Adaptive hybrid Particle Swarm Optimization-Gravitational Search Algorithm for fuzzy controller tuning

Abstract

This paper introduces an innovative adaptive hybrid Particle Swarm Optimization (PSO)-Gravitational Search Algorithm (GSA) dedicated to the optimal tuning of Takagi-Sugeno-Kang PI-fuzzy controllers (T-S-K PI-FCs). The adaptive hybrid PSO-GSA is comprised from five stages, which support the solving of optimization problems with objective functions that depend on the control error and on the output sensitivity function, and the variables of the objective functions are the fuzzy controller tuning parameters. The adaptive hybrid PSO-GSA is included in the controller tuning to offer control systems with T-S-K PI-FCs that ensure a reduced process parametric sensitivity. Digital simulation and experimental results are given to validate the fuzzy controller tuning in a laboratory nonlinear servo system application.