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

摘要

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.