS have the characteristic of steady-state error. Moreover, they require the access for full state variables. This limits their applicability. Whereas, Ziegler-Nicholas PI and PID controllers have relatively long settling time and high overshoot. The controllers tuned via COA remedy the defects of optimal and zero steady-state controllers. The performance adequacy of the proposed controllers is assessed for different operating scenarios. Matlab and its dynamic platform, Simulink, are used for stimulating the system under concern and the investigated control techniques. The simulation results revealed that COA results in the smallest settling time and overshoot compared with traditional controllers and zero steady-state error controllers. In the overshoot, COA produces around 80% less than LQR and 98.5% less than OPS, while in the settling time, COA produces around 81% less than LQR and 95% less than OPS. Moreover, COA produces the lowest steady-state frequency error. For Ziegler-Nicholas controllers, COA produces around 53% less in the overshoot and 42% less in the settling time.
In this paper, a control strategy for a combination PV-BESS-SC hybrid system in islanded microgrid with a DC load is designed and analyzed using a new topology. Although Battery Energy Storage System (BESS) is employed to keep the DC bus voltage stable; however, it has a high energy density and a low power density. On the other hand, the Supercapacitor (SC) has a low energy density but a high-power density. As a result, combining a BESS and an SC is more efficient for power density and high energy. Integrating the many sources is more complicated. In order to integrate the SC and BESS and deliver continuous power to the load, a control strategy is required. A novel method for controlling the bus voltage and energy management will be proposed in this paper. The main advantage of the proposed system is that throughout the operation, the State of Charging (SOC), BESS current, and SC voltage and current are all kept within predetermined ranges. Additionally, SC balances fast- changing power surges, while BESS balances slow-changing power surges. Therefore, it enhances the life span and minimizes the current strains on BESS. To track the Maximum Power Point (MPP) or restrict power from the PV panel to the load, a unidirectional boost converter is utilized. Two buck converters coupled in parallel with a boost converter are proposed to charge the hybrid BESS-SC. Another two boost converters are used to manage the discharge operation of the BESS-SC storage in order to reduce losses. The simulation results show that the proposed control technique for rapid changes in load demand and PV generation is effective. In addition, the proposed technique control strategy is compared with a traditional control strategy.
The reluctance of industry to allow wireless paths to be incorporated in process control loops has limited the potential applications and benefits of wireless systems. The challenge is to maintain the performance of a control loop, which is degraded by slow data rates and delays in a wireless path. To overcome these challenges, this paper presents an application–level design for a wireless sensor/actuator network (WSAN) based on the “automated architecture”. The resulting WSAN system is used in the developing of a wireless distributed control system (WDCS). The implementation of our wireless system involves the building of a wireless sensor network (WSN) for data acquisition and controller area network (CAN) protocol fieldbus system for plant actuation. The sensor/actuator system is controlled by an intelligent digital control algorithm that involves a controller developed with velocity PID- like Fuzzy Neural Petri Net (FNPN) system. This control system satisfies two important real-time requirements: bumpless transfer and anti-windup, which are needed when manual/auto operating aspect is adopted in the system. The intelligent controller is learned by a learning algorithm based on back-propagation. The concept of petri net is used in the development of FNN to get a correlation between the error at the input of the controller and the number of rules of the fuzzy-neural controller leading to a reduction in the number of active rules. The resultant controller is called robust fuzzy neural petri net (RFNPN) controller which is created as a software model developed with MATLAB. The developed concepts were evaluated through simulations as well validated by real-time experiments that used a plant system with a water bath to satisfy a temperature control. The effect of disturbance is also studied to prove the system's robustness.
In this paper an integrated electronic system has been designed, constructed and tested. The system utilizes an interface card through the parallel port in addition to some auxiliary circuits to perform fuzzy control operations for DC motor speed control with load and no load. Software is written using (C++ language Ver. 3.1) to display the image as control panel for different types of both conventional and fuzzy control. The main task of the software is to simulate: first, how to find out the correct parameters for fuzzy logic controller (membership’s function, rules and scaling factor). Second, how to evaluate the gain factors (K P , K I and K D ) by Ziegler-Nichols method. When executing any type of control process the efficiency is estimated by drawing the relative speed response for this control.
A practical method of robust generalized predictive controller (GPC) application is developed using a combination of Ziegler-Nichols type functions relating the GPC controller parameters to a first order with time delay process parameters and a model matching controller. The GPC controller and the model matching controller are used in a master/slave configuration, with the GPC as the master controller and the model matching controller as the slave controller. The model matching controller parameters are selected to obtain the desired overall performance. The effectiveness of the proposed control method is tested by simulation using a mathematical model of the boiler super heater temperature process.
This paper deals with the application of Fuzzy-Neural Networks (FNNs) in multi-machine system control applied on hot steel rolling. The electrical drives that used in rolling system are a set of three-phase induction motors (IM) controlled by indirect field-oriented control (IFO). The fundamental goal of this type of control is to eliminate the coupling influence though the coordinate transformation in order to make the AC motor behaves like a separately excited DC motor. Then use Fuzzy-Neural Network in control the IM speed and the rolling plant. In this work MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results are presented to verify the effectiveness of the proposed control schemes. It is found that the proposed system is robust in that it eliminates the disturbances considerably.
This paper presents a low-cost Brushless DC (BLDC) motor drive system with fewer switches. BLDC motors are widely utilized in variable speed drives and industrial applications due to their high efficiency, high power factor, high torque, low maintenance, and ease of control. The proposed control strategy for robust speed control is dependent on two feedback signals which are speed sensor loop which is regulated by Sliding Mode Controller (SMC) and current sensor loop which is regulated by Proportional-Integral (PI) for boosting the drive system adaptability. In this work, the BLDC motor is driven by a four-switch three-phase inverter emulating a three-phase six switch inverter, to reduce switching losses with a low complex control strategy. In order to reach a robust performance of the proposed control strategy, the Lévy Flight Distribution (LFD) technique is used to tune the gains of PI and SMC parameters. The Integral Time Absolute Error (ITAE) is used as a fitness function. The simulation results show the SMC with LFD technique has superiority over conventional SMC and optimization PI controller in terms of fast-tracking to the desired value, reduction speed error to the zero value, and low overshoot under sudden change conditions.
Hybrid electric vehicles have received considerable attention because of their ability to improve fuel consumption compared to conventional vehicles. In this paper, a series-parallel hybrid electric vehicle is used because they combine the advantages of the other two configurations. In this paper, the control unit for a series-parallel hybrid electric vehicle is implemented using a Nonlinear Model Predictive Control (NMPC) strategy. The NMPC strategy needs to create a vehicle energy management optimization problem, which consists of the cost function and its constraints. The cost function describes the required control objectives, which are to improve fuel consumption and obtain a good dynamic response to the required speed while maintaining a stable value of the state of charge (SOC) for batteries. While the cost function is subject to the physical constraints and the mathematical prediction model that evaluate vehicle's behavior based on the current vehicle measurements. The optimization problem is solved at each sampling step using the (SQP) algorithm to obtain the optimum operating points of the vehicle's energy converters, which are represented by the torque of the vehicle components.
This paper suggests the use of the traditional proportional-integral-derivative (PID) controller to control the speed of multi Permanent Magnet Synchronous Motors (PMSMs). The PMSMs are commonly used in industrial applications due to their high steady state torque, high power, high efficiency, low inertia and simple control of their drives compared to the other motors drives. In the present study a mathematical model of three phase four poles PMSM is given and simulated. The closed loop speed control for this type of motors with voltage source inverter and abc to dq blocks are designed. The multi (Master/Slaves approach) method is proposed for PMSMs. Mathwork's Matlab/Simulink software package is selected to implement this model. The simulation results have illustrated that this control method can control the multi PMSMs successfully and give better performance.
In this study, a distributed power control algorithm is proposed for Dynamic Frequency Hopping Optical-CDMA (DFH-OCDMA) system. In general, the DFH-OCDMA can support higher number of simultaneous users compared to other OCDMA techniques. However, the performance of such system degrades significantly as the received power does lower than its minimum threshold. This may obviously occur in a DFH-OCDMA network with near-far problem which consist of different fiber lengths among the users, that resulting to unequal power attenuation. The power misdistribution among simultaneous active users at the star coupler would degrade the Bit Error Rate (BER) performance for users whose transmitting signals with longer fiber lengths. In order to solve these problems, we propose an adaptive distributed power control technique for DFH-OCDMA to satisfy the target Signal to Noise Ratio (S to R) for all users. Taking into account the noise effects of Multiple Access Interference (MAI), Phase Induced Intensity oise (PII) and shot noise, the system can support 100% of users with power control as compared to 33% without power control when the initial transmitted power was -1dBm with 30 simultaneous users.
