SMC Based Tracking Control With Current Feedback Information for Discrete-Time Repetitive Systems

The article delves into the enhancement of tracking control for discrete-time repetitive systems by integrating sliding mode control (SMC) with iterative learning control (ILC). The proposed method addresses the limitations of traditional ILC by incorporating current feedback information, which improves tracking performance within each cycle and enhances robustness against non-repetitive disturbances. The study presents a novel ILC law that combines feedforward and feedback terms, leveraging the strengths of both SMC and ILC. A comprehensive convergence analysis is conducted using a 2-D system framework, ensuring stability and performance in both iteration and time directions. The effectiveness of the proposed method is validated through comparative simulations, demonstrating faster convergence rates and better handling of disturbances compared to traditional ILC approaches. This work offers a significant advancement in the field of repetitive control systems, providing a robust and efficient solution for high-precision tracking tasks.