Neural network modeling and generalized predictive control for Giant Magnetostrictive actuators

In the application of the giant magnetostrictive actuators (GMA), hysteresis of the GMA is particularly significant and causes undesired effect in the control system. This paper investigates the application of neural network based on generalized predictive control to eliminate the hysteresic effect of GMA. The modified Elman neural network is used as the multi-step predictive model, the fused identification model is proposed to improve the predictive and control precision. The modified Elman neural network on-line learning improves the control system adaptability to the unpredicted operating environment for GMA. Simulations on GMA position control are included to illustrate the effectiveness of the proposed control scheme.