Rate-Dependent Hysteresis Model Based on LS-SVM for Magnetic Shape Memory Alloy Actuator

Magnetic shape memory alloy-based actuators (MSMA-BAs) have extensive applications in the field of micro-nano positioning technology. However, complex hysteresis seriously affects its performance. To describe the hysteresis of MSMA-BA, this study proposes integrating a hysteresis operator and the rate-of-change function of the input signal into the least squares support vector machine (LS-SVM) framework to construct a rate-dependent dynamic hysteresis model for MSMA-BAs. The hysteresis operator converts the multi-valued mapping of hysteresis into a one-to-one mapping, while the rate-of-change function of the input signal captures the rate dependence of the hysteresis, thereby enhancing the model’s ability to describe complex hysteresis. In addition, with the powerful nonlinear fitting capability and good generalization of LS-SVM, the dynamic performance of the proposed model is effectively improved. Experimental results show that the proposed model accurately describes the hysteresis of MSMA-BA.