强化基于学习的H∞ 控制2D马尔科夫跳跃Roesser系统与最佳干扰减弱
IEEE transactions on neural networks and learning systems
|November 6, 2024
概括
本研究介绍了一种无模型的强化学习方法,用于在二维马尔科夫跳跃罗塞系统中控制H∞. 它使用在线数据优化干扰减弱和控制策略,优于传统的离线方法.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 系统理论 系统理论
背景情况:
- H∞控制对于在干扰下稳健的系统性能至关重要.
- 离散时间二维马尔科夫跳跃罗塞系统 (2D MJRSs) 由于其空间和时间动态,存在独特的控制挑战.
- 现有的2D MJRS的H∞控制方法通常依赖于完整的系统知识和离线计算.
研究的目的:
- 开发一种无模型的强化学习 (RL) 算法,用于对2D MJRS 的 H∞ 控制.
- 为了优化干扰减弱水平并在线找到初始稳定控制政策.
- 解决现有的离线和依赖模型的控制策略的局限性.
主要方法:
- 一个全面的无模型RL算法旨在学习最佳的H∞控制策略.
- 在线水平和垂直数据沿着系统轨迹被利用.
- 线性矩阵不等式 (LMIs) 和数据驱动的并行值代 (VI) 算法用于优化和政策搜索.
主要成果:
- 算法成功确定了最佳的干扰减弱水平和初始稳定控制策略.
- 闭环系统的RL算法的收和非对称平均平方稳定性得到了数学认证.
- 模拟结果验证了拟议的在线无模型方法的有效性.
结论:
- 拟议的无模型RL框架为2DMJRS的H∞控制提供了有效的在线解决方案.
- 这种方法克服了对完整系统动态和离线计算的需求.
- 该方法在复杂的二维系统中表现出卓越的性能和稳定性.
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