This paper investigates the distributed event-triggered control problem for a class of uncertain pure-feedback nonlinear multi-agent systems (MASs) with polluted feedback. Under the setting of event-triggered control, substantial challenges exist in both control design and stability analysis for systems in more general non-affine pure-feedback forms wherein all state variables are not directly and continuously available or even polluted due to sensor failures, and thus far very limited results are available in literature. In this work, a nominal control strategy under regular state feedback is firstly developed by combining neural network (NN) approximating with dynamic filtering technique, and then a NN-based distributed event-triggered control strategy is proposed by resorting to a novel replacement policy, making the non-differentiability issue arising from event-triggering setting completely circumvented. Besides, the sensor ineffectiveness is accommodated automatically without using fault detection and diagnosis unit or controller reconfiguration. It is shown that all the internal signals are semi-globally uniformly ultimately bounded (SGUUB) with the aid of several vital lemmas, while the outputs of all the subsystems reaching a consensus without infinitely fast execution. Finally, the efficiency of the developed algorithm are verified via numerical simulation.

翻译：本文研究一类具有污染反馈的不确定纯反馈非线性多智能体系统(MASs)的分布式事件触发控制问题。在事件触发控制框架下，对于更一般的非仿射纯反馈形式系统——其中所有状态变量因传感器故障而无法直接连续获取甚至受到污染——而言，控制设计与稳定性分析均面临重大挑战，且目前文献中可用成果极为有限。本文首先结合神经网络(NN)逼近与动态滤波技术，提出常规状态反馈下的标称控制策略；进而通过引入一种新型替换策略，提出基于神经网络的分布式事件触发控制方案，从而完全规避事件触发机制引发的非可微性问题。此外，该方法无需故障检测与诊断单元或控制器重构即可自动适应传感器失效情形。借助若干关键引理，证明所有内部信号均为半全局一致最终有界(SGUUB)，且各子系统的输出无需无限快执行即可达成一致。最后，通过数值仿真验证了所开发算法的有效性。