Decomposition-Learning-Based Output Tracking to Simultaneous Hysteresis and Dynamics Control: High-Speed Large-Range Nanopositioning Example

In this brief, a decomposition-learning-based output tracking approach is proposed to compensate for both hysteresis and dynamics effects on output tracking of hysteresis systems such as smart actuators. Simultaneous hysteresis and dynamics control (SHDC) is needed to fully exploit smart or soft actuators/sensors for high-speed, large-range positioning/tracking. It remains still, however, as a challenge to achieve SHDC with both precision (performance) and robustness in general output tracking (i.e., not restricted to periodic/repeated operations), and without complicity in modeling and controller design and/or online implementation. The proposed approach aims to address these challenges, by utilizing libraries of input-output elements constructed offline to online decompose the partially known (i.e., previewed) desired output trajectory and synthesize the control input. Iterative learning control techniques are used a priori to obtain the input elements each tracking the corresponding output elements accurately, and the Preisach modeling of hysteresis is employed to obtain the combination coefficients of the synthesized control input. An experimental implementation to high-speed, large-range nanopositioning using piezoelectric actuator is presented to demonstrate the efficiency and efficacy of the proposed approach in achieving SHDC.