Optimal tuning of PID controller parameters on a DC motor based on advanced particle swarm optimization algorithm (original) (raw)
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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 Using Particle Swarm Optimization (PSO)
The aim of this research is to design a PID Controller using PSO algorithm. The model of a DC motor is used as a plant in this paper. The conventional gain tuning of PID controller (such as Ziegler-Nichols (ZN) method) usually produces a big overshoot, and therefore modern heuristics approach such as genetic algorithm (GA) and particle swarm optimization (PSO) are employed to enhance the capability of traditional techniques. However, due to the computational efficiency, only PSO will be used in this paper. The comparison between PSO-based PID (PSO-PID) performance and the ZN-PID is presented. The results show the advantage of the PID tuning using PSO-based optimization approach.
2015
the problem of identification of controller parameters for DC motor has been considered here. Open loop system has been used for Cohen and Coon Method. The proportional, integral and derivative gains are obtained from the process reaction curve. The loop is then closed. Damped Oscillation of Ziegler-Nichols Method is used. This curve, gives the integral and derivative gain from the period of oscillation. The ISE, IAE and IATE are used as fitness function in Particle Swarm Optimization. Then proportional, integral and derivative gains are found out through number of iterations. At last the outputs of three methods are compared.
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...
… Electronic Journal of …, 2009
In this paper, an intelligent controller of DC Motor drive is designed using particle swarm optimization (PSO) method for formative the optimal proportional-integral-derivative (PID) controller Tuning parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Comparing with fuzzy logic controller using PSO intelligent algorithms, the planned method is more proficient in improving the speed loop response stability, the steady state error is reduced, the rising time is perfected and the disturbances do not affect the performances of driving motor with no overtaking.
Speed Control of DC Motor Using Particle Swarm Optimization Technique by PSO Tunned PID and FOPID
international journal of engineering trends and technology, 2014
The objective of this work is to design a speed controller of a DC motor by finding of PID and FOPID parameters using bio-inspired optimization technique of Particle Swarm Optimization (PSO). Here, model of a DC motor is considered as a second order system for speed control. In this work bio-inspired optimization technique in controllers and their advantages over conventional methods is discussed using MATLAB/Simulink. This proposed optimization methods could be applied for higher order system also to provide better system performance with minimum errors. The main aim is to apply PSO technique to design and tune parameters of PID controller to get an output with better dynamic and static performance. The application of PSO to the PID and FOPID controller imparts it the ability of tuning itself automatically in an on-line process while the application of optimization algorithm to the PID controller makes it to give an optimum output by searching for the best set of solutions for the ...
In this paper, an intelligent controller of DC Motor drive is designed using particle swarm optimization (PSO) method for formative the optimal proportional-integral-derivative (PID) controller Tuning parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Comparing with fuzzy logic controller using PSO intelligent algorithms, the planned method is more proficient in improving the speed loop response stability, the steady state error is reduced, the rising time is perfected and the disturbances do not affect the performances of driving motor with no overtaking.
00 AM-5 : 00 PM Optimal Control of DC Motors Using PSO Algorithm for Tuning PID Controller
2019
The DC motors are widely used in the mechanisms that require control of speed. Different speed can be obtained by changing the field voltage and the armature voltage. The classic PID controllers are widely used in industrial process for speed control. But they aren’t suitable for high performance cases, because of the low robustness of PID controller. So many researchers have been studying various new control techniques in order to improve the system performance and tuning PID controllers. This paper presents particle swarm optimization (PSO) method for determining the optimal PID controller parameters to find the optimal parameters of DC Motor speed control system. The DC Motor system drive is modeled in MATLAB/SIMULINK and PSO algorithm is implemented using MATLAB toolbox. The results obtained through simulation show that the proposed controller can perform an efficient search for the optimal PID controller. Simulation results show performance improvement in time domain specificat...
Comparison Performances of PSO and GA to Tuning PID Controller for the DC Motor
Sakarya University Journal of Science, 2019
A DC motor widely uses for sensitive speed and position in industry. Stability and productivity of a system are important for controlling of a DC motor speed. Stable of speed which affected from load fluctuation and environmental factors. Therefore, it is important for the speed value which is required as constant and to keep it as its value. In this study, it is aimed that the speed value which is achieved as required value and keeping it as constant using Proportional, Integral and Derivative (PID) controller for tuning parameters. Firstly, Ziegler-Nichols (ZN) is one of a traditional method used. PID parameters are determined with responses of open-loop under running system. Later, parameters of the PID are estimated using two metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). As a result, three algorithms' results are compared based on five criteria. The PSO algorithm produces better results than Genetic Algorithm for each criteria.
The proportional-integral-derivative (PID) controllers were the most popular controllers of this century because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, PID controllers are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. The computational intelligence has purposed genetic algorithms (GA) and particle swarm optimization (PSO) as opened paths to a new generation of advanced process control. These advanced techniques to design industrial control systems are, in general, dependent on achieving optimum performance with the controller when facing with various types of disturbance that are unknown in most practical applications. This paper presents a comparison study of using two algorithms for the tuning of PID-controllers for processes which represents a subsystem of complex industrial processes, known to be non-linear and time variant. Simulation results showed that the PID control tuned by PSO provides an adequate closed loop dynamic for the Ball and Hoop system experiment in wide range operations.