Performance Enhancement of PID Tuning of DC Servomotor using Metaheuristic Algorithm (original) (raw)
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Eastern-European Journal of Enterprise Technologies, 2021
A significant problem in the control field is the adjustment of PID controller parameters. Because of its high nonlinearity property, control of the DC motor system is difficult and mathematically repetitive. The particle swarm optimization PSO solution is a great optimization technique and a promising approach to address the problem of optimum PID controller results. In this paper, a modified particle swarm optimization PSO method with four inertia weight functions is suggested to find the global optimum parameters of the PID controller for speed and position control of the DC motor. Benchmark studies of inertia weight functions are described. Two scenarios have been suggested in order to modify PSO including the first scenario called M1-PSO and the second scenario called M2-PSO, as well as classical PSO algorithms. For the first scenario, the modification of the PSO was done based on changing the four inertia weight functions, social and personal acceleration coefficient, while in...
PROPORTIONAL–INTEGRAL–DERIVATIVE (PID) CONTROLLER TUNING USING PARTICLE SWARM OPTIMIZATION ALGORITHM
The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportional-integral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler-Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler-Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportional-integral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler-Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler-Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/position-control-of-servo-systems-using-pid-controller-tuning-with-soft-computing-optimization-techniques https://www.ijert.org/research/position-control-of-servo-systems-using-pid-controller-tuning-with-soft-computing-optimization-techniques-IJERTV3IS110727.pdf In this paper, position control of servo motor using PID controller with soft computing optimization techniques is discussed. PID controllers widely used in the industry. Different methods are available for tuning the PID controller. In this paper conventional tuning method Z-N method and soft computing methods like Genetic algorithm (GA) and Particle swarm optimization (PSO) are used for the position control of the DC servo motor. The results obtained from soft computing methods (GA, PSO) are compared with conventional tuning method (Z-N) found that the soft computing techniques gives better results compared to the conventional PID tuning method.
PID PARAMETERS OPTIMIZATION USING ADAPTIVE PSO ALGORITHM FOR A DCSM POSITION CONTROL
This paper demonstrates a novel algorithm in particle swarm optimization, which is called Adaptive PSO (APSO), to optimize the gains of a proportional-integral-derivative (PID) controller concurrently to control the position of a DC servomotor (DCSM). The parameters of the PID controller (K P , K i , and K D ) are obtained, firstly, by using Ziegler-Nichols tuning method, secondly by using standard PSO algorithm, thirdly by using MPSO algorithm, and finally by using APSO algorithm. The transient response analysis has been done by comparing the performance of the four tuning methods. The results showed that the proposed algorithm gives better performance than the other optimization algorithms, and the DCSM reached very fast to the final position. The analysis and simulations have been made in MATLAB R2010a software environment.
IAES International Journal of Robotics and Automation (IJRA), 2023
This study proposes a control method for servo motor position using a proportional-integral-derivative (PID) controller with particle swarm optimization (PSO). We use an AX-12 servo motor that is commonly used for robotic manipulator applications. The angular position of the servo motor will be controlled using the PID control method with PSO as a controller gain optimizer. Firstly, the transfer function model of the servo motor is generated using open-loop model identification. Then, the integral error of the closed-loop system is used as PSO input in producing PID controller gain. As an objective function of the PSO algorithm, the integral time absolute error (ITAE) index performance is used. The proposed controller was tested and compared with PID with the Ziegler-Nichols (ZN) method. We also conduct the hardware experiment using Arduino Uno as a microcontroller using one AX-12 servo motor on the base joint of the manipulator robot. Based on the simulation result, the PID-PSO controller can achieve the best control response performance if compared to PID-ZN with a rise time is less than 0.5 s, a settling time of fewer than 8 s, and an overshoot under 1.2%. The effectiveness of the proposed PID-PSO controller is also validated by hardware experimental results.
Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor
International Journal of Robotics and Control Systems
The use of DC motors is now common because of its advantages and has become an important necessity in helping human activities. Generally, motor control is designed with PID control. The main problem that is often discussed in PID is parameter tuning, namely determining the value of the Kp, Ki, and Kd parameters in order to obtain optimal system performance. In this study, one method for tuning PID parameters on a DC motor will be used, namely the Particle Swarm Optimization (PSO) method. Parameter optimization using the PSO method has stable results compared to other methods. The results of tuning the PID controller parameters using the PSO method on the MATLAB Simulink obtained optimal results where the value of Kp = 8.9099, K = 2.1469, and Kd = 0.31952 with the value of rise time of 0.0740, settling time of 0.1361 and overshoot of 0. Then the results of hardware testing by entering the PID value in the Arduino IDE software produce a stable motor speed response where Kp = 1.4551, ...
Tuning of PID controller parameters is an important problem in control field. To solve this problem we used an Advanced Particle Swarm Optimization which is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. The proposed method has fast searching speed compared to standard PSO. Furthermore this method accelerates the convergence. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. This new algorithm is proposed to augment the original PSO searching speed. The optimum tuned PID controller is applied to a DC motor. The simulation results show that the PID controller designed by APSO demonstrates better results than GA, PSO and even Improved PSO technique.
Tuning of PID Controller for Position Control of DC Servo Motor using Luus-Jaakola Optimization
— This paper presents an efficient and fast tuning method of controller parameters for position control of DC servo motor. A control scheme for PID controller tuning is proposed here which is based on Luus-Jaakola Optimization. Luus-Jaakola optimization procedure is used to optimize large scale nonlinear optimization problems. The tuning of PID controllers is accomplished by minimizing the Integral-Square-Error (ISE). The ISE is minimized using the Luus–Jaakola (LJ) optimization algorithm. LJ optimization procedure is a simple, yet powerful method for optimization available in literature. The results of PID control using LJ method are compared with the Ziegler-Nichol's (ZN) tuning. The simulation results show that the LJ method performs better results than the ZN technique and can successfully be used for tuning of PID controllers for DC servo motor. Keywords— Luus Jaakola (LJ), DC servo motor, Integral-Square Error (ISE), Ziegler-Nichols(ZN)
PID PARAMETERS OPTIMIZATION USING ADAPTIVE PSO ALGORITHM FOR A DCSM POSITI
This paper demonstrates a novel algorithm in particle swarm optimization, which is called Adaptive PSO (APSO), to optimize the gains of a proportional-integral-derivative (PID) controller concurrently to control the position of a DC servomotor (DCSM). The parameters of the PID controller (K P , K i , and K D ) are obtained, firstly, by using Ziegler-Nichols tuning method, secondly by using standard PSO algorithm, thirdly by using MPSO algorithm, and finally by using APSO algorithm. The transient response analysis has been done by comparing the performance of the four tuning methods. The results showed that the proposed algorithm gives better performance than the other optimization algorithms, and the DCSM reached very fast to the final position. The analysis and simulations have been made in MATLAB R2010a software environment.