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.

中文翻译：

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具有传感器饱和度的非线性多智能体系统的基于神经网络的自适应自触发共识

本文旨在为一类具有传感器饱和的非线性多智能体系统提出一种自触发共识控制方案。由于未知非线性动力学的存在，本研究借用神经网络的逼近能力来设计共识控制协议。本文采用神经网络逼近理想控制器，而不是采用神经网络和自适应方法相结合的方法来逼近未知系统动力学。因此，可以有益地引入用于基于事件的采样的神经网络的扩展逼近特性。此外，设计的控制器仅在离散时间更新，这使得系统可以建模为具有脉冲动力学的混合系统。因此，脉冲系统的稳定性理论可以用来分析系统的收敛性。需要说明的是，这是第一次提出有效的基于神经网络的事件触发共识控制算法。此外，本文还考虑了经常遇到的传感器饱和现象。采用凸包法来处理传感器饱和问题，而不是广泛使用的扇形条件法。最后，通过数值例子证明了所提出的基于神经网络的自触发一致性控制算法的性能。而不是广泛使用的扇区条件方法。最后，通过数值例子证明了所提出的基于神经网络的自触发一致性控制算法的性能。而不是广泛使用的扇区条件方法。最后，通过数值例子证明了所提出的基于神经网络的自触发一致性控制算法的性能。