In this paper, an event-triggered reinforcement learning-based met-hod is developed for model-based optimal synchronization control of multiple Euler-Lagrange systems (MELSs) under a directed graph. The strategy of event-triggered optimal control is deduced through the establishment of Hamilton-Jacobi-Bellman (HJB) equation and the triggering condition is then proposed. Event-triggered policy iteration (PI) algorithm is then borrowed from reinforcement learning algorithms to find the optimal solution. One neural network is used to represent the value function to find the analytical solution of the event-triggered HJB equation, weights of which are updated aperiodically. It is proved that both the synchronization error and the weight estimation error are uniformly ultimately bounded (UUB). The Zeno behavior is also excluded in this research. Finally, an example of multiple 2-DOF prototype manipulators is shown to validate the effectiveness of our method.

中文翻译：

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通过事件触发的强化学习对多个欧拉-拉格朗日系统进行最优同步控制

在本文中，针对有向图下的多个Euler-Lagrange系统（MELSs）基于模型的最优同步控制，开发了一种基于事件触发的强化学习的方法。通过建立Hamilton-Jacobi-Bellman（HJB）方程，推导了事件触发的最优控制策略，并提出了触发条件。然后从强化学习算法中借鉴事件触发的策略迭代（PI）算法，以找到最佳解决方案。一个神经网络用于表示值函数，以找到事件触发的HJB方程的解析解，其权重会不定期更新。事实证明，同步误差和权重估计误差都是统一的最终有界（UUB）。这项研究也排除了芝诺行为。