Genetic Algorithms in Iterative Learning Control

Recently it was explored by the authors whether or not a Genetic Algorithm (GA) based approach can be used in the context of norm-optimal Iterative Learning Control 9ILC). It turns out the answer is positive for both linear and nonlinear plant models. The strength of this method is that it can cope with nonlinearities and hard constraints in the problem definition, whereas, most of the existing algorithms would fail. Simulations are used to illustrate the performance of this new approach and give good results in terms of convergence speed and tracking of the reference signal, regardless of the nature of the plant under investigation. It is in fact shown in this paper that under suitable assumptions, the proposed GA-ILC approach will result in monotonic exponential convergence, which is a very strong property of an ILC-algorithm. The proposed approach also involves the design of a low-pass FIR filter which successfully smooths the "noisy" input signal naturally generated by the GA method