College of Westa, Southwest University, Chongqing, 400715, China.
College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.
Neural Netw. 2023 Jun;163:341-353. doi: 10.1016/j.neunet.2023.04.008. Epub 2023 Apr 19.
The current investigation aims at the optimal control problem for discrete-time nonstrict-feedback nonlinear systems by invoking the reinforcement learning-based backstepping technique and neural networks. The dynamic-event-triggered control strategy introduced in this paper can alleviate the communication frequency between the actuator and controller. Based on the reinforcement learning strategy, actor-critic neural networks are employed to implement the n-order backstepping framework. Then, a neural network weight-updated algorithm is developed to minimize the computational burden and avoid the local optimal problem. Furthermore, a novel dynamic-event-triggered strategy is introduced, which can remarkably outperform the previously studied static-event-triggered strategy. Moreover, combined with the Lyapunov stability theory, all signals in the closed-loop system are strictly proven to be semiglobal uniformly ultimately bounded. Finally, the practicality of the offered control algorithms is further elucidated by the numerical simulation examples.
当前的研究旨在通过强化学习的回溯技术和神经网络来解决离散时间非严格反馈非线性系统的最优控制问题。本文提出的动态事件触发控制策略可以减轻执行器和控制器之间的通信频率。基于强化学习策略，采用了演员-评论家神经网络来实现 n 阶回溯框架。然后，开发了一种神经网络权重更新算法，以最小化计算负担并避免局部最优问题。此外，引入了一种新的动态事件触发策略，该策略可以显著优于先前研究的静态事件触发策略。此外，结合 Lyapunov 稳定性理论，严格证明了闭环系统中的所有信号都是半全局一致最终有界的。最后，通过数值仿真示例进一步阐明了所提出的控制算法的实用性。
