Study of Tuning of Pid Controller by Using Particle Swarm Optimization (original) (raw)
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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.
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.
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.
Observing the effect of Particle Swarm Optimization Algorithm Based PID Controller
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, 2018
Observing the effect of PSO algorithm on the PID (Proportional-Integral-Derivative) controller is an advanced approach for getting a stable and linear response of any system. From few decades conventional PID tuning rules are used for analyzing any complex system. But these rules did not give always a satisfactory result as our requirement. That's why a better algorithm was introduced which is actually based on Evolutionary Computation method. This methodology provides a very high accuracy in the response in comparison with other tuning rules. From the very past, PID controller has been very popular and is being used in maximum industries. So, there's always a need to control the accuracy and efficiency of the controller because depending on this controller the whole industry might be functioning. If any large error occurs in the controller (PID), the functioning of the industry might be hampered. That's why using PSO algorithm for determining the PID parameter is a good idea to get an efficient and accurate output. This approach may help in future to improve the performance of PID controller and also may help to reduce errors encountered in the industries.
Study on PID parameters Tuning Based on Particle Swarm Optimization
To address the PID parameter tuning problem, inspired by the swarm intelligence optimization algorithm, a tuning method of PID parameters based on particle swarm algorithm is proposed. To find an optimal set of PID control parameters in the target space, three parameters of the PID are as particles based on the fitness function. By designing optimization programs, air-conditioning temperature control system experiments were carried out as an example. The results show that the optimization can improve the dynamic control performance of the system.
Tuning Pid Controller for Load Frequency Control Using Particle Swarm Optimization Technique
The International Conference on Electrical Engineering (Print), 2006
This paper describes the application of Particle Swarm Optimization (PSO) technique to optimize a PID controller parameters for Load Frequency Control (LFC). The robustness of the proposed controller is investigated through parameters variations and changing the magnitude of load disturbance. The simulation results show that the applied PSO-based PID controller is achieved good performance even in the presence of the generation rate constraint (GRC). A comparative study results is made between the H ∞ controller and the proposed one. The performance is shown to be better for the new PID controller.
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.
Optimized PID Controller for Low PowerApplications using Particle Swarm Optimization
International journal of engineering research and technology, 2019
Controllers are used to modify the behaviour of a system so it behaves in a specific desirable way. Proportional-Integral-Derivative controller has high effectiveness, simplicity in implementation so that, it is the most widely used controller in industries for many applications. Conventional method of tuning PID controller creates big overshoot. This is an acceptable result for some purpose but not optimal for all applications. The Artificial Intelligence (AI) method of Particle Swarm Optimization algorithm is employed in tuning of PID controller. The main aim of this particular work is to compare the conventional method and PSO method of tuning the PID controller and to prove that PSO method is more efficient than the conventional method in tuning the control parameters. Such type of optimized controllers may be used for low power applications with high efficiency.
Particle Swarm Optimization of PID Controller under Constraints on Performance and Robustness
IJEEC - INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTING
This paper presents a design procedure of the PID controller where optimal parameters of controller * * * *p i d f (k ,k ,k ,T ) areobtained by solving the constrained optimization problem. The objective function is given in the form of the Integral of Absolute Error(IAE) under specifications to achieve predictable performance and robustness. The constraints within the optimization problem setupare desired maximum sensitivity, desired maximum complementary sensitivity and maximum sensitivity to measurement noise underhigh frequencies. The optimization problem is transformed to an unconstrained problem using penalty function approach. Solution tothe optimization problem is obtained using Particle Swarm algorithm (PSO) which leads to an efficient suppression of disturbance aswell as an adequate reference tracking performance of the closed-loop system with negligible overshoot. The suggested method isapplicable to the large class of stable, integral and unstable processes, processes wi...
Tuning of digital PID controller using particle swarm optimization
2010
PID controllers are one of the most applicable controllers in different industries. The main important need in application of these controllers is their parameters tuning in order to gain desired result. Existing tuning rules for their design are usually based on trial and error which are so time consuming, not accurate and have considerable error. In this paper, an accessible method with high accuracy and speed has been presented for determination of these control parameters, using PSO optimization algorithm and performance assessment criteria. The results show that there is a considerable difference between this method's results and the other method's.