On Iterative Learning in Multi-agent Systems Coordination and Control

Abstract

Multi-agent systems coordination and control problem has been extensively studied by the control community as it has wide applications in practice. For example, the formation control problem, search and rescue by multiple aerial vehicles, synchronization of coupled oscillators, sensor fusion, distributed optimization, economic dispatch problem in power systems, etc. Meanwhile, many industry processes require both repetitive executions and coordination among several independent entities. This observation motivates the research of multi-agent coordination from iterative learning control (ILC) perspective. To study multi-agent coordination by ILC, an extra dimension, the iteration domain, is introduced to the problem. In addition, the inherent nature of multi-agent systems such as heterogeneity, information sharing, sparse and intermittent communication, imperfect initial conditions increases the complexity of the problem. Due to these factors, the controller design becomes a challenging problem. This thesis aims at designing learning controllers under various coordination conditions, and analyzing the convergence properties.