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Distributed Adaptive Control for a Class of Heterogeneous Nonlinear Multi-agent Systems With Nonidentical Dimensions


 A novel distributed adaptive control scheme based on radial basis neural network (RBFNN) is proposed for a class of leaderless heterogeneous nonlinear multi-agent system with same and different dimensions. Considering the nonidentical dynamic differential equation with different dimensions of each agent, the definition with similar parameters of each agent is addressed, and then the distributed control consisting of a series of similar matrices or vectors can make that the states of follower agents track the leader's dynamic behaviors. Through the coupling weight adaptive laws and the feedback control of neural network weights, it is ensured that all signals are uniformly ultimately bounded. Finally, two simulation examples are utilized to verify the effectiveness of the proposed control design method.