Repetitive Process Based Higher-Order Iterative Learning Control Law Design

This paper uses the repetitive process setting to develop new results on the design of higher-order learning control laws. The basic idea of higher-order iterative learning control is to use information from a finite number of previous trials instead of just the last trial to update the control input for application on the subsequent trial, with the primary objective of improving the error convergence performance. The sufficient conditions ensuring the convergence of the resulting control scheme are established with repetitive process setting and utilizing non-unit memory repetitive process models. Also, the corresponding control law gains are derived from a set of linear matrix inequality constraints. Finally, an example demonstrates the properties of the new design.