Cooperative ETM-Based Adaptive Neural Network Tracking Control for Nonlinear Pure-Feedback MASs: A Special-Shaped Laplacian Matrix Method.

Abstract

This article solves the cooperative adaptive tracking control problem for nonlinear pure-feedback multi-agent systems (MASs). Compared with the previous achievements of adaptive control of pure-feedback MASs, the partial derivative of the nonaffine function may not exist by using decoupling technology. In the controller design framework based on the backstepping technique, the additional state variables are processed using the special properties of the radial basis function neural networks (RBF NNs). A special-shaped Laplacian matrix is proposed to unify the leader gain form in the tracking error design process (the coefficient in the second term of tracking error). Furthermore, an event trigger mechanism (ETM) is introduced to save resources. The constructed controller under the ETM can not only stabilize the system states but also make the tracking error reach a small accuracy. Finally, the simulation results demonstrated the feasibility of the proposed method.