Neural-Network Based Adaptive Self-Triggered Consensus of Nonlinear Multi-Agent Systems With Sensor Saturation

This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination of neural network and adaptive method to approximate the unknown system dynamics. Thus, the extended approximation property of neural network for event-based sampling can be beneficially introduced. Moreover, the designed controller only updates at discrete time, which enables that the system can be modeled as a hybrid system with impulsive dynamics. Thus, the stability theory of impulsive systems can be used to analyze the convergence of the system. It should be noted that this is the first time to propose an effective event-triggered consensus control algorithm based on neural network. Furthermore, this paper also considers a frequently encountered phenomenon of sensor saturation. The convex hull method is adopted to deal with sensor saturation problem, instead of the widely used sector condition method. Finally, the performance of the proposed neural-network based self-triggered consensus control algorithm is demonstrated by the numerical examples.