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通过并行复合政策代方案对具有未知动态的非线性系统进行基于强化学习的模糊控制
IEEE transactions on cybernetics
|February 25, 2026
概括
本研究介绍了一种新的并行复合政策代 (PCPI) 算法,用于非线性系统中基于强化学习 (RL) 的模糊控制. PCPI算法克服了传统方法的局限性,即使在未知系统动态的情况下,也可以实现高效的控制.
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
- 模糊逻辑系统 模糊逻辑系统
背景情况:
- 强化学习 (RL) 和模糊控制对于非线性系统至关重要.
- 传统的政策代 (PI) 和价值代 (VI) 方法面临诸如初始稳定政策和持续刺激 (PE) 条件等挑战.
- 解决复杂的非线性系统的模糊代数里卡蒂方程 (FARE) 很难用传统的方法.
研究的目的:
- 为基于RL的模糊控制开发一种新的并行复合政策代 (PCPI) 算法.
- 解决现有的PI/VI算法的局限性,包括需要初始稳定控制政策和PE条件.
- 在具有未知动态的非线性系统中解决复杂的模糊代数里卡蒂方程 (FARE).
主要方法:
- 提出了一种新的PCPI算法,结合了适应参数,以消除对初始稳定控制政策的需求.
- 为难以获取动态信息的系统引入了一个在线的,无模型的PCPI变体.
- 通过利用在线数据,PE条件被放松到初始激发 (IE) 条件,算法按照模糊规则并行运行.
主要成果:
- 拟议的PCPI算法有效地减轻了基于RL的传统模糊控制方法的缺点.
- 适应性参数消除了对初始稳定控制政策的要求.
- 在线,无模型的PCPI将PE条件放松到IE,增强对具有未知动态的系统的适用性.
结论:
- 开发的PCPI算法为基于RL的非线性系统的模糊控制提供了有效的解决方案.
- 算法的放松激发条件和无模型运行的能力提高了它的实际应用性.
- 在机器人臂和主动悬挂系统上的实验验证证证了算法的有效性.
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