通过优化模式分离方法对马科维斯跳跃系统进行抽样数据随机稳定
IEEE transactions on cybernetics
|March 3, 2025
概括
这项研究引入了一种新的方法,用于使用采样数据控制器稳定马科维跳跃系统 (MJS). 该方法优化模式分离,以提高性能和减少随机系统中的保守性.
科学领域:
- 控制理论 控制理论
- 系统工程 系统工程
- 随机系统 随机系统 随机系统
背景情况:
- 马科维斯跳跃系统 (MJS) 由于其依赖状态的切换行为,存在独特的控制挑战.
- 采样数据控制器在MJS中引入了更多的复杂性,原因是信号离散和控制器切换.
研究的目的:
- 为使用采样数据控制器的MJS开发一种新的随机稳定方法.
- 解决与随机控制器切换和采样状态信号相关的挑战.
- 与现有方法相比,为了获得不那么保守的结果.
主要方法:
- 模式分离的优化,其数量由第二种斯特林数决定.
- 使用增强的拉格朗代价函数来制定一个优化问题的公式,以保证局部最佳性.
- 开发了一种改进的登算法,增强了Q学习,以获得最佳的衰减系数选择.
主要成果:
- 介绍了一种使用采样数据控制器对MJS进行随机稳定的一种新方法.
- 拟议的方法比现有方法提供了不那么保守的结果.
- 开发了一个高效的优化算法,减少计算复杂性.
结论:
- 开发的方法有效地稳定了在采样数据控制下马科维亚跳跃系统.
- 优化策略和增强的算法提供了一个优越和计算效率高的方法.
- 通过两个说明性示例证明有效性和优越性来验证这些发现.
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