A Composite Controller for Piezoelectric Actuators with Model Predictive Control and Hysteresis Compensation

Piezoelectric actuators (PEAs) are ubiquitous in nanopositioning applications due to their high precision, rapid response and large mechanical force. However, precise control of PEAs is a challenging task because of the existence of hysteresis, an inherent strong nonlinear property. To minimize its influence, various control methods have been proposed in the literature, which can be roughly classified into three categories: feedforward control, feedback control and feedforward-feedback control. Feedforward-feedback control combines the advantages of feedforward control and feedback control and turns into a better control scheme. Inspired by this strategy, a composite controller is proposed for the tracking control of PEAs in this paper. Specifically, the model of PEAs is constructed by a multilayer feedforward neural network (MFNN). This model is then instantaneously linearized, which leads to an explicit model predictive control law. Then, an inverse Duhem hysteresis model is adopted as a feedforward compensator to mitigate the hysteresis nonlinearity. Experiments are designed to validate the effectiveness of the proposed method on a piezoelectric nanopositioning stage (P-753.1CD, Physik Instrumente). Comparative experiments are also conducted between the proposed method and some existing control methods. Experimental results demonstrate that the root mean square tracking error of the proposed method is reduced to 16% of that under the previously proposed model predictive controller [16].