强化基于学习的容错控制不确定的严格反非线性系统与间歇性执行器故障的基于学习的容错控制
概括
一个新的自适应式故障耐受控制 (FTC) 使用强化学习来管理非线性系统中的执行器故障. 这种方法确保了系统的稳定性,并实现了跟踪目标,尽管存在不确定性和非线性动态.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 非线性系统是非线性系统.
背景情况:
- 耐故障控制 (FTC) 对于在执行器故障下保持系统性能至关重要.
- 具有不确定性的非线性系统对传统的控制策略构成重大挑战.
- 执行器冗余提供了一条提高系统弹性的途径.
研究的目的:
- 为非线性严格反系统开发一种基于强化学习的新型自适应FTC方案.
- 为应对非线性动力学,不确定性和执行器故障所带来的挑战.
- 确保强大的跟踪控制和系统稳定性.
主要方法:
- 开发了一种基于学习的切换函数技术,以根据性能指数管理执行器组.
- 最佳跟踪控制问题 (OTCP) 通过自适应前控制器转化为最佳调节问题.
- 强化学习算法最小化了与汉密尔顿-雅各比-贝尔曼 (HJB) 相关的客观函数,从神经网络 (NN) 近似估计误差.
主要成果:
- 拟议的FTC方案通过自动化执行器组转向有效地减轻故障执行器的影响.
- 强化学习算法成功地将估计错误最小化,而不需要值或政策代.
- 追踪目标得到实现,所有闭环系统信号被证明是有界的.
结论:
- 开发的基于强化学习的自适应FTC方案为具有执行器冗余性的非线性系统提供了强大的解决方案.
- 该方法保证了系统的稳定性和性能,即使存在非线性动态和不确定性.
- 模拟结果验证了理论发现,证明了拟议的控制策略的有效性.
相关概念视频
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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01:10Control Systems
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At the heart...
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01:07Time-Domain Interpretation of PD Control
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01:22Controller Configurations
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01:17Open and closed-loop control systems
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01:24Effects of feedback
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Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
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