Prescribed Performance Adaptive Control for Nonlinear Systems with Unmodeled Dynamics via Event-triggered

prescribed performance neural network adaptive control scheme based on event-triggered mechanism is presented for a class of strict-feedback nonlinear systems with un-modeled dynamics. First, in order to improve the performance of system, finite-time performance function is introduced. The unknown nonlinear functions are approximated by radial basis function (RBF) neural networks. Then, an adaptive event-triggered controller based on back-stepping is designed, which guarantees that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Meanwhile, the tracking error can converge to a prescribed range, and the Zeno-behavior can be avoided. Finally, simulation verifies the effectiveness of the proposed method.