Distributed cooperative adaptive state estimation and system identification for multi-agent systems

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In this study, the authors address the problem of composite cooperative state estimation and system identification for linear multi-agent systems (MASs) under the leader–follower framework. This problem specifies an objective for each follower agent to estimate its local plant state and identify its plant dynamics simultaneously through interacting and communicating with its neighbours. A novel distributed adaptive estimation and identification protocol is proposed, which possesses five important properties to improve existing cooperative approaches: (i) it deals with general linear MASs with a completely unknown agent dynamics, allowing dynamics heterogeneity between the leader and the followers; (ii) it enables simultaneous exact state estimation and accurate system identification of MASs with guaranteed exponential convergence performance; (iii) it utilises only relative measurements from neighbouring agents, but without requiring any knowledge of absolute local plant states/ outputs; (iv) it is fully distributed in the sense that its design and implementation do not involve any global information, including the overall network connectivity and the leader's dynamics; (v) exact system identification can be achieved under a cooperative persistent excitation (PE) condition, which significantly relaxes the classical PE condition. Simulation studies have been conducted to demonstrate the effectiveness of the proposed approach.

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IET Control Theory and Applications