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access icon free Distributed model reference adaptive control for cooperative tracking of uncertain dynamical multi-agent systems

© The Institution of Engineering and Technology
Received 27/09/2012, Accepted 10/02/2013, Revised 09/01/2013, Published

In this study, a distributed model reference adaptive control architecture is developed to achieve the cooperative tracking of uncertain dynamical multi-agent systems, where the reference model serves as a virtual leader for the group to track. Two adaptive laws, with one adjusting the coupling weights and the other adjusting the neural network weights, are designed based on the relative state information of neighbouring agents. The proposed controller guarantees that the state of each agent synchronizes to that of the reference model over any undirected connected communication graphs, and all signals in the closed-loop network are uniformly ultimately bounded. In contrast to the existing results, the developed controller can be implemented in a fully distributed manner by each agent without using any global information and the accurate model of each agent. An extension to asymptotic stability is further studied.

Inspec keywords: graph theory; asymptotic stability; nonlinear dynamical systems; uncertain systems; distributed control; closed loop systems; model reference adaptive control systems; multi-agent systems

Other keywords: closed-loop network; cooperative tracking; reference model; uncertain dynamical multi-agent systems; neighbouring agents; neural network; asymptotic stability; virtual leader; relative state information; distributed model reference adaptive control architecture; adaptive laws; connected communication graphs

Subjects: Artificial intelligence (theory); Nonlinear control systems; Self-adjusting control systems; Stability in control theory

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