Model-Based Event-Triggered Control of a Class of Strict-Feedback Nonlinear Discrete-Time Systems

In this paper, we present a novel model-based event triggered adaptive neural network (NN) tracking control scheme for a class of strict-feedback discrete-time nonlinear systems. By using the event sampled NN to approximate the uncertainties of the system, an NN-based adaptive model is first constructed and then the controller is designed by means of backstepping. In this scheme, the feedback state signals and NN weights signals are updated in an aperiodic manner at the triggering instants which is depending on the adaptive event trigger condition. Specially, the adaptive event trigger condition consists of the NN estimated weights and some design parameters to determine the trigger instants. The Lyapunov method and the decoupled backstepping technique is used to verify the ultimate boundedness of the NN weight error and the system signals. Simulation results are given to verify the effectiveness of the control scheme.