In this paper, we propose a novel event-triggered near-optimal control for nonlinear continuoustime systems. The receding horizon principle is utilized to improve the system robustness and obtain better dynamic control performance. In the proposed structure, we first decompose the infinite horizon optimal control into a series of finite horizon optimal problems. Then a learning strategy is adopted, in which an actor network is employed to approximate the cost function and an critic network is used to learn the optimal control law in each finite horizon. Furthermore, in order to reduce the computational cost and transmission cost, an event-triggered strategy is applied. We design an adaptive trigger condition, so that the signal transmissions and controller updates are conducted in an aperiodic way. Detailed stability analysis shows that the nonlinear system with the developed event-triggered optimal control policy is asymptotically stable. Simulation results on a single-link robot arm with different noise types have demonstrated the effectiveness of the proposed method.

在本文中，我们为非线性连续时间系统提出了一种新颖的事件触发近最优控制。后退水平原理用于提高系统的鲁棒性并获得更好的动态控制性能。在提出的结构中，我们首先将无限层最优控制分解为一系列有限层最优问题。然后采用一种学习策略，在该策略中，使用参与者网络来近似成本函数，并使用评论者网络来学习每个有限范围内的最优控制律。此外，为了减少计算成本和传输成本，应用了事件触发策略。我们设计了一个自适应触发条件，以便以非周期性的方式进行信号传输和控制器更新。详细的稳定性分析表明，具有事件触发的最优控制策略的非线性系统是渐近稳定的。在具有不同噪声类型的单链接机器人手臂上的仿真结果证明了该方法的有效性。