具有有限数据的状态过渡学习,用于安全控制切换非线性系统
Chenchen Fan1, Kai-Fung Chu2, Xiaomei Wang3
1Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China.
概括
本研究介绍了基于学习的交换非线性系统的新型控制策略,即使数据有限,也能确保安全性和稳定性. 它利用神经控制屏障和Lyapunov函数在任意切换下提供强大的性能.
科学领域:
- 控制理论 控制理论
- 非线性系统是非线性系统.
- 机器学习 机器学习
背景情况:
- 现实世界的系统表现出交换动态,需要像交换系统这样的模型.
- 将安全约束整合到交换系统的控制合成中是一个重大挑战.
- 有限的系统数据使交换非线性系统的控制设计复杂化.
研究的目的:
- 根据任意切换规律,为切换非线性系统开发基于学习的控制策略.
- 用最少的数据确保系统稳定性和维护安全约束.
- 为了提高控制性能,利用切换特性.
主要方法:
- 采用控制屏障函数 (CBF) 方法和莱阿普诺夫安全和稳定理论.
- 开发了一个神经控制屏障函数和一个神经利亚普诺夫函数.
- 用一种状态过渡学习方法来进行控制政策的合成.
- 纳入政策损失和政策学习的前状态估计.
主要成果:
- 成功合成了用于交换非线性系统的安全控制器.
- 证明了维持稳定性和安全约束的能力.
- 验证了神经屏障和利亚普诺夫功能的有效性.
- 在有限数据的任意切换规则下展示了强大的性能.
结论:
- 拟议的基于学习的控制策略有效地解决了交换非线性系统的安全性和稳定性.
- 神经网络对控制障碍和利亚普诺夫函数的近似计算为复杂系统提供了可行的解决方案.
- 国家过渡学习方法促进了安全和稳定的控制政策的设计.
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