Event-triggered neural network control for a class of uncertain nonlinear systems with input quantization

Abstract

This paper investigates a neural network control issue of a class of uncertain nonlinear systems. An adaptive quantized control strategy is developed such that the input quantization can be achieved using three different kinds of quantizers, and uncertain dynamics of system can be approximated and compensated by neural networks (NNs). Besides, a triggering event is addressed on the basis of a fixed and relative combined threshold strategy for relieving the communication load between the controller and actuator. With such control scheme, all signals of the closed-loop system are bounded and Lyapunov method is applied to prove the uniform ultimate boundedness of the control system. Simulation example is provided for illustrating tracking performance of the proposed control strategy.