Study on dynamic hysteresis characteristic modeling and parameter identification methods for high-voltage piezoelectric actuators

High-voltage piezoelectric actuators are widely used in the field of active vibration control of aerospace structures due to their advantages of strong load capacity, large nominal thrust, and fast response. However, its inherent hysteresis characteristics will affect the efficiency and stability of the piezoelectric active control system. Aiming at the problem of hysteresis characteristic modeling and parameter identification in high-voltage piezoelectric actuators, a dynamic hysteresis characteristic modeling and parameter identification method for piezoelectric actuators based on the Hammerstein model and a modified adaptive particle swarm optimization algorithm is proposed. Firstly, the asymmetric Bouc-Wen model is used to describe the static hysteresis effect. Combining with the transfer function model further, the Hammerstein rate-dependent dynamic hysteresis model for high-voltage piezoelectric actuator is constructed. Secondly, a modified adaptive particle swarm optimization algorithm is proposed based on the nonlinear decreasing inertia weight strategy and dynamic learning factors, and the hysteresis model parameters are identified by using the hysteresis characteristic experimental data of the high-voltage piezoelectric actuator, which verifies the superiority of the present algorithm over the traditional particle swarm optimization algorithm. Thirdly, the hysteresis effect prediction experiments of the high-voltage piezoelectric actuator demonstrate that the established Hammerstein model can efficiently describe its dynamic hysteresis effect and has a strong adaptability to the changes in the frequency and amplitude of the actuator driving voltage in the concerned frequency band.