A fractional-order modelling and parameter identification method via improved driving training-based optimization for piezoelectric nonlinear system

The accurate identification of the parameters of fractional-order systems is still a challenging problem. The purpose of this paper is to establish a fractional hysteresis model based on a Bouc-Wen differential equation and an improved driving training-based optimization (DTBO) parameter identification method to accurately characterize the nonlinear characteristics of the piezoelectric hysteresis system. In this paper, there are two contributions. On the one hand, an accurate fractional-order hysteresis model is proposed and its rate-dependent property is proved based on the definition of fractional calculus; on the other hand, an improved DTBO optimization method with enhanced performance is proposed to achieve excellent precision and robustness for the purpose of parameter identification. The unique features of the improved DTBO algorithm include an instructorlearner separation strategy, a new update mechanism of instructor position, and an improved phase of the learner group. The results show that the output of the model is consistent with that of the actual piezoelectric platform. Compared with the standard DTBO algorithm and Antlion Optimization algorithm, the improved DTBO algorithm has better performance. The novelty of this paper is that a new high-performance heuristic optimization method is proposed for the particular problem of the parameter identification of fractional-order hysteresis models. The proposed method holds promise as a good candidate in the field of hysteresis modelling and identification.