A PSO-based adaptive fuzzy PID-controllers

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...

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Vydáno v:Simulation modelling practice and theory Ročník 26; s. 49 - 59
Hlavní autoři: Chiou, Juing-Shian, Tsai, Shun-Hung, Liu, Ming-Tang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.08.2012
Témata:
Automotive components
Evolutionary programming (EP)
Fuzzy
Fuzzy logic controllers (FLCs)
Fuzzy systems
Integral of Absolute Error (IAE)
Mathematical models
Membership functions
Optimization
Particle swarm optimization (PSO)
Proportional-integral-derivative (PID)
Q-learning
Searching
Suspension systems
Membership functions
Proportional-integral-derivative (PID)
Q-learning
Fuzzy logic controllers (FLCs)
Evolutionary programming (EP)
Particle swarm optimization (PSO)
Integral of Absolute Error (IAE)
ISSN:1569-190X, 1878-1462
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Shrnutí: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.
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ISSN:1569-190X
1878-1462
DOI:10.1016/j.simpat.2012.04.001