The robot is a repeated task plant. The control of such a plant under parameter variations and load disturbances is one of the important problems. The aim of this work is to design Genetic-Fuzzy controller suitable for online applications to control single link rigid robot arm plant. The genetic-fuzzy online controller (forward controller) contains two parts, an identifier part and model reference controller part. The identification is based on forward identification technique. The proposed controller it tested in normal and load disturbance conditions.
The main purpose of using the suspension system in vehicles is to prevent the road disturbance from being transmitted to the passengers. Therefore, a precise controller should be designed to improve the performances of suspension system. This paper presents a modeling and control of the nonlinear full vehicle active suspension system with passenger seat utilizing Fuzzy Model Reference Learning Control (FMRLC) technique. The components of the suspension system are: damper, spring and actuator, all of those components have nonlinear behavior, so that, nonlinear forces that are generated by those components should be taken into account when designed the control system. The designed controller consumes high power so that when the control system is used, the vehicle will consume high amount of fuel. It notes that, when vehicle is driven on a rough road; there will be a shock between the sprung mass and the unsprung mass. This mechanical power dissipates and converts into heat power by a damper. In this paper, the wasted power has reclaimed in a proper way by using electromagnetic actuator. The electromagnetic actuator converts the mechanical power into electrical power which can be used to drive the control system. Therefore, overall power consumption demand for the vehicle can be reduced. When the electromagnetic actuator is used three main advantages can be obtained: firstly, fuel consumption by the vehicle is decreased, secondly, the harmful emission is decreases, therefore, our environment is protected, and thirdly, the performance of the suspension system is improved as shown in the obtained results.
Unmanned aerial vehicles (UAV), have enormous important application in many fields. Quanser three degree of freedom (3-DOF) helicopter is a benchmark laboratory model for testing and validating the validity of various flight control algorithms. The elevation control of a 3-DOF helicopter is a complex task due to system nonlinearity, uncertainty and strong coupling dynamical model. In this paper, an RBF neural network model reference adaptive controller has been used, employing the grate approximation capability of the neural network to match the unknown and nonlinearity in order to build a strong MRAC adaptive control algorithm. The control law and stable neural network updating law are determined using Lyapunov theory.
In this article, a PD-type iterative learning control algorithm (ILC) is proposed to a nonlinear time-varying system for cases of measurement disturbances and the initial state errors. The proposed control approach uses a simple structure and has an easy implementation. The iterative learning controller was utilized to control a constant current source inverter (CSI) with pulse width modulation (PWM); subsequently the output current trajectory converged the sinusoidal reference signal and provided constant switching frequency. The learning controller's parameters were tuned using particle swarm optimization approach to get best optimal control for the system output. The tracking error limit is achieved using the convergence exploration. The proposed learning control scheme was robust against the error in initial conditions and disturbances which outcome from the system modeling inaccuracies and uncertainties. It could correct the distortion of the inverter output current waveform with less computation and less complexity. The proposed algorithm was proved mathematically and through computer simulation. The proposed optimal learning method demonstrated good performances.
An efficient feedback scheduling scheme based on the proposed Feed Forward Neural Network (FFNN) scheme is employed to improve the overall control performance while minimizing the overhead of feedback scheduling which exposed using the optimal solutions obtained offline by mathematical optimization methods. The previously described FFNN is employed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. The proposed intelligent scheduler will be examined with different optimization algorithms. An inverted pendulum cost function is used in these experiments. Then, simulation of three inverted pendulums as intelligent Real Time System (RTS) is described in details. Numerical simulation results demonstrates that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling
In this paper, we consider robust control of nonlinear systems, via inclusion nonlinear systems solution and $H_{\infty}$ controller using singular perturbation method. First, using a technique for solving inclusion nonlinear systems, we transform the nonlinear system to an ordinary nonlinear system. Then using normal form equations, we eliminate the nonlinear part of the system matrix of equations of the system and transform it to a linear diagonal form. Separating new equations into slow and fast subsystems, due to the singular perturbation method and with the assumption of norm-boundedness of the fast dynamics, we can treat them as disturbance and design an $H_{\infty}$ controller for a system with a lower order than the original one that stabilizes the overall closed loop system. The proposed method is applied to a nominal system.
Induction Motors have been used as the workhorse in the industry for a long time due to its easy build, high robustness, and generally satisfactory efficiency. However, they are significantly more difficult to control than DC motors. One of the problems which might cause unsuccessful attempts for designing a proper controller would be the time varying nature of parameters and variables which might be changed while working with the motion systems. One of the best suggested solutions to solve this problem would be the use of Sliding Mode Control (SMC). This paper presents the design of a new controller for a vector control induction motor drive that employs an outer loop speed controller using SMC. Several tests were performed to evaluate the performance of the new controller method, and two other sliding mode controller techniques. From the comparative simulation results, one can conclude that the new controller law provides high performance dynamic characteristics and is robust with regard to plant parameter variations.
Use of multilevel inverters is becoming popular in the recent years for high power applications. The important feature of these inverters is of having low harmonics content in the output voltage. The switching angles in a multilevel inverter are computed so as to produce an ac output voltage with minimum harmonics. A new control circuit is designed to achieve these angles. This control circuit has the ability to control the RMS output voltage using sinusoidal pulse width modulation (SPWM). The results presented in this work prove the ability of the designed control circuit to gain the required ac output voltage with minimum distortion.
Soft computing control system have been applied in various applications particularly in the fields of robotics controls. The advantage of having a soft computing controls methods is that it enable more flexibility to the control system compared with conventional model based controls system. In this paper, a UAV airship is controlled using fuzzy logic for its propulsion and steering system. The airship is tested on a simulation level before test flight. The prototype airship has on board GPS and compass for telemetry and transmitted to the ground control system via a wireless link.
This work focuses on the use of the Linear Quadratic Gaussian (LQG) technique to construct a reliable Static VAr Compensator (SVC), Thyristor Controlled Series Compensator (TCSC), and Excitation System controller for damping Subsynchronous Resonance ( SSR ) in a power system. There is only one quantifiable feedback signal used by the controller (generator speed deviation). It is also possible to purchase this controller in a reduced-order form. The findings of the robust control are contrasted with those of the "idealistic" full state optimal control. The LQG damping controller's regulator robustness is then strengthened by the application of Loop Transfer Recovery (LTR). Nonlinear power system simulation is used to confirm the resilience of the planned controller and demonstrates how well the regulator dampens power system oscillations. The approach dampens all torsional oscillatory modes quickly while maintaining appropriate control actions, according to simulation results.
A torsional rotating system is considered for the investigation of passive vibration control using dual loop controllers Proportional-Integral-Derivative (PID) with derivative (D) gain and Proportional – Derivative (PD) with Integral (I) controllers. The controllers are used as low pass filters. Simulation of the models using Matlab-Simulink have been built in this work for torsional vibration control. A comparison between the two controllers with uncontrolled system have been carried out. Results show that the PD – I control is the best method which gives better stability response than the PID – D control.
In this paper the identification and control for the impressed current cathodic protection (ICCP) system are present. Firstly, an identification model using an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) was implemented. The identification model consists of four inputs which are the aeration flow rates, the temperature, conductivity, and protection current, and one output that represented by the structure-to-electrolyte potential. The used data taken from an experimental CP system model, type impressed current submerged sample pipe carbon steel. Secondly, two control techniques are used. The first control technique use a conventional Proportional-Integral-Derivative (PID) controller, while the second is the fuzzy controller. The PID controller can be applied to control ICCP system and quite easy to implement. But, it required very fine tuning of its parameters based on the desired value. Furthermore, it needed time response more than fuzzy controller to track reference voltage. So the fuzzy controller has a faster and better response.
