Robust repetitive learning control for a class of time-varying nonlinear systems

Repetitive learning control only requires that the learned variables satisfy the repetitive condition like iterative learning control, and does not require repositioning like repetitive control, which avoids the initial repositioning in iterative learning control and extends the applicability of repetitive control. A robust repetitive learning control is proposed for a class of nonlinear systems with non-parametric uncertainties. The robust control part is used to guarantee all the variables in the closed-loop system to be bounded, and the repetitive learning control part can effectively eliminate the tracking error. The stability in the closed-loop and asymptotic convergence of the tracking error are established, respectively, for both partially and fully saturated learning controls. The computer simulation is carried out to demonstrate effectiveness of the proposed control algorithms.