基于神经网络的自适应滑动模式控制,用于切换具有马尔科夫跳转参数的分布式延迟系统
Baoping Jiang1, Hamid Reza Karimi2, Xin Zhang3
1School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China; Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy.
本研究介绍了基于观察者的自适应滑动模式控制,用于具有切换和随机跳跃的复杂延迟系统. 新型神经网络方法确保了有限时间的稳定性,即使在未知的系统动态.
科学领域:
- 控制系统工程 控制系统工程
- 应用数学 应用数学 应用数学
- 人工智能的人工智能
背景情况:
- 分布式延迟系统在现实应用中很常见,但难以控制.
- 具有决定性切换和随机跳跃的系统表现出复杂的动态.
- 现有的控制方法经常与未知的参数和非线性作斗争.
研究的目的:
- 为分布式延迟系统开发基于观察者的自适应滑动模式控制策略.
- 用于处理具有同时确定性切换和随机跳跃过程的系统.
- 增强对未知的系统非线性和过渡率的稳定性.
主要方法:
- 设计一个Lebesgue观察器用于状态估计.
- 一个整体形状的滑动模式超平面的制定.
- 开发一种用于有限时间滑动运动的新型自适应动态控制器.
- 集成基于观察者的神经补偿器用于非线性衰减.
- 应用平均停留时间方法进行稳定性分析.
主要成果:
- 滑动运动的有限时间存在是保证的,即使有未知的模式信息.
- 拟议的自适应控制器有效地处理未知的过渡率.
- 神经补偿器成功地减轻了未知的非线性.
- 使用平均停留时间方法证明了平均平方指数稳定性.
- 通过实践示例验证证实了该方法的有效性.
结论:
- 拟议的基于观察者的自适应滑动模式控制对复杂的延迟系统有效.
- 该方法在具有挑战性的条件下提供了稳定性和有限时间稳定性.
- 这种方法为控制具有切换和随机性的系统提供了一个统一的框架.
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