This paper principally advises a simple and reliable control for Static Synchronous Compensator (STATCOM) in a stand-alone wind driven self-excited induction generator power system. The control was proposed based on instantaneous P-Q theory. The advised control enjoys the merits of robustness, reliability and simplicity. The paper also proposes a dimensioning procedure for the STATCOM that involves advising an annotative analytical expression for sizing the DC-link capacitor. This procedure has the advantages of applicability for different reactive power compensators that depend on a separate DC-link in its operation. Comprehensive simulation results in Matlab environment were illustrated for corroborating the performance of the advised control under rigorous operating scenarios. The results show the feasibility, reliability and practicability of the proposed controller.
This Paper presents a novel hardware design methodology of digital control systems. For this, instead of synthesizing the control system using Very high speed integration circuit Hardware Description Language (VHDL), LabVIEW FPGA module from National Instrument (NI) is used to design the whole system that include analog capture circuit to take out the analog signals (set point and process variable) from the real world, PID controller module, and PWM signal generator module to drive the motor. The physical implementation of the digital system is based on Spartan-3E FPGA from Xilinx. Simulation studies of speed control of a D.C. motor are conducted and the effect of a sudden change in reference speed and load are also included.
Nowadays, there are no accurate records of the various quantities of fuel being dispensed at the Iraqi fuel stations. All such reports are usually paper-based and are missing the required precision to control this valuable commodity, which could lead to misuse or illegal sale of fuel. This paper presents a control system based on RFID technology to monitor the supply and dispensing of vehicle fuel in Baghdad. The system consists of RFID readers attached to fuel dispensers and pumps, and RFID tags assigned to the vehicles and the trucks used for delivering fuel to the fuel stations. A hardware part is connected to conventional fuel dispensers and machines, located at both the stations and the fuel refineries or depots, which makes them work under the control of the RFID system, without the need to rebuild new dispensers. The designed system database is a centralized one located on a cloud server, in order to allow fuel stations to communicate with it and do the required transactions. Throughout this system, different reports can be generated, which can give important online statistics about the movement of fuel supplies over the city.
In this paper, enhancing dynamic performance in power systems through load frequency control (LFC) is explored across diverse operating scenarios. A new Neural Network Model Predictive Controller (NN-MPC) specifically tailored for two-zone load frequency power systems is presented. ” Make your paper more scientific. The NN-MPC marries the predictive accuracy of neural networks with the robust capabilities of model predictive control, employing the nonlinear Levenberg-Marquardt method for optimization. Utilizing local area error deviation as feedback, the proposed controller’s efficacy is tested against a spectrum of operational conditions and systemic variations. Comparative simulations with a Fuzzy Logic Controller (FLC) reveal the proposed NN-MPC’s superior performance, underscoring its potential as a formidable solution in power system regulation.
According to the growing interest in the soft robotics research field, where various industrial and medical applications have been developed by employing soft robots. Our focus in this paper will be the Pneumatic Muscle Actuator (PMA), which is the heart of the soft robot. Achieving an accurate control method to adjust the actuator length to a predefined set point is a very difficult problem because of the hysteresis and nonlinearity behaviors of the PMA. So the construction and control of a 30 cm soft contractor pneumatic muscle actuator (SCPMA) were done here, and by using different strategies such as the PID controller, Bang-Bang controller, Neural network controller, and Fuzzy controller, to adjust the length of the (SCPMA) between 30 cm and 24 cm by utilizing the amount of air coming from the air compressor. All of these strategies will be theoretically implemented using the MATLAB/Simulink package. Also, the performance of these control systems will be compared with respect to the time-domain characteristics and the root mean square error (RMSE). As a result, the controller performance accuracy and robustness ranged from one controller to another, and we found that the fuzzy logic controller was one of the best strategies used here according to the simplicity of the implementation and the very accurate response obtained from this method.
In this paper, a combined RBF neural network sliding mode control and PD adaptive tracking controller is proposed for controlling the directional heading course of a ship. Due to the high nonlinearity and uncertainty of the ship dynamics as well as the effect of wave disturbances a performance evaluation and ship controller design is stay difficult task. The Neural network used for adaptively learn the uncertain dynamics bounds of the ship and their output used as part of the control law moreover the PD term is used to reduce the effect of the approximation error inherited in the RBF networks. The stability of the system with the combined control law guaranteed through Lyapunov analysis. Numeric simulation results confirm the proposed controller provide good system stability and convergence.
In this paper a fully neural network-based structure have been proposed to control speeds of rolling stands of a steel rolling mill. The structure has property of controlling the motors speed such that the loop height between each successive stands tracks the required height reference. Synchronization between these stands is also maintained so that the metal flow rate from first stand to the last stand is kept constant. This structure is robust against the disturbance effects such as, torque loading, plant parameter change... etc. The results reveal performance of the structure as a comparison with the conventional control method for a practical worksheet data.
Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients loose all communication pathways except for their sensory and cognitive functions. One of the possible rehabilitation methods for these patients is to provide a brain machine interface (BMI) for communication; the BMI uses the electrical activity of the brain detected by scalp EEG electrodes. Classification of EEG signals extracted during mental tasks is a technique for designing a BMI. In this paper a BMI design using five mental tasks from two subjects were studied, a combination of two tasks is studied per subject. An Elman recurrent neural network is proposed for classification of EEG signals. Two feature extraction algorithms using overlapped and non overlapped signal segments are analyzed. Principal component analysis is used for extracting features from the EEG signal segments. Classification performance of overlapping EEG signal segments is observed to be better in terms of average classification with a range of 78.5% to 100%, while the non overlapping EEG signal segments show better classification in terms of maximum classifications.
This paper proposes a new control circuit to control the switching of the main switches of the used Zero Current Zero Voltage Transition (ZCZVT) inverter to ensure Zero Current and Zero Voltage Switching (ZCZVS). The reverse recovery losses of the main diodes are minimized and the auxiliary switches of the commutation cell are turned on at Zero Current Switching (ZCS) and off at ZCZVS. The commutation losses are practically reduced to zero due to ZCS. Sinusoidal Pulse Width Modulation (SPWM) is used to perform the switching of the power semiconductor devices and to control the output voltage value. MATLAB software is used to simulate the inverter circuit. Simulation results are presented to demonstrate the feasibility of the proposed control circuit.
In this paper, a proposed control strategy is presented to improve the performance of the pulse width modulation (PWM) boost type rectifier when operating under different supply voltage conditions (balanced, unbalanced, and distorted three-phase supply voltages). The proposed control strategy is divided into two parts, the first part is voltage controller and the second part is current controller. In the voltage controller, Repetitive Controller (RC) is used to reduce the even order harmonics in the regulated output dc voltage so small output capacitor (filter) is used instead of large capacitor. RC also reduces the even order harmonics which appear in the reflected dc current (I MAX ), this leads to reduce the odd order harmonics which appear in the input currents. While in the current controller, Enhanced Phase Locked Loop (EPLL) technique is used to obtain sinusoidal and balanced three phases, to construct the reference currents, which are in phase with the fundamental supply voltages. Therefore, the supply-side power factor is kept close to unity. A proportional controller is used to give excellent tracking between the line and the reference currents. The complete system with the proposed control strategy are simulated using Matlab/Simulink. The results for the complete system using repetitive voltage controller are obtained and compared to the results of the system with the conventional voltage controller (Proportional-Integral (PI) controller connected in series with a Low Pass Filter (LPF)). The results with the repetitive controller show better response and stable operation in the steady state under different input voltage conditions, as well as in the transient response under changing the load condition. — Enhanced Phase Locked Loop,Repetitive Controller,Three-Phase PWM Boost Rectifier, Proportional-Integral controller. I. INTRODUCTION The boost type PWM rectifier has been increasingly employed in recent years since it offers the possibility of a low distortion line current withnear unity power factor for any load condition. Another advantage over traditional phase-controlled thyristor rectifiers is its capability for nearly instantaneous reversal of power flow. Unfortunately, the features of the PWM boost type rectifier are fully realized only when the supply three phase input voltages are balanced. It has been shown that unbalanced input voltages cause an abnormal second order harmonic at the dc output voltage, which reflects back to the input causing third-order harmonic current to flow. Next, the third-order harmonic current causes a fourth-order harmonic voltage on the dc bus, and so on. This results in the appearance of even harmonics at the dc output and odd harmonics in the input currents. An attempt was made to reduce low order harmonics at the input and the output of the PWM Boost Type Rectifier under unbalance input voltages [1]. The authors in [2] used two synchronous reference frames: a positive- sequence current regulated by a
The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PI λ D μ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.
