发表机构
Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM); Department of Mathematics and Statistics, University of New Mexico（胡志明市技术大学机械工程学院，越南国立大学胡志明市; 新墨西哥大学数学与统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种事件触发的固定时间积分强化学习框架，用于未知非线性系统的最优控制，通过经验回放避免持续激励条件，并保证实用固定时间稳定性及排除Zeno行为。
AI 中文摘要
本文针对未知非线性系统的最优控制问题，开发了一种事件触发的固定时间积分强化学习框架。首先，利用积分数据驱动的标识器重建未知动力学，随后采用逆最优公式构造固定时间运行成本。接着，推导出满足实用固定时间特性的学习律。先前收集的数据或在有限激励区间内获得的数据被存储在经验回放缓冲区中，并纳入权重更新律，从而避免了在实际操作中难以满足的持续激励条件。为了减少通信和控制更新，引入了事件触发机制。本文证明，在事件触发实现下，闭环系统仍能实现实用固定时间稳定性，同时所提出的触发规则保证了排除Zeno行为。最后，通过一个非线性示例验证了本文开发的理论结果。
英文摘要
This paper develops an event-triggered fixed-time integral reinforcement learning framework for optimal control of unknown nonlinear systems. An integral data-driven identifier is first used to reconstruct the unknown dynamics, after which an inverse-optimal formulation is employed to construct a fixed-time running cost. A learning law satisfying the practical fixed-time property is then derived. Previously collected data, or data obtained during a finite excitation interval, are stored in an experience-replay buffer and incorporated into the weight-update law. This avoids the persistent-excitation condition, which is often difficult to satisfy in practical operation. To reduce communication and control updates, an event-triggered mechanism is introduced. The paper shows that, under the event-triggered implementation, the closed-loop system still achieves practical fixed-time stability, while the proposed triggering rule guarantees the exclusion of Zeno behavior. Finally, a nonlinear example is presented to verify the theoretical results developed in the paper.
