A hysteresis functional link artificial neural network for identification and model predictive control of SMA actuator

In this paper, a modified Hysteresis Functional Link Artificial Neural Network (HFLANN) is proposed to identify and control a Shape Memory Alloy (SMA) actuator, which has an inherent hysteresis phenomenon. In this structure, a hysteresis operator combined with the Functional Link Artificial Neural Network (FLANN) to employ the hysteresis phenomenon and the dynamic of the SMA actuator. The hysteresis operator is introduced to capture the SMA hysteresis. And the FLANN is employed to approximate the dynamic of the system. In identification problem, the FLANN parameters are trained by Particle Swarm Optimization technique. For control problem, a Model Predictive Controller based HFLANN is derived to control the system. The identification results show that the HFLANN can employ for the SMA dynamic. The simulation and experimental results demonstrated the effectiveness of the proposed algorithm. The SMA hysteresis phenomenon is compensated completely by proposed controller. (C) 2012 Elsevier Ltd. All rights reserved.