Bin picking robots require vision sensors capable of recognizing objects in the bin irrespective of the orientation and pose of the objects inside the bin. Bin picking systems are still a challenge to the robot vision research community due to the complexity of segmenting of occluded industrial objects as well as recognizing the segmented objects which have irregular shapes. In this paper a simple object recognition method is presented using singular value decomposition of the object image matrix and a functional link neural network for a bin picking vision system. The results of the functional link net are compared with that of a simple feed forward net. The network is trained using the error back propagation procedure. The proposed method is robust for recognition of objects.
It's not easy to implement the mixed / optimal controller for high order system, since in the conventional mixed / optimal feedback the order of the controller is much than that of the plant. This difficulty had been solved by using the structured specified PID controller. The merit of PID controllers comes from its simple structure, and can meets the industry processes. Also it have some kind of robustness. Even that it's hard to PID to cope the complex control problems such as the uncertainty and the disturbance effects. The present ideas suggests combining some of model control theories with the PID controller to achieve the complicated control problems. One of these ideas is presented in this paper by tuning the PID parameters to achieve the mixed / optimal performance by using Intelligent Genetic Algorithm (IGA). A simple modification is added to IGA in this paper to speed up the optimization search process. Two MIMO example are used during investigation in this paper. Each one of them has different control problem.
In recent years, wireless microrobots have gotten more attention due to their huge potential in the biomedical field, especially drug delivery. Microrobots have several benefits, including small size, low weight, sensitivity, and flexibility. These characteristics have led to microscale improvements in control systems and power delivery with the development of submillimeter-sized robots. Wireless control of individual mobile microrobots has been achieved using a variety of propulsion systems, and improving the actuation and navigation of microrobots will have a significant impact. On the other hand, actuation tools must be integrated and compatible with the human body to drive these untethered microrobots along predefined paths inside biological environments. This study investigated key microrobot components, including medical applications, actuation systems, control systems, and design schemes. The efficiency of a microrobot is impacted by many factors, including the material, structure, and environment of the microrobot. Furthermore, integrating a hybrid actuation system and multimodal imaging can increase the microrobot’s navigation effect, imaging algorithms, and working environment. In addition, taking into account the human body’s moving distance, autonomous actuating technology could be used to deliver microrobots precisely and quickly to a specific position using a combination of quick approaches.
In this paper we present the details of methodology pursued in implementation of an HMI and Demo Temperature Monitoring application for wireless sensor-based distributed control systems. The application of WSN for a temperature monitoring and control is composed of a number of sensor nodes (motes) with a networking capability that can be deployed for monitoring and control purposes. The temperature is measured in the real time by the sensor boards that sample and send the data to the monitoring computer through a base station or gateway. This paper proposes how such monitoring system can be setup emphasizing on the aspects of low cost, energy-efficient, easy ad-hoc installation and easy handling and maintenance. This paper focuses on the overall potential of wireless sensor nodes and networking in industrial applications. A specific case study is given for the measurement of temperature (with thermistor or thermocouple), humidity, light and the health of the WSN. The focus was not on these four types of measurements and analysis but rather on the design of a communication protocol and building of an HMI software for monitoring. So, a set of system design requirements are developed that covered the use of the wireless platforms, the design of sensor network, the capabilities for remote data access and management, the connection between the WSN and an HMI software designed with MATLAB.
In this article, a robust control technique for 2-DOF helicopter system is presented. The 2-DOF helicopter system is 2 inputs and 2 outputs system that is suffering from the high nonlinearity and strong coupling. This paper focuses on design a simple, robust, and optimal controller for the helicopter system. Moreover, The proposed control method takes into account effects of the measurement noise in the closed loop system that effect on the performance of controller as well as the external disturbance. The proposed controller combines low pass filter with robust PID controller to ensure good tracking performance with high robustness. A low pass filter and PID controller are designed based H∞weighted mixed sensitivity. Nonlinear dynamic model of 2-DOF helicopter system linearized and then decoupled into pitch and yaw models. Finally, proposed controller applied for each model. Matlab program is used to check effectiveness the proposed control method. Simulation results show that the proposed controllers has best tracking performance with no overshot and the smallest settling time with respect to standard H∞and optimized PID controller.
PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability.
This paper presents a method for improving the speed profile of a three phase induction motor in direct torque control (DTC) drive system using a proposed fuzzy logic based speed controller. A complete simulation of the conventional DTC and closed-loop for speed control of three phase induction motor was tested using well known Matlab/Simulink software package. The speed control of the induction motor is done by using the conventional proportional integral (PI) controller and the proposed fuzzy logic based controller. The proposed fuzzy logic controller has a nature of (PI) to determine the torque reference for the motor. The dynamic response has been clearly tested for both conventional and the proposed fuzzy logic based speed controllers. The simulation results showed a better dynamic performance of the induction motor when using the proposed fuzzy logic based speed controller compared with the conventional type with a fixed (PI) controller.
This paper examines the use of non-integer switching frequency ratios in digitally controlled DC-DC converters. In particular the execution of multiple control algorithms using a Digital Signal Processor (DSP) for this application is analyzed. The variation in delay from when the Analog to Digital Converter (ADC) samples the output voltage to when the duty cycle is updated is identified as a critical factor to be considered when implementing the digital control system. Fixing the delay to its maximum value is found to produce reasonable performance using a conventional DSP. A modification of the DSP’s interrupt control logic is proposed here that minimizes the delay and thereby yields improved performance compared with that given by a standard interrupt controller. Applying this technique to a multi-rail power supply system provides the designer with the flexibility to choose arbitrary switching frequencies for individual converters, thereby allowing optimization of the efficiency and performance of the individual converters.
In medium voltage and high-power drive applications, pulse width modulation (PWM) techniques are widely used to achieve effective speed control of AC motors. In real-time, an industrial drive system requires reduced hardware complexity and low computation time. The reliability of the AC drive can be improved with the FPGA (field programmable gate array) hardware equipped with digital controllers. To improve the performance of AC drives, a new FPGA-based Wavect real-time prototype controller (Xilinx ZYNQ-7000 SoC) is used to verify the effectiveness of the controller. These advanced controllers are capable of reducing computation time and enhancing the drive performance in real- time applications. The comparative performance analysis is carried out for the most commonly used voltage source inverter (VSI)-based PWM techniques such as sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) for three-phase, two-level inverters. The comparative study shows the SVPWM technique utilizes DC bus voltage more effectively and produces less harmonic distortion in terms of higher output voltage, flexible control of output frequency, and reduced harmonic distortion at output voltage for motor control applications. The simulation and hardware results are verified and validated by using MATLAB/Simulink software and FPGA-based Wavect real-time controller respectively.
This work presents the mathematical model for a torso compass gait biped robot with three degrees of freedom (DOF) which is comprised of two legs and torso. Euler Lagrange method's is used to drive the dynamic equation of robot with computed control is used as a controller. The relative angles are used to simplify the robot equation and get the symmetry of the matrix. Convention controller uses critical sampling to find the value of KP and Kv in computed controller, in this paper the Genetic optimization method is used to find the optimal value of KP and Kv with suitable objective function which employ the error and overshoot to make the biped motion smooth as possible. To investigate the work of robot a Matlab 2013b is used and the result show success of modeling.
In this article, a novel three dimensional chaotic systems is presented. An extensive analysis including Lyapunov exponents, dissipation, symmetry, rest points with their properties is introduced. An adaptive tracking control system for the proposed chaos system has been designed. Also, synchronization system for two identical systems has been designed. The simulation results showed the effectiveness of the designed tracking and synchronization control systems.
