Robust Accelerated Iterative Learning Control Design for Discrete-Time Systems with Input Saturation

Abstract

This paper considers the iterative learning control design problem for discrete-time systems with uncertain parameters and input saturation. To accelerate convergence of the learning process, a combination of heavy-ball methods from optimization theory and the Lyapunov vector function method for repetitive processes is proposed. An example is given, including a comparison with known results.