Cooperative tracking of uncertain dynamical multi-agent systems: A distributed MRAC approach

In this paper, a distributed model reference adaptive control approach 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. A cooperative adaptive controller, with one adaptive law adjusting the coupling weights and the other adjusting the neural network weights, is designed based on the relative state information of neighboring 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 guaranteed to be 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 example is given to show the efficacy of the proposed method.

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