The objective of this paper is to design an efficient control scheme for car suspension system. The purpose of suspension system in vehicles is to get more comfortable riding and good handling with road vibrations. A nonlinear hydraulic actuator is connected to passive suspension system in parallel with damper. The Particles Swarm Optimization is used to tune a PID controller for active suspension system. The designed controller is applied for quarter car suspension system and result is compared with passive suspension system model and input road profile. Simulation results show good performance for the designed controller I. I NTRODUCTION Suspensions systems can be classified into three types are (passive, simi active and active). Figs. 1, 2 and 3 below shows the three types of Quarter car suspension system and hydraulic actuator position in each type.[1] Fig. 1 Passive Quarter Car Model Fig. 2 Simi-Active Quarter Car Model Fig. 3 Active Quarter Car Model In passive suspension systems the main parts are springs and hydraulic dumpers. The main job of these dumpers is to decrease the road profile and vibration effects into driver and passenger’s cabin. In active suspension system there are three parts under spring mass (body of car), spring, dumper and hydraulic actuator are connected in parallel. In this paper an additional parts is added to passive suspension system in parallel with springs and dumpers called a hydraulic actuator to get an active suspension system. This hydraulic actuator is a nonlinear part and it is controlled by spool valve. The mechanism of this actuator is to decrease the road profile and vibration from passive suspension system to get more comfortable riding. By using PID controller trained by Particle Swarm Optimization (PSO) to find optimal values of proportional, divertive and Quarter Car Active Suspension System Control Using PID Controller tuned by PSO Wissam H. Al-Mutar Turki Y. Abdalla Electrical Eng. Computer Eng. University of Basrah University of Basrah Basrah. Iraq. Basrah. Iraq. Spring Mass Unpring Mass K Kt C Ct Spring Mass Unpring K K C C Spring Mass Unpring Mass K Kt C F Ct اﻟﻤﺠﻠﺔ اﻟﻌﺮاﻗﻴﺔ ﻟﻠﻬﻨﺪﺳﺔ اﻟﻜﻬﺮﺑﺎﺋﻴﺔ واﻻﻟﻜﺘﺮوﻧﻴﺔ Iraq J. Electrical and Electronic Engineering ﻡﺠﻠﺪ 11 ، اﻟﻌﺪد 2 ، 2015 Vol.11 No.2 , 2015 Active suspension, PSO, PID controller, quarter car
In this paper, explicit model predictive controller is applied to an inverted pendulum apparatus. Explicit solutions to constrained linear model predictive controller can be computed by solving multi-parametric quadratic programs. The solution is a piecewise affine function, which can be evaluated at each sample to obtain the optimal control law. The on-line computation effort is restricted to a table-lookup. This admits implementation on low cost hardware at high sampling frequencies in real-time systems with high reliability and low software complexity. This is useful for systems with limited power and CPU resources.
Among all control methods for induction motor drives, Direct Torque Control (DTC) seems to be particularly interesting being independent of machine rotor parameters and requiring no speed or position sensors. The DTC scheme is characterized by the absence of PI regulators, coordinate transformations, current regulators and PWM signals generators. In spite of its simplicity, DTC allows a good torque control in steady state and transient operating conditions to be obtained. However, the presence of hysterics controllers for flux and torque could determine torque and current ripple and variable switching frequency operation for the voltage source inverter. This paper is aimed to analyze DTC principles, and the problems related to its implementation, especially the torque ripple and the possible improvements to reduce this torque ripple by using a proposed fuzzy based duty cycle controller. The effectiveness of the duty ratio method was verified by simulation using Matlab/Simulink software package. The results are compared with that of the traditional DTC models.
The hybrid AC/DC microgrid is considered to be more and more popular in power systems as increasing loads. In this study, it is presented that the hybrid AC/DC microgrid is modeled with some renewable energy sources (e.g. solar energy, wind energy) in the residential of the consumer in order to meet the demand. The power generation and consumption are undergoing a major transformation. One of the tendencies is to integrate microgrids into the distribution network with high penetration of renewable energy resources. In this paper, a new distributed coordinated control is proposed for hybrid microgrid, which could apply to both grid-connected mode and islanded mode with hybrid energy resources and variable loads. The proposed system permits coordinated operation of distributed energy resources to concede necessary active power and additional service whenever required. Also, the maximum power point tracking technique is applied to both photovoltaic stations and wind turbines to extract the maximum power from the hybrid power system during the variation of the environmental conditions. Finally, a simulation model is built with a photovoltaic, wind turbine, hybrid microgrid as the paradigm, which can be applied to different scenarios, such as small-sized commercial and residential buildings. The simulation results have verified the effectiveness and feasibility of the introduced strategy for a hybrid microgrid operating in different modes
The multilevel inverter is attracting the specialist in medium and high voltage applications, among its types, the cascade H bridge Multi-Level Inverter (MLI), commonly used for high power and high voltage applications. The main advantage of the conventional cascade (MLI) is generated a large number of output voltage levels but it demands a large number of components that produce complexity in the control circuit, and high cost. Along these lines, this paper presents a brief about the non-conventional cascade multilevel topologies that can produce a high number of output voltage levels with the least components. The non-conventional cascade (MLI) in this paper was built to reduce the number of switches, simplify the circuit configuration, uncomplicated control, and minimize the system cost. Besides, it reduces THD and increases efficiency. Two topologies of non-conventional cascade MLI three phase, the Nine level and Seventeen level are presented. The PWM technique is used to control the switches. The simulation results show a better performance for both topologies. THD, the power loss and the efficiency of the two topologies are calculated and drawn to the different values of the Modulation index (ma).
The paper dells with a modified experimental prototype for pulse-width modulation (PWM) sliding mode control (SMC) applied to a DC-to-DC-boost converter operated in continuous conduction mode (CCM). Experimental results show that the proposed control schme provides good voltage regulation and is suitable for common DC-to-DC conversion purposes. The prototype and its implementation are given in detail. The static and dynamic performances of the The static and dynamic performances of the experimental system are recorded. Experimental results show that the proposed control scheme provides good voltage regulation and is suitable for common DC-to-DC conversion purposes.
In this paper, three phase induction motor (IM) has been modelled in stationary reference frame and controlled by using direct torque control (DTC) method with constant V/F ratio. The obtained drive system consists of nine nonlinear first order differential equations. The numerical analysis is used to investigate the system behavior due to control parameter change. The integral gain of speed loop is used as bifurcation parameter to test the system dynamics. The simulation results show that the system has period-doubling route to chaos, period-1, period-2, period-4, and then the system gets chaotic oscillation. A specific value of the parameter range shows that the system has very strong randomness and a high degree of disturbance
voltage sags represent the greatest threat to the sensitive loads of industrial consumers, the microprocessor based-loads, and any electrical sensitive components. In this paper, a special topology is proposed to mitigate deep and long duration sags by using a modified AC to AC boost converter with a new control method. A boost converter is redesigned with a single switch to produces an output voltage that is linearly proportional to the duty cycle of the switch. On the other hand, the proposed control system is based on introducing a mathematical model that relates the missing voltage to the duty cycle of the boost converter switch. The simulation results along with the system analysis are presented to confirm the effectiveness and feasibility of the proposed circuit.
This paper presents a new strategy for controlling induction motors with unknown parameters. Using a simple linearized model of induction motors, we design robust adaptive controllers and unknown parameters update laws. The control design and parameters estimators are proved to have global stable performance against sudden load variations. All closed loop signals are guaranteed to be bounded. Simulations are performed to show the efficacy of the suggested scheme.
A self learning fuzzy logic controller for ship steering systems is proposed in this paper. Due to the high nonlinearity of ship steering system, the performances of traditional control algorithms are not satisfactory in fact. An intelligent control system is designed for controlling the direction heading of ships to improve the high e ffi ciency of transportation, the convenience of manoeuvring ships, and the safety of navigation. The design of fuzzy controllers is usually performed in an ad hoc manner where it is hard to justify the choice of some fuzzy control parameters such as the parameters of membership function. In this paper, self tuning algorithm is used to adjust the parameters of fuzzy controller. Simulation results show that the efficiency of proposed algorithm to design a fuzzy controller for ship steering system.
In this paper, a model of PM DC Motor Drive is presented. The nonlinear dynamics of PM DC Motor Drive is discussed. The drive system shows different dynamical behaviors; periodic, quasi-period, and chaotic and are characterized by bifurcation diagrams, time series evolution, and phase portrait. The stabilization of chaos to a fixed point is adopted using slide mode controller (SMC). The chaotic dynamics are suppressed and the fixed point dynamics are observed after the activation of proposed controller. Numerical simulation results show the effectiveness of the proposed method of control for stabilization the chaos and different disturbances in the system.
In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.
