对于不确定的非线性系统,基于概率模型的容错控制
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究介绍了使用高斯过程回归来管理非线性系统中的不确定性和故障的两个自适应式容错控制 (FTC) 方法. 这些方法确保系统稳定,尽管存在未知的动态和计算延迟.
科学领域:
- 控制工程 控制工程 控制工程
- 机器学习 机器学习
- 非线性系统分析 非线性系统分析
背景情况:
- 耐故障控制 (FTC) 对于在故障条件下保持系统安全和性能至关重要.
- 在有缺陷的系统中同时解决不确定性和未知的动态对传统的控制方法构成重大挑战.
- 现有的方法经常与实时适应和计算约束的复杂性作斗争.
研究的目的:
- 为具有未知动态的非线性系统提出基于概率模型的新型自适应故障耐受性控制 (FTC) 策略.
- 调查高斯过程 (GP) 回归对离线和在线学习系统动态的有效性,事件触发的场景.
- 分析和减轻实时GP回归预测中固有的计算延迟的影响.
主要方法:
- 开发了两种利用高斯过程 (GP) 回归的自适应FTC方法,用于未知系统动态的概率建模.
- 实现离线学习方法和事件触发的在线数据驱动建模技术.
- 制定四个理论标准,以保证闭环控制系统的概率稳定性.
主要成果:
- 数字模拟证明了拟议的自适应FTC方法在处理系统不确定性和故障方面的有效性.
- 与现有的耐故障控制方法相比,开发的方法显示出具有竞争力的性能.
- 验证在各种操作条件下确保概率稳定的理论标准.
结论:
- 拟议的基于概率模型的自适应FTC方法为具有未知的动态和故障的非线性系统提供了强大的解决方案.
- 尽管有计算方面的考虑,高斯过程回归却为FTC中的数据驱动建模提供了一个强大的工具.
- 该研究强调了解决计算延迟和确保实际FTC应用程序的概率稳定性的重要性.
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