NN-Based Adaptive Tracking Control of Discrete-Time Nonlinear Systems With Actuator Saturation and Event-Triggering Protocol

Abstract

In this article, a novel neural network (NN)-based adaptive event-triggered control scheme is developed for a class of uncertain discrete-time strict-feedback nonlinear systems with asymmetric actuator saturation. To deal with the asymmetric input saturation, a unified smooth nonlinear function is constructed to effectively characterize the limitations between the control signal and the actuator. Subsequently, the novel backstepping design process, instead of the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -step-ahead predictor, is developed to design the stable event-triggered adaptive tracking controller by combining one neural approximator. Especially, a modified event-triggering condition is equipped into the designed controller to increase the number of triggering events at the transient-state stage. The proposed control scheme can not only achieve the good tracking performance with the avoidance of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -step time delays and the improvement of the transient-state performance but also alleviate the transmission burden of the network resource, and eliminate the effect of the asymmetric actuator saturation. Numerical simulation results demonstrate the effectiveness of the control scheme proposed in this article.