In this article, a decentralized event-triggered (ET) regulation problem is tackled for networked interconnected systems (NISs) with control constraints and unmatched interference. Foremost, the decentralized regulation issue is converted into the optimal control problems for the associated auxiliary subsystem. In confronting the unavailability of system dynamics, the utilization of generalized fuzzy hyperbolic models-assisted identifier provides a novel perspective to devise the efficacious control policy for the constrained NISs. For the sake of mitigating the communication workload, a new dual threshold functions-based adaptive ET scheme (DTAETS) is put forward by incorporating the current data and latest ET signal. Moreover, we present a data-driven decentralized reinforcement learning algorithm to acquire the solution of DTAETS-boosted Hamilton–Jacobi–Isaacs equation. Then, the uniformly ultimately bounded stability of auxiliary subsystem and the weight estimation error is assured. Ultimately, a numeral experiment is conducted to substantiate the validity of the theoretical results.

中文翻译：

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使用广义模糊双曲模型的数据驱动的网络互连系统的分散学习调节


在本文中，解决了具有控制约束和无与伦比的干扰的网络互连系统（NIS）的去中心化事件触发（ET）调节问题。首先，将分散调节问题转化为相关辅助子系统的最优控制问题。面对系统动力学的不可用性，广义模糊双曲模型辅助识别器的使用为设计受限NIS的有效控制策略提供了新的视角。为了减轻通信工作量，结合当前数据和最新的ET信号，提出了一种新的基于双阈值函数的自适应ET方案（DTAETS）。此外，我们提出了一种数据驱动的分散强化学习算法来获取 DTAETS 增强的 Hamilton-Jacobi-Isaacs 方程的解。然后，保证了辅助子系统的一致最终有界稳定性和权重估计误差。最后通过数值实验验证了理论结果的有效性。