College of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China.
School of Information Science and Engineering, Shandong Normal University, Jinan 250014, China.
Neural Netw. 2022 Oct;154:43-55. doi: 10.1016/j.neunet.2022.06.039. Epub 2022 Jun 30.
In this paper, an event-triggered control scheme with periodic characteristic is developed for nonlinear discrete-time systems under an actor-critic architecture of reinforcement learning (RL). The periodic event-triggered mechanism (ETM) is constructed to decide whether the sampling data are delivered to controllers or not. Meanwhile, the controller is updated only when the event-triggered condition deviates from a prescribed threshold. Compared with traditional continuous ETMs, the proposed periodic ETM can guarantee a minimal lower bound of the inter-event intervals and avoid sampling calculation point-to-point, which means that the partial communication resources can be efficiently economized. The critic and actor neural networks (NNs), consisting of radial basis function neural networks (RBFNNs), aim to approximate the unknown long-term performance index function and the ideal event-triggered controller, respectively. A rigorous stability analysis based on the Lyapunov difference method is provided to substantiate that the closed-loop system can be stabilized. All error signals of the closed-loop system are uniformly ultimately bounded (UUB) under the guidance of the proposed control scheme. Finally, two simulation examples are given to validate the effectiveness of the control design.
在强化学习（RL）的演员-评论家架构下，为非线性离散时间系统开发了具有周期性特征的事件触发控制方案。构建周期性事件触发机制（ETM）以决定是否将采样数据传输到控制器。同时，仅当事件触发条件偏离规定阈值时，才会更新控制器。与传统的连续 ETM 相比，所提出的周期性 ETM 可以保证最小的事件间隔下界，并避免采样计算点对点，这意味着可以有效地节省部分通信资源。由径向基函数神经网络（RBFNN）组成的评论家神经网络和演员神经网络，旨在分别逼近未知的长期性能指标函数和理想的事件触发控制器。基于李雅普诺夫差分方法的严格稳定性分析证明了闭环系统可以稳定。在提出的控制方案的指导下，闭环系统的所有误差信号都是一致有界的（UUB）。最后，给出了两个仿真示例，以验证控制设计的有效性。
