Fuzzy controller design using group-crossover particle swarm optimization for truck reversing control

2011 ◽  
Author(s):  
Chia-Feng Juang ◽  
Yu-Cheng Chang ◽  
Chia-Hung Hsu ◽  
I-Fang Chung
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Particle Swarm ◽  
Swarm Optimization

Self-Tuning Robust Fuzzy Controller Design Based on Multi-Objective Particle Swarm Optimization Adaptation Mechanism

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Edson B. M. Costa ◽  
Ginalber L. O. Serra
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Adaptation Mechanism ◽  
Swarm Optimization ◽  
Adaptive Fuzzy ◽  
Multi Objective

In this paper, an adaptive fuzzy controller design methodology via multi-objective particle swarm optimization (MOPSO) based on robust stability criterion is proposed. The plant to be controlled is modeled from its input–output experimental data considering a Takagi–Sugeno (TS) fuzzy nonlinear autoregressive with exogenous input model, by using the fuzzy C-means clustering algorithm (antecedent parameters estimation) and the weighted recursive least squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital proportional-integral-derivative (PID) controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time-varying delay are presented to illustrate the efficiency and applicability of the proposed methodology.

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