Design of Dual-Motor Drive Composite Control Strategy Based on Iterative Learning Feedforward Control and Super-Twisting Sliding Mode Observer

Periodic and non-periodic disturbances significantly affect the tracking accuracy of servo systems. A dual-motor drive composite control strategy based on iterative learning feedforward control and super-twisting sliding mode observer is proposed. Initially, a novel reaching law capable of dynamically adjusting gain coefficients based on system states is introduced, leading to the design of a sliding mode controller with proven asymptotic stability. To address non-periodic total disturbances, a super-twisting sliding mode observer is developed, and Lyapunov stability theory is employed to demonstrate system stability and error convergence to zero. A resonant controller is designed to suppress medium- to high-frequency periodic disturbances. For periodic total disturbances, a parameterized feedforward controller based on iterative learning is devised, and an input-shaping filter is introduced to refine the input trajectory. The feedforward control parameters are iteratively updated using a data-driven approach. Experiments are conducted on a differential dual-drive servo system. The nut motor adopts the sliding mode controller with an observer. The screw motor employs the iterative learning feedforward controller. Results show effective suppression of the disturbances. Speed ripple is reduced, and tracking accuracy is significantly improved. The study demonstrates the feasibility and advantage of combining robust control with iterative learning in high-precision servo systems.