A novel identification approach of Bouc–Wen model parameter for piezoelectric hysteresis characteristic based on a modified whale optimization algorithm

Abstract

PurposeDue to the non-linear nature of the hysteresis behavior, the accurate identification of the parameters of the Bouc–Wen hysteresis model is still a challenging problem. The purpose of this paper is to explore the potential of a heuristic improved whale optimization algorithm (IWOA) to accurately identify the model parameters, which has never been applied to the field of piezoelectric hysteresis identification.Design/methodology/approachBased on the analysis of the Bouc–Wen model structure and WOA optimization process, an approach that can fully exploit the potential of WOA is proposed. In this work, the position updating formula is improved by introducing non-linear weights, and the convergence factor formula is modified. And thus, the iteration speed, accuracy and stability of the classical WOA can be improved.FindingsThe experimental results show that the model output is in good agreement with the response of the real piezoelectric platform. Compared with the standard WOA and particle swarm optimization algorithms, the search performance of the proposed IWOA is better than those two competitors in terms of convergence speed and identification accuracy.Originality/valueAn IWOA is proposed according to the properties of the Bouc–Wen model and piezoelectric hysteresis. It has been approved that the algorithm has a good prospect in the identification of piezoelectric hysteresis systems. Furthermore, this method is easy to implement and is a good candidate algorithm to identify Bouc–Wen model parameters.