神经适应控制与增强的稳定性和可靠性
IEEE transactions on neural networks and learning systems
|March 18, 2025
概括
这项研究引入了一种新的方法,以确保神经网络 (NN) 控制系统通过在固定的区域内保持训练信号来保持可靠性. 这提高了NN的性能,并确保了稳健的运行,提高了控制系统的可靠性.
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
- 机器学习 机器学习
背景情况:
- 神经网络 (NN) 在控制系统中的性能取决于NN单元的可靠性.
- 保持NN训练信号的紧集条件对于通用近似能力至关重要,但经常被忽视.
- 现有的NN控制研究经常忽视了紧的输入集对于持续的NN功能的重要性.
研究的目的:
- 开发一种方法,确保NN训练信号在运行期间保持在一个固定的区域内.
- 通过满足通用近似定理的紧性条件来保护NN驱动控制单元的功能.
- 加强基于NN的控制方案的稳定性和可靠性,即使在NN表现不佳的情况下.
主要方法:
- 引入了基于约束转换的设计方法,以确保激发信号来自固定的区域.
- 在跟踪误差为零的非对称收时采用衰减缓冲率.
- 开发了一个基于最坏的NN行为来处理表现不佳的故障安全控制策略.
主要成果:
- 拟议的方法确保了NN训练信号的紧性条件,保留了NN的功能.
- 追踪错误异常地汇聚到零,超过了最终均边界 (UUB) 的限制.
- 数字模拟证实了NN驱动控制系统的稳定性和性能的显著改进.
结论:
- 约束转换方法有效地保持了NN训练信号的紧性,提高了控制系统的可靠性.
- 故障保护机制提供了强大的操作,确保系统稳定性,即使在低于最佳的NN性能.
- 该研究表明,在创建可靠和高性能的NN驱动控制系统方面取得了重大进展.
相关概念视频
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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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01:26Feedback control systems
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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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01:07Time-Domain Interpretation of PD Control
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01:24Effects of feedback
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01:17Open and closed-loop control systems
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