A decentralised iterative learning control framework for collaborative tracking

Abstract

Collaborative tracking control involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. Decentralised iterative learning control schemes have demonstrated highly accurate collaborative tracking by using past experience gained over repeated attempts at the task. However they impose highly restrictive constraints on the system dynamics, and their reliance on inverse dynamics has degraded their robustness to model uncertainty.
This paper proposes the first general decentralised iterative learning framework to address this problem, thereby enabling a wide range of existing iterative learning control methodologies to be applied in a decentralised manner to collaborative subsystems. This framework is illustrated through the derivation of a variety of new decentralised iterative learning control algorithms which balance
collaborative tracking performance with optimisation of a general objective function. The framework is illustrated by application to wearable stroke rehabilitation technology in which each subsystem is a muscle artificially activated by electrical stimulation. These verify the framework’s simplified design and reduced hardware and communication overheads.

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Identifiers

ORCID for Shangcheng Chen: orcid.org/0000-0001-8032-2779

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