Adaptive Event-Triggered Control for Nonlinear Systems with Output Constraint

Abstract

In this paper, the event-triggered adaptive neural network-based tracking control problem is investigated for a class of single-input single-output (SISO) nonlinear systems in strict-feedback form. In the considered systems, there exist unknown functions which are approximated by radial basis function neural networks (RBFNNs). Moreover, the output constraint problem is also taken into account, which is solved by exploiting a barrier Lyapunov function. In order to save resources, the event-triggered control method is developed by using the backstepping technique. Then, the boundedness of all variables appearing in the systems is obtained, as well as the tracking error stays in a small neighborhood of the origin. In the end, a simulation example is employed to show the effective of the proposed scheme.