Finite-time Command Filtered Based Adaptive Event-triggered Neural Control for Nonlinear Systems

This paper addresses the problem of adaptive event-triggered neural network control for a class of strict-feedback nonlinear systems. First, the radial basis function neural network is utilized to approximate unknown nonlinear functions and the command filter technique is introduced to address the problem of explosion of complexity in the backstepping design. Then, by using backstepping design, radial basis function neural network and event-triggered mechanism, an adaptive event-triggered tracking controller is designed. The designed controller ensures that the tracking error converges to an arbitrarily small neighborhood of zero and reduces the communication burden. Finally, the numerical example is presented to demonstrate effectiveness of the proposed control scheme.