It poses technical difficulty to achieve stable tracking even for single mismatched nonlinear strict-feedback systems when intermittent state feedback is utilized. The underlying problem becomes even more complicated if such systems are networked with directed communication and state-triggering setting. In this work, we present a fully distributed neuroadaptive tracking control scheme for multiple agent systems in strict-feedback form using triggered state from the agent itself and the triggered states from the neighbor agents. To circumvent the non-differentiability of virtual controllers stemming from state-triggering, we first develop a distributed continuous control scheme under regular state feedback, upon which we construct the distributed event-triggered control scheme by replacing the states in the preceding scheme with the triggered ones. Several useful lemmas are introduced to allow the stability condition to be established with such replacement, ensuring that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the output tracking error converging to a residual set around zero. Besides, with proper choices of the design parameters, the tracking performance in the mean square sense can be improved. Numerical simulation verifies the benefits and efficiency of the proposed method.
翻译:在使用间歇状态反馈时,即使单一不匹配的非线性严格反馈系统也难以实现稳定跟踪,即使单一不匹配的非线性严格反馈系统也难以实现稳定跟踪。如果这种系统以定向通信和州触发设置联网,其根本问题就变得更加复杂。在这项工作中,我们提出了一个完全分布式神经适应性跟踪控制系统,使用代理人本身的触发状态和邻国代理人的触发状态,以严格反馈形式,对多个代理系统实施完全分布式神经适应性跟踪控制机制。为避免因州触发而导致的虚拟控制器无法区分,我们首先在定期状态反馈下开发一个分布式连续控制机制,我们据此构建分布式事件触发控制机制,用触发的系统取代前一个系统中的状态。引入了几个有用的 Lemmas,允许以这种替换来建立稳定性条件,确保所有闭环信号最终以半全球统一的方式连接(SGUUBUB),将输出跟踪错误集中到零左右的残余值。此外,除了对设计参数的适当选择外,还可以改进中正方意义的跟踪功能。