Iterative Learning Control of a Multiagent System under Random Perturbations

Abstract

A multiagent system in which each of the agents is described by a linear discrete-time model with random perturbations (external random disturbances affecting the plant and measurement noises) is considered. Networked modifications of iterative learning control laws based on minimizing the deviations from a reference model and also based on the theory of stochastic stability of repetitive processes using the divergent method of vector Lyapunov functions are proposed. These modifications are compared with each other by an illustrative example of iterative learning control for a group of gantry robots.