基于强化学习的分散安全控制,用于受限制的相互连接的非线性安全关键系统
Chunbin Qin1, Yinliang Wu1, Jishi Zhang2
1School of Artificial Intelligence, Henan University, Zhengzhou 450046, China.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
本研究引入了一种新的分散安全控制 (DSC) 方法,用于使用强化学习的非线性系统. 这种方法确保了系统稳定性和在复杂的约束条件下最佳的政策学习.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 非线性系统是非线性系统.
背景情况:
- 分散安全控制 (DSC) 对相互连接的非线性安全关键系统至关重要.
- 现有的方法经常与不对称的输入和安全约束作斗争.
- 强化学习 (RL) 为适应性控制策略提供了一个有希望的途径.
研究的目的:
- 为受限制的相互连接的非线性安全关键系统制定新的DSC战略.
- 为应对不对称输入和安全约束所带来的挑战.
- 确保稳定的系统运行和安全领域内的最佳政策学习.
主要方法:
- 在辅助子系统中为执行器估计构建了改进的性能功能.
- 将安全性和不对称输入约束的去中心化控制问题转化为仅使用障碍函数的不对称输入约束的同等问题.
- 应用利亚普诺夫理论来分析系统稳定性.
主要成果:
- 拟议的方法确保安全关键系统在其安全的全球领域内运行并学习最佳的DSC政策.
- 最优的控制策略保证了整个系统的统一最终界限 (UUB).
- 闭环辅助子系统中的所有信号都被证明是统一的最终边界.
结论:
- 开发的分散安全控制策略有效地处理非线性系统中的不对称输入和安全约束.
- 基于强化学习的方法确保了系统稳定性和最佳的控制政策趋同.
- 模拟结果验证了拟议方法在安全关键应用中的实际有效性.
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