基于模糊增强学习的线性系统的控制与输入和
Kainan Liu1, Xiaojun Ban1, Shengkun Xie2
1Harbin Institute of Technology, Harbin, China.
ISA transactions
|January 25, 2025
概括
这项研究引入了一种新型的对输入和线性系统的最佳控制方法,它结合了Takagi-Sugeno模糊模型和强化学习. 该方法提高了可解释性,并处理实际控制应用的和挑战.
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:
- 输入和是线性控制系统中常见的挑战,限制了性能和稳定性.
- 传统的强化学习 (RL) 方法往往缺乏可解释性,并与不可区分的和边界作斗争.
- 塔卡吉-苏格诺 (T-S) 模糊模型为近似复杂函数和结合先前知识提供了一个框架.
研究的目的:
- 为具有输入和的线性系统开发可解释和有效的最佳控制策略.
- 将TS模糊模型与RL集成,以克服传统基于神经网络的RL的局限性.
- 解决与和点不差异性和依赖RL未来状态的未来状态相关的挑战.
主要方法:
- 使用TS模糊模型对值函数和最佳控制规律的近似计算.
- 使用细分函数来近似和导数特征,管理不可差异性.
- 实施一种用于改善政策的新型梯度识别方法,而不依赖于下一个时间步骤的状态变量.
主要成果:
- 拟议的TS模糊模型和RL混合方法成功地为具有输入和的系统生成了最佳控制规律.
- 与基于标准神经网络的RL相比,该方法显示了增强的解释性.
- 计算机模拟验证了开发的控制策略的有效性,最佳性和收性.
结论:
- 这项研究为输入和系统的最佳控制提供了一个强大而实用的框架.
- TS模糊模型和RL的协同作用为现实世界的控制工程问题提供了多功能解决方案.
- 这种创新方法为推进控制理论及其应用做出了重大贡献.
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