Application of Particle Swarm Optimization in Design of PID Controller for AVR System

2013 ◽  
Vol 2 (3) ◽  
pp. 1-17 ◽  
Author(s):  
H. F. Abu-Seada ◽  
W. M. Mansor ◽  
F. M. Bendary ◽  
A. A. Emery ◽  
M. A. Moustafa Hassan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Stability Margin ◽  
Pso Algorithm ◽  
Step Response ◽  
Voltage Response ◽  
Swarm Optimization ◽  
Avr System ◽  
Terminal Voltage

This paper presents a method to get the optimal tuning of Proportional Integral Derivative (PID) controller parameters for an AVR system of a synchronous generator using Particle Swarm Optimization (PSO) algorithm. The AVR is not initially robust to variations of the power system parameters. Therefore, it was necessary to use PID controller to increase the stability margin and to improve performance of the system. Fast tuning of optimum (PID) controller parameter yield high quality solution. New criteria for time domain performance evaluation was defined. Simulation for comparison between the proposed method and Ziegler-Nichols method is done. The proposed method was indeed more efficient also. The terminal voltage step response for AVR model will be discussed in different cases and the effect of adding rate feed back stabilizer to the model on the terminal voltage response. Then the rate feedback will be compared with the proposed PID controller based on use of (PSO) method to find its coefficients. Different simulation results are presented and discussed.

Research on Double Closed-Loop DC Speed-Regulation System Based on Improved Particle Swarm Optimization

2013 ◽  
Vol 846-847 ◽  
pp. 317-320 ◽  
Author(s):  
Le Peng Song ◽  
Han Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Step Response ◽  
Regulation System ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Improved Pso

For the defects of the parameter tuning and optimization of the PID controller uses an improved Particle Swarm Optimization (IPSO) algorithm to apply on the dual closedloop DC speed tuning system and adjust PID controller parameters online. The optimization result of adopting step response of the improved PSO algorithm is analyzed. It shows that using the improved PSO algorithm will obtain better dynamic performance, follow faster and more robustness than the traditional engineering design method. It provides a good performance of practical method for PID parameters optimization.

scholarly journals Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

2020 ◽  
pp. 184-188
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Optimization Technique ◽  
Heuristic Method ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Computational Method ◽  
Swarm Optimization ◽  
Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

scholarly journals Solar System Array by Fuzzy Logic based on MPPT Algorithm

2020 ◽  
Vol 9 (6) ◽  
pp. 1132-1139
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Solar System ◽  
Pid Controller ◽  
Performance Enhancement ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Point Tracking ◽  
Power Point

In recent times a huge attention has been given on development of proper planning In this paper we present a top dimension perspective on forefront status of Closed circle ID system the use of PID Controller from explicit creators. The proportional– integral– subsidiary (PID) controller is the most extreme comprehensively ordinary controller inside the business bundles, specifically in strategy enterprises in light of fabulous expense to profit proportion. In this paper we focus on MPPT based solar system performance enhancement by use of fuzzy logic controller’s designs optimized by particle swarm optimization (PSO). We have described about different latest A.I. techniques that has been hybrid with fuzzy logic for improving PV array based solar plants performance in recent time. The artificial intelligence technique applied in this work is the Particle Swarm Optimization (PSO) algorithm and is used to optimize the membership functions for maximum power point tracking rule set of the FLC. By using PSO algorithm, the optimized FLC is able to maximize energy to the system loads while also maintaining a higher stability and speed as compared to P& O based MPPT algorithm

Robustness Study of Fractional Order PID Controller Optimized by Particle Swarm Optimization in AVR System

2016 ◽  
Vol 6 (5) ◽  
pp. 2033 ◽  
Author(s):  
N. Ramesh Raju ◽  
P. Linga Reddy
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Pid Controller ◽  
Design Method ◽  
Particle Swarm ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Modeling And Control ◽  
Avr System ◽  
Fractional Order Pid

<p>In this paper a novel design method for determining fractional order PID (PI<sup>λ</sup>D<sup>µ</sup>) controller parameters of an AVR system using particle swarm optimization algorithm is presented. This paper presents how to employ the particle swarm optimization to seek efficiently the optimal parameters of PI<sup>λ</sup>D<sup>µ</sup> controller. The robustness study is made for this controller against parameter variation of AVR system. This work has been simulated in MATLAB environment with FOMCON (Fractional Order Modeling and Control) tool box.The proposed PSOPI<sup>λ</sup>D<sup>µ</sup> controller has superior performance and robust compared to GA tuned PI<sup>λ</sup>D<sup>µ</sup> controller. The results are also compared with PSO tuned PID controller.</p>

Landsman Converter Based Particle Swarm Optimization Technique

2017 ◽  
Vol 8 (3) ◽  
pp. 619
Author(s):  
Ramesh P. ◽  
V. Mathivanan
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Control Technique ◽  
Swarm Optimization ◽  
Particle Swarm Optimization Technique ◽  
Comparative Results

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

scholarly journals Performance of PID Controller Tuning based on Particle Swarm Optimization and Firefly Algorithm

2019 ◽  
Vol 8 (3S2) ◽  
pp. 225-230 ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Firefly Algorithm ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Transient Responses ◽  
Swarm Optimization ◽  
Pid Controller Tuning ◽  
Tank System

This paper shows the study of tuning the Proportional-Integral-Derivatives (PID) in the application of coupled tank system. The controller was tuned by using an optimization technique which is a Firefly Algorithm (FA) and a Particle Swarm Optimization (PSO) Algorithm. Both FA and PSO performance were evaluated by using performance index of Integral Time Square Error (ITSE). The systems response of FA and PSO were gathered and compared in term of transient responses, ITSE and standard deviation by considering the system condition of with and without a disturbance. The simulation is conducted by using MATLAB software. The result shows that the FA giving a better system performance compared to PSO in term of overall transient responses.

scholarly journals PID Controller Design for Cruise Control System using Particle Swarm Optimization

2017 ◽  
Vol 43 (2) ◽  
pp. 30-35
Author(s):  
Ahmed Abdulnabi
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Controller Design ◽  
Robustness Analysis ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Peak Time ◽  
Cruise Control ◽  
Swarm Optimization ◽  
Best Response

This paper presents a design of a Proportional-Integral-Derivative (PID) controller for automobile cruise controlsystem. The parameters of the PID controller, which are the proportional ( ), derivative ( ) , and integrator ( ), have beenselected using Particle Swarm Optimization (PSO) algorithm. In this study, the overall system performance has beencompared with other predesigned controllers (conventional PID, Fuzzy logic PID, state space, and Genetic algorithm basedPID controller). The simulation result illustrates that PSO based PID controller gives the best response in terms of settlingtime, rise time, peak time, and maximum overshot. The robustness analysis shows that the system is robust despite thedeviations in some of the system parameters.

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