Adaptive cooperative tracking control for a class of high-order nonaffine nonlinear multi-agent systems

This paper investigated the adaptive cooperative tracking control for a class of high-order nonaffine nonlinear multi-agent systems. An effective distributed adaptive control strategy was proposed only based on the local state information of the network neighbor agents. The main contribution was a novel dynamics transformation that converted the nonaffine system into an affine system through the mean value theorem. Radial Basis Function Neural Networks (RBFNNs) were used to approximate the unknown nonlinear function of the multi-agent systems, and a Lyapunov function was designed for the robust adaptive synchronization control protocol. At the end, simulation results demonstrated the effectiveness of the proposed control method.