The identification of system parameters plays an essential role in system modeling and control. This paper presents a parameter estimation for a permanent magnetic DC motor using the simulink design optimization method. The parameter estimation may be represented as an optimization problem. Firstly, the initial values of the DC motor parameters are extracted using the dynamic model through measuring the values of voltage, current, and speed of the motor. Then, these values are used as an initial value for simulink design optimization. The experimentally input- output data can be collected using a suggested microcontroller based circuit that will be used later for estimating the DC motor parameters by building a simulink model. Two optimization algorithms are used, the pattern search and the nonlinear least square. The results show that the nonlinear least square algorithm gives a more accurate result that almost approaches to the actual measured speed response of the motor. )
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.
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.
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.
Five-phase machine employment in electric drive system is expanding rapidly in many applications due to several advantages that they present when compared with their three-phase complements. Synchronous reluctance machines(SynRM) are considered as a proposed alternative to permanent magnet machine in the automotive industry because the volatilities in the permanent magnet price, and a proposed alternative for induction motor because they have no field excitation windings in the rotor, SyRM rely on high reluctance torque thus no needing for magnetic material in the structure of rotor. This paper presents dynamic simulation of five phase synchronous reluctance motor fed by five phase voltage source inverter based on mathematical modeling. Sinusoidal pulse width modulation (SPWM) technique is used to generate the pulses for inverter. The theory of reference frame has been used to transform five-phase SynRM voltage equations for simplicity and in order to eliminate the angular dependency of the inductances. The torque in terms of phase currents is then attained using the known magnetic co-energy method, then the results obtained are typical.
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.
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.
The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.
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.
Due to their vital applications in many real-world situations, researchers are still presenting bunches of methods for better analysis of motor imagery (MI) electroencephalograph (EEG) signals. However, in general, EEG signals are complex because of their nonstationary and high-dimensionality properties. Therefore, high consideration needs to be taken in both feature extraction and classification. In this paper, several hybrid classification models are built and their performance is compared. Three famous wavelet mother functions are used for generating scalograms from the raw signals. The scalograms are used for transfer learning of the well-known VGG-16 deep network. Then, one of six classifiers is used to determine the class of the input signal. The performance of different combinations of mother functions and classifiers are compared on two MI EEG datasets. Several evaluation metrics show that a model of VGG-16 feature extractor with a neural network classifier using the Amor mother wavelet function has outperformed the results of state-of-the-art studies.
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.
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
In this paper, the vector-controlled Permanent Magnet Synchronous Motor (PMSM) fed by Indirect Matrix Converter (IMC) is analyzed, designed, and simulated by using the IMC with Carrier Based Pulse Width Modulation (CBPWM). The CBPWM strategy is based on Space Vector Pulse Width Modulation (SVPWM) analysis, it is used to enhance the input current waveform, reduce the complexity of switching signals generation, and to solve the commutation problem. The traditional PMSM drive system is simulated for comparison with proposed drive system. The proposed drive system is compared to the traditional drive system using the Total Harmonic Distortion (THD). The comparison results show that the proposed drive system outperform the traditional drive system by THD different of 1/30 of input current and 1/1.5 of stator current, with high input power factor.
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.
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.
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.
This paper present an adaptation mechanism for fuzzy logic controller FLC in order to perfect the response performance against small rotation angles of real D.C. motor with unknown parameters. A supervisor fuzzy controller SFC is designed to continuously adjust, on-line, the universe of discourse UOD of the basic fuzzy controller BFC input variables based on position error and change of position error. Performance of the proposed adaptive fuzzy controller is compared with corresponding conventional FLC in terms of several performance measures such rise time, settling time, peak overshoot, and steady state error. The system design and implementation are carried out using LabVIEW 2009 with NI PCI-6251 data acquisition DAQ card. The practical results demonstrate using self tuning FLC scheme grant a better performance as compared with conventional FLC which is incapable of rotating a motor if the rotation angle is being small.
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.
The rapid growth in microelectronics and crunching RISC in the field of bio-medical sciences incorporated of soft tools to diagnose various parameters of human fluids. Conventional method of blood sample analysis makes use of laboratory technique of titration, which is operator-dependent and results in lot of errors depending on the skill of the technician. In order to eliminate the human errors involved in the conventional method, in this paper an attempt has been made to present a capillary centrifuge technique driven by high speed DC motor fed by Morgan chopper and controlled by powerful ARM processor. It results in accurate analysis of the blood samples. The various techniques involved in accurate sensing of speed using timer and generation of firing pulses to thyristor in the Morgan chopper is judiciously achieved. This paper clearly brings out the advantages of the proposed blood measurement technique which effectively gives blood analysis faster and at a low cost.
