A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System

2004 ◽  
Vol 19 (2) ◽  
pp. 384-391 ◽  
Author(s):  
Z.-L. Gaing
Keyword(s):  
Particle Swarm Optimization ◽  
Optimum Design ◽  
Pid Controller ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Avr System

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>

Design and Experiment of Iterative Learning Controller Based on Particle Swarm Optimization Approach with New Bounded Constraints Technique

2013 ◽  
Vol 284-287 ◽  
pp. 2233-2237 ◽  
Author(s):  
Yi Cheng Huang ◽  
Yi Hao Li ◽  
Shu Ting Li
Keyword(s):  
Particle Swarm Optimization ◽  
Iterative Learning Control ◽  
Pid Controller ◽  
Particle Swarm ◽  
Iterative Learning ◽  
Optimization Approach ◽  
Proportional Integral Derivative ◽  
Swarm Optimization ◽  
Nonminimum Phase ◽  
Motion Profile

This paper utilizes the Improved Particle Swarm Optimization (IPSO) with bounded constraints technique for adjusting the gains of a Proportional-Integral-Derivative (PID) and Iterative Learning Control (ILC) controllers. This study compares the conventional ILC-PID controller with proposed IPSO-ILC-PID controller. A cycloid trajectory for mimicking the real industrial motion profile is applied. Two system plants with nonminimum phase are numerically simulated. Proposed IPSO with bounded constraints technique is evaluated on one axis of linear synchronous motor (LSM) with a PC-based real time controller. Simulations and experiment results show that the proposed controller can reduce the error significantly after two iterations.

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