Hysteresis Modeling of a Magnetic Shape Memory Alloy Actuator Using a NARMAX Model and a Long Short-Term Memory Neural Network

Hysteresis primarily affects the positioning accuracy of the magnetic shape memory alloy-based actuator (M-SMAA). This paper proposes the use of the nonlinear autoregressive moving average with an exogenous input (NARMAX) model to describe the complex dynamic hysteresis of M-SMAA. First, an improved Prandtl–Ishlinskii operator is proposed as the exogenous variable function for the NARMAX model, using a hyperbolic tangent function as the input to the exogenous variable function, to better capture and represent the multivalued mapping hysteresis in M-SMAA. Then, a long short-term memory neural network is introduced to construct the NARMAX model, further optimizing its performance. Finally, the experimental results verify the effectiveness of the model.