基于学习的建模和预测控制未知非线性系统的稳定性保证
概括
本研究为未知的非线性系统引入了一种基于学习的稳定控制方法. 它通过解决学习动力学稳定性和建模错误来确保可靠的系统控制来确保安全.
科学领域:
- 控制理论 控制理论
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 控制未知的非线性系统由于其固有的复杂性和潜在的不稳定性而带来了重大挑战.
- 确保控制系统的安全性和稳定性,特别是那些采用机器学习的控制系统,对于现实世界的应用至关重要.
研究的目的:
- 开发一种基于学习的控制方案,保证未知非线性系统的稳定性.
- 解决模拟不匹配的挑战,并在实际场景中确保安全运行.
主要方法:
- 利用库普曼理论对未知的非线性动力学进行线性表示.
- 采用深度学习来近似库普曼操作员嵌入函数.
- 集成的稳定性和利普希茨约束对于强大的模型学习.
- 采用了强大的预测控制方案,以减轻建模错误.
主要成果:
- 成功学习了一个稳定的模型来预测和控制未知的非线性系统.
- 通过强大的预测控制,证明了模拟不匹配效应的消除.
- 实现了未知非线性系统的稳定.
结论:
- 提出的基于学习的控制方案有效地确保了未知的非线性系统的稳定性和安全性.
- 该方法在绑定的太空机器人 (TSR) 上得到了验证,证明了其实际适用性.
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