LMI-based gain scheduled ILC design for linear parameter-varying systems

This paper considers the design of iterative learning control laws for systems whose state-space model matrices are functions of a vector of varying parameters. The repetitive process setting is exploited to develop a linear matrix inequality based procedure for computing gain-scheduling feedback and feedforward (learning) controllers. These controllers guarantee acceptable dynamics along the trials and ensure monotonic convergence of the trial-to-trial error dynamics, respectively. A simulation example is given to illustrate the theoretical developments.