Parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system based on the improved particle swarm optimization method

An improved particle swarm optimization method that has better equilibrium characteristic between global search and local search is proposed for parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system. In this article, a general electromechanical-based piezoelectric positioning systems’ hysteresis nonlinear dynamic model suitable for engineering optimization is introduced, and a parameter identification technique based on improved particle swarm optimization for this dynamic model is presented. For increasing the speed and precision of identification process, the hysteresis effect is described by the generalized Prandtl–Ishlinskii model, and the symmetric solutions of the system are applied to solve the hysteresis nonlinear dynamics equations and then the working process of the improved particle swarm optimization method is given. In order to verify the effectiveness of the proposed method, comparisons between experimental data and simulation data are carried out. The results demonstrate that improved particle swarm optimization method can make the hysteresis nonlinear dynamic model with identified parameters fit the experimental data with higher precision, and since the application of the symmetric solutions, the identification time is greatly reduced. Therefore, the improved particle swarm optimization method is an optional effective technique for identifying the parameters of hysteresis nonlinear dynamic model for the piezoelectric positioning system.