Adaptive RBF Neural Network Cooperative Control for High-order Nonlinear Multi-agent Systems With Uncertainties

The leader-following consensus control problem is considered for a class of high-order nonlinear multi-agent systems with external disturbances and uncertain system parameters. The dynamics of systems with high-order integrator coupling unknown nonlinear dynamics and unknown external disturbance, adopts the distributed adaptive radial basis function(RBF)neural network control algorithm, to ensure that the neural network is employed to approximate the unknown nonlinear system functions on line, and eliminate persistent bounded disturbances such as uncertainties affecting stability. First of all, weights of neural network adaptive tuning law was designed, then a kind of adaptive sliding mode control protocol based on RBF neural network was proposed. Using Lyapunov stability theory, the sufficient condition of high-order nonlinear uncertain multi-agent system had leader-following to achieving consensus, and ultimately bounded residual errors was discussed. The results of umerical simulations of homogeneous and heterogeneous multi-agent systems are given to demonstrate the effectiveness of the proposed control methodology.