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.
The PH regulation of cooling tower plant in southern fertilizers company (SCF) in Iraq is important for industry pipes protection and process continuity. According to the Mitsubishi standard, the PH of cooling water must be around (7.1 to 7.8). The deviation in PH parameter affects the pipes, such as corrosion and scale. Acidic water causes pipes to corrode, and alkaline water causes pipes to scale. The sulfuric acid solution is used for PH neutralization. The problem is that the sulfuric acid is pumped manually in the cooling tower plant every two or three hours for PH regulation. The manual operation of the sulfuric acid pump makes deviations in the PH parameter. It is very difficult to control the PH manually. To solve this problem, a PID controller for PH regulation was used. The reason for using the PID controller is that the PH response is irregular through the neutralization process. The methodology is to calculate the transfer function of the PH loop using the system identification toolbox of MATLAB, to design and implement a PID controller, to optimize the PID controller response using particle swarm optimization PSO algorithm, and to make a comparison among several tuning methods such as Ziegler Nichols (ZN) tuning method, MATLAB tuner method, and PSO algorithm tuning method. The results showed that the PSO-based PID controller tuning gives a better overshoot, less rise time, and an endurable settling time than the other tuning methods. Hence, the PH response became according to the target range. The experimental results showed that the PH regulation improved using the PSO-based PID controller tuning.
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
This paper focuses on the vibration suppression of a half-car model by using a modified PID controller. Mostly, car vibrations could result from some road disturbances, such as bumps or potholes transmitted to a car body. The proposed controller consists of three main components as in the case of the conventional PID controller which are (Proportional, Integral, and Derivative) but the difference is in the positions of these components in the control loop system. Initially, a linear half-car suspension system is modeled in two forms passive and active, the activation process occurred using a controlled hydraulic actuator. Thereafter, the two systems have been simulated using MATLAB/Simulink software in order to demonstrate the dynamic response. A comparison between conventional and modified PID controllers has been carried out. The resulting dynamic response of the half-car model obtained from the simulation process was improved when using a modified PID controller compared with the conventional PID controller. Moreover, the efficiency and performance of the half-car model suspension have been significantly enhanced by using the proposed controller. Thus, achieving high vehicle stability and ride comfort.
The main problem of line follower robot is how to make the mobile robot follows a desired path (which is a line drawn on the floor) smoothly and accurately in shortest time. In this paper, the design and implementation of a complex line follower mission is presented by using Matlab Simulink toolbox. The motion of mobile robot on the complex path is simulated by using the Robot Simulator which is programed in Matlab to design and test the performance of the proposed line follower algorithm and the designed PID controller. Due to the complexity of selection the parameters of PID controller, the Particle Swarm Optimization (PSO) algorithm are used to select and tune the parameters of designed PID controller. Five Infrared Ray (IR) sensors are used to collect the information about the location of mobile robot with respect to the desired path (black line). Depending on the collected information, the steering angle of the mobile robot will be controlled to maintain the robot on the desired path by controlling the speed of actuators (two DC motors). The obtained simulation results show that, the motion of mobile robot is still stable even the complex maneuver is performed. The hardware design of the robot system is perform by using the Arduino Mobile Robot (AMR). The Simulink Support Package for Arduino and control system toolbox are used to program the AMR. The practical results show that the performances of real mobile robot are exactly the same of the performances of simulated mobile robot.
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.
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 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.
This paper presents a new optimization algorithm called corrosion diffusion optimization algorithm (CDOA). The proposed algorithm is based on the diffusion behavior of the pitting corrosion on the metal surface. CDOA utilizes the oxidation and reduction electrochemical reductions as well as the mathematical model of Gibbs free energy in its searching for the optimal solution of a certain problem. Unlike other algorithms, CDOA has the advantage of dispensing any parameter that need to be set for improving the convergence toward the optimal solution. The superiority of the proposed algorithm over the others is highlighted by applying them on some unimodal and multimodal benchmark functions. The results show that CDOA has better performance than the other algorithms in solving the unimodal equations regardless the dimension of the variable. On the other hand, CDOA provides the best multimodal optimization solution for dimensions less than or equal to (5, 10, 15, up to 20) but it fails in solving this type of equations for variable dimensions larger than 20. Moreover, the algorithm is also applied on two engineering application problems, namely the PID controller and the cantilever beam to accentuate its high performance in solving the engineering problems. The proposed algorithm results in minimized values for the settling time, rise time, and overshoot for the PID controller. Where the rise time, settling time, and maximum overshoot are reduced in the second order system to 0.0099, 0.0175 and 0.005 sec., in the fourth order system to 0.0129, 0.0129 and 0 sec, in the fifth order system to 0.2339, 0.7756 and 0, in the fourth system which contains time delays to 1.5683, 2.7102 and 1.80 E-4 sec., and in the simple mass-damper system to 0.403, 0.628 and 0 sec., respectively. In addition, it provides the best fitness function for the cantilever beam problem compared with some other well-known algorithms.
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.
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.
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.
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.
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 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.
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 today's chemical, refinery, and petrochemical sectors, separation tanks are one of the most significant separating processes. One or more separation tanks must operate consistently and reliably for multiple facilities' safe and efficient operation. Therefore, in this paper, a PI controller unit has been designed to improve the performance of the tank level controller of the industrial process in Basrah Refinery Station. The overall system mathematical model has been derived and simulated by MATLAB to evaluate the performance. Further, to improve the performance of the tank level controller, optimal PI parameters should be calculated, which Closed-Loop PID Autotuner has been used for this task. Several experiments have been conducted to evaluate the performance of the proposed system. The results indicated that the PI controller based on the Autotuner Method is superior to the conventional PI controller in terms of ease to implement and configuration also less time to get optimal PI gains.
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 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.