Model Reference Adaptive Iterative Learning Control for a Class of Time-varying Systems

A model reference adaptive iterative learning control algorithm is proposed for a class of first order linear time-varying systems with time-varying high-frequency-gain and inertial parameter in which the boundary input and the boundary output are stable and repeatable in a finite time interval.The model reference adaptive control is combined with iterative learning control.The approach adapts the systems with time-varying uncertain parameters for tracing different reference trajectories.Based on Lyapunov-like function it is proved that the output tracking error converges to zero uniformly in the finite time interval when the trail approaches to infinity.The boundedness of the estimated parameters and the control input in the closed system is proved.The effectiveness of the algorithm is demonstrated by simulations.