A novel disturbance observer based sliding mode combined repetitive learning control strategy for large range nanopositioning system

A novel disturbance observer based sliding mode combined repetitive learning strategy is proposed to compensate the nonlinear hysteresis and disturbance of large range piezoelectrical nanopositoning system. A novel observer is designed to estimate the disturbance in realtime. Sliding mode control combined with Lyapunov based repetitive learning control strategy is constructed as feedback controller to eliminate hysteresis nonlinearity, unmodeling dynamics, and drift disturbance during the periodic tracking. With Lyapunov based repetitive learning algorithm and disturbance observer, periodic unmodeling dynamics and disturbance are compensated, which greatly decrease the chattering of sliding mode controller. Compared to traditional sliding mode controller and repetitive controller, this combined controller construction can minimize chattering and compensate tracking errors more effectively. Sufficient simulations and experiments are performed to validate that the effectiveness of proposed strategy.