Asymmetric Bouc-Wen hysteresis modeling and inverse compensation for piezoelectric actuator via a genetic algorithm-based particle swarm optimization identification algorithm

The hysteresis characteristics, which commonly existed in smart materials-based actuators, play a significant role in precision control technology. In this article, a modified Bouc-Wen model which can describe the asymmetric hysteresis characteristics of piezoelectric ceramic actuators is investigated. The corresponding parameters of the modified Bouc-Wen hysteresis model are identified through a genetic algorithm-based particle swarm optimization algorithm. Compared with independent particle swarm optimization method which is easily trapped in the local extremum, the proposed genetic algorithm-based particle swarm optimization features the strong searching ability both in early global search period and the later local search period. The experimental results show that the asymmetric Bouc-Wen model identified via genetic algorithm-based particle swarm optimization algorithm are more accurate than that identified through independent particle swarm optimization or genetic algorithm approach, and the maximum displacement error and the maximum relative error between the genetic algorithm-based particle swarm optimization model and the experimental value are 0.20 mu m and 14.28%, respectively, which are much smaller than that of particle swarm optimization method with 0.67 mu m and 47.85% and genetic algorithm method with 0.35 mu m and 25%. In order to further verify the accuracy of the identified model, the hysteresis compensation of piezoelectric ceramic actuator was realized using the feedforward controller based on the inverse Bouc-Wen model.