ILC Coupled with Unscented Kalman Filter Strategy for Trajectory Tracking

This paper employs an iterative learning control (ILC) approach to achieve tracking task for non-repetitive time-varying systems (NTVSs). In industrial production tasks, the actual model of the system is usually unknown, and the nominal model will have a certain degree of error, which brings complexity and difficulty to the design of control task. To tackle this issue, ILC framework incorporates unscented Kalman filter strategy to estimate parameter, which yields the proposed algorithm. The robust convergence property of the scheme is proven in theory based on the design goal. Finally, the advantage of the proposed method is demonstrated by comparison with other methods.