Model-Based Adaptive Event-Triggered Tracking Control of Discrete-Time Nonlinear Systems Subject to Strict-Feedback Form

The consumption of communication resources is an essential issue when control tasks are implemented in a wireless network environment. In order to lessen the network resources, a novel model-based (MB) adaptive event-triggered (ET) tracking control scheme is put forward in this article for strict-feedback discrete-time nonlinear systems. In this article, an event-based adaptive model is constructed by the combination of an
$n$
-step-ahead predictor and event-sampled neural networks. Then, the adaptive neural model is used for designing the MB ET controller. Besides, a modified ET condition is constructed without any delay. By combining a decoupled backstepping framework, the reverse Lyapunov stability technology is developed to verify the ultimate boundedness of all closed-loop signals and the convergence of the tracking error. Compared to the zero-order hold method, which keeps transmitted state signals unchanged in the interevent period, the proposed MB ET control scheme can keep the real-time update of state signals transmitted to the controller. It means that the triggering error will be smaller by the MB trigger mechanism, thereby improving the event-based tracking performance and further saving communication resources. Comparisons of simulation results are given to verify the effectiveness of the proposed control scheme.