Decentralised collaborative and formation iterative learning control for multi-agent systems

Collaborative tracking 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 each agents. However, focus has been on centralized structures, and existing solutions typically have limited convergence rates and robustness properties. This paper addresses current limitations by developing a powerful decentralised framework which 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.