☆ 4.6 Article

A PSO-based adaptive fuzzy PID-controllers

SIMULATION MODELLING PRACTICE AND THEORY (2012)

Journal

SIMULATION MODELLING PRACTICE AND THEORY

Volume 26, Issue -, Pages 49-59

Publisher

ELSEVIER SCIENCE BV

DOI: 10.1016/j.simpat.2012.04.001

Keywords

Fuzzy logic controllers (FLCs); Particle swarm optimization (PSO); Evolutionary programming (EP); Proportional-integral-derivative (PID); Q-learning; Integral of Absolute Error (IAE); Membership functions

Categories

Computer Science, Interdisciplinary Applications Computer Science, Software Engineering

Funding

National Science Council, Taiwan, ROC [NSC 99-2221-E-218-002, NSC-100-2221-E-027-017]
Institute for Information Industry, Ministry of Economy Affairs of the Republic of China

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Abstract

In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system's behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance. (C) 2012 Elsevier B.V. All rights reserved.

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