Non-ideal channel conditions degrade the performance of wireless networks due to the occurrence of frame errors. Most of the well-known works compute the saturation throughput and packet delay for the IEEE 802.11 Distributed Coordination Function (DCF) protocol with the assumption that transmission is carried out via an ideal channel (i.e., no channel bit errors or hidden stations), and/or the errors exist only in data packets. Besides, there are no considerations for transmission errors in the control frames (i.e., Request to Send (RTS), Clear to Send (CTS), and Acknowledgement (ACK)). Considering the transmission errors in the control frames adds complexity to the existing analysis for the wireless networks. In this paper, an analytical model to evaluate the Medium Access Control (MAC) layer saturation throughput and packet delay of the IEEE 802.11g and IEEE 802.11n protocols in the presence of both collisions and transmission errors in a non-ideal wireless channel is provided. The derived analytical expressions reveal that the saturation throughput and packet delay are affected by the network size (n), packet size, minimum backoff window size (W min ), maximum backoff stage (m), and bit error rate (BER). These results are important for protocol optimization and network planning in wireless networks .
This article emphasizes on a strategy to design a Super Twisting Sliding Mode Control (STSMC) method. The proposed controller depends on the device of Field Programmable Gate Array (FPGA) for controlling the trajectory of robot manipulator. The gains of the suggested controller are optimized using Chaotic Particle Swarm Optimization (PSO) in MATLAB toolbox software and Simulink environment. Since the control systems speed has an influence on their stability requirements and performance, (FPGA) device is taken in consideration. The proposed control method based on FPGA is implemented using Xilinx block sets in the Simulink. Integrated Software Environment (ISE 14.7) and System Generator are employed to create the file of Bitstream which can be downloaded in the device of FPGA. The results show that the designed controller based of on the FPGA by using System Generator is completely verified the effectiveness of controlling the path tracking of the manipulator and high speed. Simulation results explain that the percentage improvement in the Means Square Error (MSEs) of using the STSMC based FPGA and tuned via Chaotic PSO when compared with the same proposed controller tuned with classical PSO are 17.32 % and 13.98 % for two different cases of trajectories respectively.
In this paper a neurofuzzy control structure is presented and used for controlling the two-link robot manipulator. A neurofuzzy networks are constructed for both the controller and for identification model of robot manipulator. The performance of the proposed structure is studied by simulation. Different operating conditions are considered. Results of simulation show good performance for the proposed control structure.
In this paper, a model of PI-speed control current-driven induction motor based on indirect field oriented control (IFOC) is addressed. To assess the complex dynamics of a system, different dynamical properties, such as stability of equilibrium points, bifurcation diagrams, Lyapunov exponents spectrum, and phase portraits are characterized. It is found that the induction motor model exhibits chaotic behaviors when its parameters fall into a certain region. Small variations of PI parameters and load torque affect the dynamics and stability of this electric machine. A chaotic attractor has been observed and the speed of the motor oscillates chaotically. Numerical simulation results are validating the theoretical analysis.
This work deals with the simulation model of multi-machines system as cold rolling mill is considered as application. Drivers of rolling system are a set of DC motors, which have extend applications in factories as aluminum rolling. Interconnection of multi DC motors in such a way that they are synchronized in their rotational speed. In cold rolling, the accuracy of the strip exit thickness is a very important factors. To realize accuracy in the strip exit thickness, Automatic Gauge Control system is used. In this paper MATLAB/SIMULINK models are proposed and implemented for the entire structures. Simulation results were presented to verify proposed model of cold rolling mill.
The conventional multilevel inverter (MLI) is divided into three types: diode clamped MLI, cascade H Bridge MLI and flying capacitor MLI. The main disadvantage of these types is the higher required number of components when the number of the levels increases and this results in more switching losses, system higher cost, more complex of control circuit as well as less accuracy. The work in this paper proposes two topologies of nonconventional diode clamping MLI three phase nine levels and eleven levels. The first proposed topology has ten switches and six diodes per phase while the second topology has nine switches and four diodes per phase. The pulse width modulation (PWM) control method is used as a control to gate switches. THD of the two proposed topologies are analyzed and calculated according different values of Modulation index (where the power loss and efficiency are obtained and plotted.
the proposed design offers a complete solution to support and surveillance vehicles remotely. The offered algorithm allows a monitoring center to track vehicles; diagnoses fault remotely, control the traffic and control CO emission. The system is programmed to scan the on-board diagnostic OBD periodically or based on request to check if there are any faults and read all the available sensors, then make an early fault prediction based on the sensor readings, an experience with the vehicle type and fault history. It is so useful for people who are not familiar with fault diagnosis as well as the maintenance center. The system offers tracking the vehicle remotely, which protects it against theft and warn the driver if it exceeds the speed limit according to its location. Finally, it allows the user to report any traffic congestion and allow s a vehicle navigator to be up to date with the traffic condition based on the other system’s user feedback.
This work presents a wireless communication network (WCN) infrastructure for the smart grid based on the technology of Worldwide Interoperability for Microwave Access (WiMAX) to address the main real-time applications of the smart grid such as Wide Area Monitoring and Control (WAMC), video surveillance, and distributed energy resources (DER) to provide low cost, flexibility, and expansion. Such wireless networks suffer from two significant impairments. On one hand, the data of real- time applications should deliver to the control center under robust conditions in terms of reliability and latency where the packet loss is increased with the increment of the number of industrial clients and transmission frequency rate under the limited capacity of WiMAX base station (BS). This research suggests wireless edge computing using WiMAX servers to address reliability and availability. On the other hand, BSs and servers consume affected energy from the power grid. Therefore, the suggested WCN is enhanced by green self-powered based on solar energy to compensate for the expected consumption of energy. The model of the system is built using an analytical approach and OPNET modeler. The results indicated that the suggested WCN based on green WiMAX BS and green edge computing can handle the latency and data reliability of the smart grid applications successfully and with a self-powered supply. For instance, WCN offered latency below 20 msec and received data reliability up to 99.99% in the case of the heaviest application in terms of data.
In this paper, a modified wavelet neural network (WNN) (or wavenet)-based predictor is introduced to predict link status (congestion with load indication) of each link in the computer network. On the contrary of previous wavenet-based predictors, the proposed modified wavenet-based link state predictor (MWBLSP) generates two indicating outputs for congestion and load status of each link based on th e premeasured power burden (square values) of utilization on each link in the previous time intervals. Fortunately, WNNs possess all learning and generalization capabilities of traditional neural networks. In addition, the ability of such WNNs are efficiently enhanced by the local characteristics of wavelet functions to deal with sudden changes and burst network load. The use of power burden utilization at the predictor input supports some non-linear distri butions of the predicted values in a more efficient manner. The proposed MWBLSP pre dictor can be used in the context of active congestion control and link load balancing techniques to improve the performance of all links in the network with best utilization of network resources.
A single phase boost rectifier circuit is studied with and without feedforward techniques. The circuit is implemented and tested experimentally. It can be operated at high power factor (greater than 0.99), and at line current total harmonic distortion (THD) (less than 0.06), by selecting a suitable control parameters at the desired output power.
As a key type of mobile robot, the two-wheel mobile robot has been developed rapidly for varied domestic, health, and industrial applications due to human-like movement and balancing characteristics based on the inverted pendulum theory. This paper presents a developed Two-Wheel Self-Balanced Robot (TWSBR) model under road disturbance effects and simulated using MATLAB Simscape Multibody. The considered physical-mechanical structure of the proposed TWSBS is connected with a Simulink controller scheme by employing physical signal converters to describe the system dynamics efficiently. Through the Simscape environment, the TWSBR motion is visualized and effectively analyzed without the need for complicated analysis of the associated mathematical model. Besides, 3D visualization of real-time behavior for the implemented TWSBR plant model is displayed by Simulink Mechanics Explorer. Robot balancing and stability are achieved by utilizing Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controllers' approaches considering specific control targets. A comparative study and evaluation of both controllers are conducted to verify the robustness and road disturbance rejection. The realized performance and robustness of developed controllers are observed by varying object-carrying loaded up on mechanical structure layers during robot motion. In particular, the objective weight is loaded on the robot layers (top, middle, and bottom) during disturbance situations. The achieved findings may have the potential to extend the deployment of using TWSBRs in the varied important application.
