Event-triggered adaptive neural constraint output control for switched nonlinear system

In this paper, an event-triggered adaptive neural control issue is addressed for a class of switched unknown strict-feedback nonlinear system under constraint output. To deal with the unknown nonlinear system, the radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions. Under adaptive backstepping technique, associated with barrier Lyapunov function method, an event-triggered controller is designed to ensure that the system's output signal follows a given reference signal. meanwhile, the system output signal meets the asymmetric constraint requirement. The proposed control strategy is guaranteed to solve the presented problem. Finally, a simulation example is presented to demonstrate the efficacy of the proposed scheme.