Iterative learning control under parameter uncertainty and failures

This paper develops new results on the design of iterative learning control schemes using a repetitive process setting for analysis. Iterative learning control has been developed as a technique for controlling systems which are required to repeat the same operation over a finite duration known as the trial duration, or length, and information from previous executions is used to update the control input for the next one and thereby sequentially improve performance. This paper considers the design of iterative learning control laws for plants modeled by linear discrete systems with uncertain parameters and possible failures. Using a Lyapunov function approach both state and output feedback based schemes are developed.