The aim of this paper is to suggest a methodical smooth control method for improving the stability of two wheeled self-balancing robot under effect disturbance. To promote the stability of the robot, the design of linear quadratic regulator using particle swarm optimization (PSO) method and adaptive particle swarm optimization (APSO). The computation of optimal multivariable feedback control is traditionally by LQR approach by Riccati equation. Regrettably, the method as yet has a trial and error approach when selecting parameters, particularly tuning the Q and R elements of the weight matrices. Therefore, an intelligent numerical method to solve this problem is suggested by depending PSO and APSO algorithm. To appraise the effectiveness of the suggested method, The Simulation result displays that the numerical method makes the system stable and minimizes processing time.
This paper investigates Load Frequency Control of multi area inter connected power system having different turbines with PID controller. The gain values of controller are optimized using different Metaheuristic Algorithms. The performance and validity of designed controllers were checked on multi area interconnected power system with various Step Load Perturbations. Finally, the performance of proposed controllers was compared with conventional controller and from the result it was proved that the proposed controller exhibits superior performance than conventional controller for various Step Load Perturbations.
Voltage instability problems in power system is an important issue that should be taken into consideration during the planning and operation stages of modern power system networks. The system operators always need to know when and where the voltage stability problem can occur in order to apply suitable action to avoid unexpected results. In this paper, a study has been conducted to identify the weakest bus in the power system based on multi-variable control, modal analysis, and Singular Value Decomposition (SVD) techniques for both static and dynamic voltage stability analysis. A typical IEEE 3- machine, 9-bus test power system is used to validate these techniques, for which the test results are presented and discussed.
A Programmable logic controller (PLC) uses the digital logic circuits and their operating concepts in its hardware structure and its programming instructions and algorithms. Therefore, the deep understanding of these two items is staple for the development of control applications using the PLC. This target is only possible through the practical sensing of the various components or instructions of these two items and their applications. In this work, a user-friendly and re-configurable ladder, digital logic learning and application development design and testing platform has been designed and implemented using a Programmable Logic Controller (PLC), Human Machine Interface panel (HMI), four magnetic contactors, one Single-phase power line controller and one Variable Frequency Drive (VFD) unit. The PLC role is to implement the ladder and digital logic functions. The HMI role is to establish the virtual circuit wiring and also to drive and monitor the developed application in real time mode of application. The magnetic contactors are to play the role of industrial field actuators or to link the developed application control circuit to another field actuator like three phase induction motor. The Single -phase power line controller is to support an application like that of the soft starter. The VFD is to support induction motor driven applications like that of cut-to-length process in which steel coils are uncoiled and passed through cutting blade to be cut into required lengths. The proposed platform has been tested through the development of 14 application examples. The test results proved the validity of the proposed platform.
This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed, and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller's design. The effects show a neural local (NARMA-L2) can attribute even higher than PID. The settling time, upward jab time, and most overshoot of the response of NARMA-L2 is a notable deal an awful lot less than the corresponding factors for the accepted PID controller. The conclusion from this paper can be to utilize synthetic neural networks of industrial elements and sturdy manageable to be viewed as a dependable desire to normal modeling, simulation, and manipulation methodologies. The model developed in this paper can be used offline to structure and manufacturing points of conditions monitoring, faults detection, and troubles shooting for wind generation systems.
The continuously ever-growing demand for the electrical power causing the continuous expansion and complexity of power systems, environmental and economic factors forcing the system to work near the critical limits of stability, so research's stability have become research areas worthy of attention in the resent day. The present work includes two phases: The first one is to determine the Voltage Stability Index for the more insensitive load bus to the voltage collapse in an interconnected power system using fast analyzed method based on separate voltage and current for PQ buses from these of PV buses, while the second phase is to suggested a simulated optimization technique for optimal voltage stability profile all around the power system. The optimization technique is used to adjust the control variables elements: Generator voltage magnitude, active power of PV buses, VAR of shunt capacitor banks and the position of transformers tap with satisfied the limit of the state variables (load voltages, generator reactive power and the active power of the slack bus). These control variables are main effect on the voltage stability profile to reach the peak prospect voltage stable loading with acceptable voltage profile. An optimized voltage collapse based on Particle Swarm Optimization has been tested on both of the IEEE 6 bus system and the Iraqi Extra High Voltage 400 kV Grid 28 bus . To ensure the effectiveness of the optimization technique a comparison between the stability indexes for load buses before and after technical application are presented. Simulation results have been executed using Matlab software). Keyword: Voltage Stability Indicator; voltage collapse; Stability of Extra High Voltage Grid; PSO optimization technique.
This work addresses the critical need for secure and patient-controlled Electronic Health Records (EHR) migration among healthcare hospitals’ cloud servers (HHS). The relevant approaches often lack robust access control and leave data vulnerable during transfer. Our proposed scheme empowers patients to delegate EHR migration to a trusted Third-Party Hospital (TTPH); which is the Certification Authority (CA) while enforcing access control. The system leverages asymmetric encryption utilizing the Elliptic Curve Digital Signature Algorithm (ECDSA), EEC and ECDSA added robust security and lightness EHR sharing. Patient and user privacy is managed due to anonymity through cryptographic hashing for data protection and utilizes mutual authentication for secure communication. Formal security analysis using the Scyther tool and informal analysis was conducted to validate the system’s robustness. The proposed scheme achieved EHR integrity due to the verification of the communicated HHS and ensuring the integrity of the HHS digital certificate during EHR migration. Ultimately, the result achieved in the proposed work demonstrated the scheme’s high balance between data security and accuracy of communication, where the best result obtained represented 7.7/ ms as computational cost and 1248 /bits as communication cost compared with the relevant approaches.
In this work, the phase lock loop PLL-based controller has been adopted for tracking the resonant frequency to achieve maximum power transfer between the power source and the resonant load. The soft switching approach has been obtained to reduce switching losses and improve the overall efficiency of the induction heating system. The jury’s stability test has been used to evaluate the system’s stability. In this article, a multilevel inverter has been used with a series resonant load for an induction heating system to clarify the effectiveness of using it over the conventional full-bridge inverter used for induction heating purposes. Reduced switches five-level inverter has been implemented to minimize switching losses, the number of drive circuits, and the control circuit’s complexity. A comparison has been made between the conventional induction heating system with full bridge inverter and the induction heating system with five level inverter in terms of overall efficiency and total harmonic distortion THD. MATLAB/ SIMULINK has been used for modeling and analysis. The mathematical analysis associated with simulation results shows that the proposed topology and control system performs well.
This paper provides a two algorithms for designing robust formation control of multiple robots called Leader- Neighbor algorithm and Neighbor-Leader algorithm in unknown environment. The main function of the robot group is to use the RP lidar sensor attached to each robot to form a static geometric polygon. The algorithms consist of two phases implemented to investigate the formation of polygon shape. In the leading- neighbor algorithm, the first stage is the leader alignment and the adjacent alignment is the second stage. The first step uses the information gathered by the main RP Lidar sensor to determine and compute the direction of each adjacent robot. The adjacent RP Lidar sensors are used to align the adjacent robots of the leader by transferring these adjacent robots to the leader. By performing this stage, the neighboring robots will be far from the leader. The second stage uses the information gathered by adjacent RP sensors to reposition the robots so that the distance between them is equal. On the other hand, in the neighbor-leader algorithm, the adjacent robots are rearranged in a regular distribution by moving in a circular path around the leader, with equal angles between each of the two neighbor robots. A new distribution will be generated in this paper by using one leader and four adjacent robots to approve the suggested leader neighbor algorithm and neighbor-leader algorithm .
Some engineering applications requires constant engine speed such as power generators, production lines ..etc. The current paper focuses on adding a new closed loop based on engine torque. Load cells can be used to measure the torque of load applied , the electrical signal is properly handled to manipulate a special fuel actuator to compensate for the reduction in engine speed. The speed loop still acts as the most outer closed loop. This method leads to rapid speed compensation and lead control action.
The Intelligent Control of Vibration Energy Harvesting system is presented in this paper. The harvesting systems use a me- chanical vibration to generate electrical energy in a suitable form for use. Proportional-Integrated-derivative controller and Fuzzy Logic controller have been suggested; their parameters are optimized using a new heuristic algorithm, the Camel Trav- eling Algorithm(CTA). The proposed circuit Simulink model was constructed in Matlab facilities, and the model was tested under various operating conditions. The results of the simulation using the CTA was compared with two other methods.