Training the user in Brain-Computer Interface (BCI) systems based on brain signals that recorded using Electroencephalography Motor Imagery (EEG-MI) signal is a time-consuming process and causes tiredness to the trained subject, so transfer learning (subject to subject or session to session) is very useful methods of training that will decrease the number of recorded training trials for the target subject. To record the brain signals, channels or electrodes are used. Increasing channels could increase the classification accuracy but this solution costs a lot of money and there are no guarantees of high classification accuracy. This paper introduces a transfer learning method using only two channels and a few training trials for both feature extraction and classifier training. Our results show that the proposed method Independent Component Analysis with Regularized Common Spatial Pattern (ICA-RCSP) will produce about 70% accuracy for the session to session transfer learning using few training trails. When the proposed method used for transfer subject to subject the accuracy was lower than that for session to session but it still better than other methods.
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.
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.
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.
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.
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.
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.
Swimming performance underlies the biomechanical properties and functional morphology of fish fins. In this article, a pair of concave fin has been suggested, which is inspired from Labriform-mode Swimming fish. First, three concave fins with different sizes are proposed in order to choose the optimum size. All three fins have the same length but with different surface areas, such that each fin has an aspect ratio different from the others. Next, the complete design of the robot is suggested, the complete design of the body and pectoral fins were subjected to computational fluid dynamics (CFD) analysis to show the validity of the proposed model. Finally, the physical model is suggested and provided with 3D printer of Polylactic Acid (PLA) with a density of 1240 kg/ m3. The swimming robot fins have been examined by CFD analysis provided by Solidworks® to evaluate the highest thrust and lowest drag forces. The result showed that the optimum fin is the one with the lowest aspect ratio fin produces the highest drag, whereas the highest aspect ratio fin gives the lowest drag and thrust, therefore; a value of aspect ratio in between these two cases is chosen. While other types of examinations are based on motion analysis of the 3D design, the required motor torque is calculated in order to select a suitable servomotor for this purpose, which a HS-5086WP waterproof servomotor can achieve the calculated torque.
Due to the last increase in data and information technology, the need to use robots in many life areas is increased. There is a great diversity in this field, depending on the type of task required, as the robot enters the parcels of air, land, and water. In this paper, a robot's mission designed to move things is concentrated, relying on line-tracing technology that makes it easy to track its path safely, the RFID is distributed in its approach. When the robot reads the RFID tag, it stops until it raises the load from above, the robot continues its path toward the target. When an obstacle obstructs the robot path, the robot deviates and returns after a while to its previous approach. All this technology is implemented using a new algorithm which is programmed using the visual basic program. The robot designed to transfer the stored material is used according to a site known as an identifier that is identified by the RFID value, where the robot is programmed through a microcontroller and a unique store program that determines the current location and the desired location, then is given the task for the robot to do it as required. The robot is controlled using an ATmega controller to control other parts connected to the electronic circuit, the particular infrared sensor, and ultrasound to avoid potential obstacles within the robot's path to reach the target safely. In addition to this, the robot is made up of an RFID sensor to give unique to each desired target site. Through the console, it is possible to know the link indicated by the target. The H-bridge is also used to obtain a particular command and guide the robot as needed to move freely in all directions and a DC motor which is unique for moving wheels at the desired speed, and Bluetooth for programmable and secure wireless transmission and reception with all these parts through a unique program that also uses application inventory. The robot has proven to be a great success in performing the required task through several tests that have been practically performed.
In this paper, we present a new programmable chaotic circuit based on the dynamical chaotic system introduced by E. Lorenz. The design and realization of the model are accomplished by using a programmable logic controller (PLC). The system can be modeled and realized with a structured texted. The nonlinear differential equations of Lorenz model are solved numerically. The generated chaotic signal by using PLC is applied to a single- phase induction motor via a variable frequency drive to create a chaotic perturbation in the experiments of liquid mixing. Colorization liquid experiments shows that the generated chaotic motion effectively makes an enhancement of the mixing process in the stirred-tank mixer model in our laboratory.