In this article, a novel online decentralized event-triggered control scheme is proposed for a class of nonlinear interconnected large-scale systems subject to unknown internal system dynamics and interconnected terms. First, by designing a neural network-based identifier, the unknown internal dynamics of the interconnected systems is reconstructed. Then, the adaptive critic design method is used to learn the approximate optimal control policies in the context of event-triggered mechanism. Specifically, the event-based control processes of different subsystems are independent, asynchronous, and decentralized. That is, the decentralized event-triggering conditions and the controllers only rely on the local state information of the corresponding subsystems, which avoids the transmissions of the state information between the subsystems over the wireless communication networks. Then, with the help of Lyapunov's theorem, the states of the developed closed-loop control system and the critic weight estimation errors are proved to be uniformly ultimately bounded. Finally, the effectiveness and applicability of the event-based control method are verified by an illustrative numerical example and a practical example.

中文翻译：

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无线通信网络上的未知大型系统的分散事件触发的在线自适应控制。

在本文中，针对一类非线性互连的大型系统，提出了一种新颖的在线分散事件触发控制方案，该系统受未知的内部系统动力学和互连项约束。首先，通过设计基于神经网络的标识符，可以重建互连系统的未知内部动力学。然后，采用自适应批评家设计方法来学习事件触发机制下的近似最优控制策略。具体来说，不同子系统的基于事件的控制过程是独立的，异步的和分散的。也就是说，分散的事件触发条件和控制器仅依赖于相应子系统的本地状态信息，这避免了状态信息在子系统之间通过无线通信网络的传输。然后，在李雅普诺夫定理的帮助下，已开发的闭环控制系统的状态和批评者权重估计误差被证明是最终一致的。最后，通过一个示例性的数值示例和一个实际示例验证了基于事件的控制方法的有效性和适用性。