Chaotic particle swarm optimization algorithm parametric identification of Bouc-Wen hysteresis model for piezoelectric ceramic actuator

Abstract

A chaotic particle swarm optimization (CPSO) algorithm is proposed by introducing chaos state into the original Particle Swarm Optimization (PSO) which aims to solving the flaws of easy plunging into local optimum and losing search ability in the last period for the fast particle velocity decrease. CPSO algorithm takes advantage of the ergodicity, randomicity, and regularity of chaos to make chaotic searching for the global extremun at the same time with the particle swarm optimization. This algorithm synthesizes the high efficiency of global optimization of PSO algorithm and the ergodicity and randomicity of local search of chaotic algorithm. This paper utilizes aforementioned algorithm to identify the Bouc-Wen hysteresis model for piezoelectric ceramic actuators (PCA). The experimental results show that the model identified by CPSO algorithm has better performance than that by PSO algorithm.