Gradient-based iterative learning control for decentralised collaborative tracking

Collaborative tracking control of multi-agent sys tems involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. However existing, predominately centralised, control structures are sensitive to communication delays and data drop-out leading to inaccurate tracking. It- erative learning control (ILC) has been applied to increase performance using past experience, but reliance on inverse dynamics has severely reduced robustness to model uncertainty. This paper proposes the first general decentralized iterative learning framework to address this problem, thereby enabling a wide range of existing ILC methodologies to be applied to this area. This framework is illustrated through the derivation of a decentralised gradient based ILC algorithm which ensures convergence to the required reference trajectory, while simultaneously optimising the control input energy. In addition, a novel balancing algorithm is also proposed to distribute the input energy of each agent and hence avoid sub agent overloading.</p