Neural network based predictive control with optimized search space for dynamic tracking of a piezo-actuated nano stage

This paper presents an intelligent modified predictive control approach with squeezed search space, for tracking control of piezo-actuated nano stage. The model obtained from the gray box neural network is first dynamically linearized to avoid calculation of inverse hysteresis model. The optimum control values of the previous control cycle are used to construct a squeezed search space, which reduces the computation burden and improves the tracking control performance. The effectiveness of the proposed scheme is verified theoretically by deriving a convergence analysis and by experimental results. The results show that the proposed approach significantly improves the dynamic tracking performance for high-frequency reference signals than existing results in the literature.