动态建模和控制设计的统一框架,使用深度学习与稳定性侧面信息进行深度学习.
IEEE transactions on neural networks and learning systems
|March 20, 2025
概括
本研究引入了一个深度学习框架,通过同时学习动态,控制器和Lyapunov函数来保证系统稳定性. 这种方法增强了数据驱动的控制,并为现实世界的应用提供了强大的理论保证.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 动态系统 动态系统
背景情况:
- 传统的数据驱动方法往往无法结合必要的控制特性,如稳定性.
- 确保稳定性对于反控制系统的可靠性能至关重要.
研究的目的:
- 为动态建模和控制设计开发一个统一的深度学习框架,明确保证稳定性.
- 将先前的稳定性知识纳入基于神经网络的控制策略中.
主要方法:
- 一种新的神经网络 (NN) 方法,可以同时学习系统动态,稳定反控制器和利亚普诺夫函数.
- 嵌入稳定性作为深度学习框架中的核心属性.
主要成果:
- 拟议的框架明确保证了学习模型的稳定性.
- 在各种控制问题上表现出有效性,包括安全控制和增益控制.
- 在各种场景中,在稳定性和控制性能方面展示了改进.
结论:
- 开发的深度学习框架提供了基于数据的模型,具有强大的控制理论保障.
- 这种方法显著提高了学习模型在实际控制应用中的实用性.
- 这些方法适用于没有控制设计的建模,并且是开源的.
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