政策代 Q-学习与马科维斯跳跃的线性伊托随机系统及其应用到电力系统
IEEE transactions on cybernetics
|June 12, 2024
概括
本研究介绍了连续时间的伊托随机系统与马科维跳跃的新型在线学习算法,为最佳控制和稳定性分析提供了无模型的方法.
科学领域:
- 控制理论 控制理论
- 随机系统 随机系统 随机系统
- 机器学习 机器学习
背景情况:
- 连续时间线性伊托随机系统与马科维跳跃带来了重要的控制挑战.
- 现有的最佳控制策略通常依赖于依赖模型的方法,限制了它们的适用性.
- 在线政策代 (PI) 和强化学习 (RL) 提供了适应性控制解决方案的潜力.
研究的目的:
- 开发和分析一个在线政策代算法,用于解决连续时间线性伊托随机系统与马科维斯跳跃.
- 通过引入无模型在线强化学习方法来解决依赖模型的离线算法的局限性.
- 严格分析拟议的控制算法的稳定性和收性.
主要方法:
- 开发一种依赖模型的离线政策代 (PI) 算法,以解决代数的里卡蒂方程 (ARE).
- 莱阿普诺夫理论的应用在线PI算法收和控制法可接受性的严格数学分析中.
- 引入了一种新的在线强化学习算法,不需要系统矩阵或过渡概率.
主要成果:
- 线下PI算法证明了趋同,并产生了通过数学分析验证的可接受的控制定律.
- 拟议的在线强化学习算法有效地处理Ito随机系统与马科维亚跳跃,没有先前的系统知识.
- 经过彻底的稳定性分析,在开发的在线控制策略下,确认了闭环系统的稳定性.
结论:
- 该研究成功地介绍了线下和在线算法,以优化控制马科维跳跃的伊托随机系统.
- 新的在线强化学习方法克服了传统方法的数据要求,提高了实际适用性.
- 模拟结果验证了拟议的算法的有效性和稳定性,为先进的自适应控制解决方案铺平了道路.
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