Adaptive NN consensus tracking control of a class of nonlinear multi-agent systems

This paper proposes a novel robust adaptive consensus tracking control approach for a class of nonlinear multi-agent systems with modeling uncertainties and external disturbances. Radial Basis Function Neural Networks (RBFNNs) are used to approximate the unknown nonlinear function of agent׳s dynamic. Compared with existing NN consensus algorithms of nonlinear multi-agent systems, the proposed consensus control method only needs a small number of adjustable parameters, thus the online computation burden is greatly alleviated. In addition, by online updating the estimation of the NN approximation errors, the proposed consensus control approach can enhance the system robustness against modeling uncertainties. It is proven that all the signals of the multi-agent system are uniformly bounded and the consensus tracking errors converge to a small neighborhood of zero. A simulation is carried out to demonstrate the effectiveness of the proposed control method.