Parameters identification of Bouc–Wen hysteresis model for piezoelectric actuators using hybrid adaptive differential evolution and Jaya algorithm

Abstract

The nonlinearities hysteresis of the piezoelectric-PZT actuator can greatly degrade in precise positioning applications. Therefore, modeling and identifying the hysteresis parameter of PZT actuator are still many challenges nowadays. In this paper, the hybrid adaptive differential evolution and Jaya algorithm (aDE-Jaya) is proposed to identify the Bouc–Wen hysteresis model of a piezoelectric actuator. In the aDE-Jaya algorithm, the improvement is focused on a hybrid mutant operator “DE/rand/1” and Jaya operator tried to balance between two contradictory aspects of their performance: exploration and exploitation and adaptive control parameters (mutant factor F, crossover rate CR, population size NP) to enhance the convergence efficiency. To prove the effectiveness and robustness of the proposed aDE-Jaya algorithm, it is tested on 8 benchmark functions and compared with other state-of-the-art optimizations. The comparison results show that aDE-Jaya has better performance in convergence rate and accuracy. After that, aDE-Jaya is applied to identify the Bouc–Wen hysteresis model based on experimental input–output data. The identified Bouc–Wen hysteresis resulted is used to design the feedforward controller to test accurate identification. As a consequent, the proposed aDE-Jaya algorithm can successfully identify the highly hysteretic nonlinearity of the piezoelectric actuator with perfect precision.