Decentralised Collaborative and Formation Iterative Learning Control for Multi-Agent Systems

Abstract

Collaborative tracking control and formation control are common approaches in which multiple agents work together to perform a global objective. They are increasingly used in a diverse range of applications, however few controllers simultaneously address both tasks. To improve performance of repeated tasks, iterative learning control (ILC) has been independently applied to both methodologies. However, focus has been on centralized structures, and existing solutions typically have limited convergence rates and robustness properties.This paper addresses these limitations by developing a powerful decentralised ILC framework that unites both collaborative tracking and formation control objectives. It enables broad classes of ILC algorithm to be derived with well-defined convergence rates, optimal tracking solutions, and transparent robustness properties. The framework is illustrated through derivation of three new ILC updates: inverse, gradient and norm optimal ILC. Convergence analysis for the proposed framework is also given.