The Permanent Magnet Synchronous Motor (PMSM) is commonly used as traction motors in the electric traction applications such as in subway train. The subway train is better transport vehicle due to its advantages of security, economic, health and friendly with nature. Braking is defined as removal of the kinetic energy stored in moving parts of machine. The plugging braking is the best braking offered and has the shortest time to stop. The subway train is a heavy machine and has a very high moment of inertia requiring a high braking torque to stop. The plugging braking is an effective method to provide a fast stop to the train. In this paper plugging braking system of the PMSM used in the subway train in normal and fault-tolerant operation is made. The model of the PMSM, three-phase Voltage Source Inverter (VSI) controlled using Space Vector Pulse Width Modulation technique (SVPWM), Field Oriented Control method (FOC) for independent control of two identical PMSMs and fault-tolerant operation is presented. Simulink model of the plugging braking system of PMSM in normal and fault tolerant operation is proposed using Matlab/Simulink software. Simulation results for different cases are given.
Fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. To reduce the huge number of fuzzy rules required in the normal design for fuzzy PID controller, the fuzzy PID controller is represented as Proportional-Derivative Fuzzy (PDF) controller and Proportional-Integral Fuzzy (PIF) controller connected in parallel through a summer. The PIF controller design has been simplified by replacing the PIF controller by PDF controller with accumulating output. In this paper, the modified Fuzzy PID controller design for bench-top helicopter has been presented. The proposed Fuzzy PID controller has been described using Very High Speed Integrated Circuit Hardware Description Language (VHDL) and implemented using the Field Programmable Gate Array (FPGA) board. The bench-top helicopter has been used to test the proposed controller. The results have been compared with the conventional PID controller and Internal Model Control Tuned PID (IMC-PID) Controller. Simulation results show that the modified Fuzzy PID controller produces superior control performance than the other two controllers in handling the nonlinearity of the helicopter system. The output signal from the FPGA board is compared with the output of the modified Fuzzy PID controller to show that the FPGA board works like the Fuzzy PID controller. The result shows that the plant responses with the FPGA board are much similar to the plant responses when using simulation software based controller.
In this work, the collective behavior of Artemia Salina is studied both experimentally and theoretically. Several experiments have been designed to investigate the Artemia motion under different environment conditions. From the results of such experiments, a strategy to control the direction of motion of an Artemia population, by exploiting their sensitivity to light, has been derived and then implemented.
A composite PD and sliding mode neural network (NN)-based adaptive controller, for robotic manipulator trajectory tracking, is presented in this paper. The designed neural networks are exploited to approximate the robotics dynamics nonlinearities, and compensate its effect and this will enhance the performance of the filtered error based PD and sliding mode controller. Lyapunov theorem has been used to prove the stability of the system and the tracking error boundedness. The augmented Lyapunov function is used to derive the NN weights learning law. To reduce the effect of breaching the NN learning law excitation condition due to external disturbances and measurement noise; a modified learning law is suggested based on e-modification algorithm. The controller effectiveness is demonstrated through computer simulation of cylindrical robot manipulator.
In this paper, the power system stabilizer (PSS) and Thyristor controlled phase shifter(TCPS) interaction is investigated . The objective of this work is to study and design a controller capable of doing the task of damping in less economical control effort, and to globally link all controllers of national network in an optimal manner , toward smarter grids . This can be well done if a specific coordination between PSS and FACTS devices , is accomplished . Firstly, A genetic algorithm-based controller is used. Genetic Algorithm (GA) is utilized to search for optimum controller parameter settings that optimize a given eigenvalue based objective function. Secondly, an optimal pole shifting, based on modern control theory for multi-input multi-output systems, is used. It requires solving first order or second order linear matrix Lyapunov equation for shifting dominant poles to much better location that guaranteed less overshoot and less settling time of system transient response following a disturbance.
This paper presents and discusses a buck DC/DC converter control based on fuzzy logic approach, in which the fuzzy controller has been driven by voltage error signal and a current error signal for which the load current has been taken as a reference one. The validity of the proposed approach has been examined through starting the buck DC/DC converter at different loading and input voltages (to monitor the starting performances), exposing the converter into large load resistance and input voltage step variations (to explore its dynamic performance), in addition to step and smooth variation in the reference voltage (to see its ability in readjusting its operating point to comply with the new setting). The simulation results presented an excellent load & line regulations abilities in addition to a good reference tracking ability. It also showed the possibility of using the buck converter as smooth variable voltage source (under smooth reference voltage variations).
The corrosion of metallic structures buried in soil or submerged in water which became a problem of worldwide significance and causes most of the deterioration in petroleum industry can be controlled by cathodic protection (CP).CP is a popular technique used to minimize the corrosion of metals in a variety of large structures. To prevent corrosion, voltage between the protection metal and the auxiliary anode has to be controlled on a desired level. In this study two types of controllers will be used to set a pipeline potential at required protection level. The first one is a conventional Proportional-Integral-Derivative (PID) controller and the second are intelligent controllers (fuzzy and neural controllers).The results were simulated and implemented using MATLAB R 2010a program which offers predefined functions to develop PID, fuzzy and neural control systems.
The control problem for a class of a nonlinear systems that contain the coupling of unmeasured states and unknown parameters is addressed. The system actuation is assumed to suffer from unknown dead zone nonlinearity. The parameters bounds of the unknown dead zone to be considered are unknown. Adaptive sliding mode controller, unmeasured states observer, and unknown parameters estimators are suggested such that global stability is achieved. Simulation for a single link mechanical system with unknown dead zone and friction torque is implemented for proving the efficacy of the suggested scheme.
The evolution of wireless communication technology increases human machine interaction capabilities especially in controlling robotic systems. This paper introduces an effective wireless system in controlling the directions of a wheeled robot based on online hand gestures. The hand gesture images are captured and processed to be recognized and classified using neural network (NN). The NN is trained using extracted features to distinguish five different gestures; accordingly it produces five different signals. These signals are transmitted to control the directions of the cited robot. The main contribution of this paper is, the technique used to recognize hand gestures is required only two features, these features can be extracted in very short time using quite easy methodology, and this makes the proposed technique so suitable for online interaction. In this methodology, the preprocessed image is partitioned column-wise into two half segments; from each half one feature is extracted. This feature represents the ratio of white to black pixels of the segment histogram. The NN showed very high accuracy in recognizing all of the proposed gesture classes. The NN output signals are transmitted to the robot microcontroller wirelessly using Bluetooth. Accordingly the microcontroller guides the robot to the desired direction. The overall system showed high performance in controlling the robot movement directions.
The unprogrammed penetration for the loads in the distribution networks make it work in an unbalancing situation that leads to unstable operation for those networks. the instability coming from the imbalance can cause many serious problems like the inefficient use of the feeders and the heat increased in the distribution transformers. The demands response can be regarded as a modern solution for the problem by offering a program to decreasing the consumption behavior for the program's participators in exchange for financial incentives in specific studied duration according to a direct order from the utility. The paper uses a new suggested algorithm to satisfy the direct load control demand response strategy that can be used in solving the unbalancing problem in distribution networks. The algorithm procedure has been simulated in MATLAB 2018 to real data collected from the smart meters that have been installed recently in Baghdad. The simulation results of applying the proposed algorithm on different cases of unbalancing showed that it is efficient in curing the unbalancing issue based on using the demand response strategy.
An accurate model for a permanent magnet syn- chronous generator (PMSG) is important for the design of a high-performance PMSG control system. The performance of such control systems is influenced by PMSG parameter variations under real operation conditions. In this paper, the electrical parameters of a PMSG (the phase resistance, the phase inductance and the rotor permanent magnet (PM) flux linkage) are identified by a particle swarm optimisation (PSO) algorithm based on experimental tests. The advantages of adopting the PSO algorithm in this research include easy implementation, a high computational efficiency and stable convergence characteristics. For PMSG parameter identification, the normalised root mean square error (NRMSE) between the measured and simulated data is calculated and minimised using PSO.
Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN’s prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.
In this paper the dynamic behavior of linear induction motor is described by a mathematical model taking into account the end effects and the core losses. The need for such a model rises due to the complexity of linear induction motors electromagnetic field theory. The end affects are modeled by introducing a speed dependent scale factor to the magnetizing inductance and series resistance in the d-axis equivalent circuit. Simulation results are presented to show the validity of the model during both no-load and sudden load change intervals. This model can also be used directly in simulation researches for linear induction motor vector control drive systems.