Predictive Model-Assisted Iterative Learning Control for Suppressing Unknown Periodic Disturbances

This paper presents a feedforward online predictive model-assisted iterative learning control (PMA-ILC) method, addressing the critical challenge of achieving higher tracking accuracy along with strong task flexibility and disturbance suppression ability. The PMA-ILC framework comprises two key components: 1) a space-dependent oblique projection-based iterative learning control (SOBP-ILC) term for reducing tracking errors effectively without affecting feedback stability, 2) an online model predictive compensation term to dynamically generate an optimal feedforward signal at each sampling instant through iterative computation, significantly enhancing tracking precision and disturbance suppression. By integrating these strategies, the proposed method ensures superior performance and robustness, making it suitable for high-precision tracking applications. Simulation results demonstrate the effectiveness of the proposed method.