一个由神经动态事件触发的机制,用于控制非线性联网马科维斯跳跃系统的自适应滑动模式
Yiming Yang1, Dongyu Liu1, Baoping Jiang2
1School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.
ISA transactions
|October 8, 2025
概括
这项研究引入了一个适应性的滑动模式控制,用于随机跳跃的非线性系统,使用事件触发的观察者和神经网络. 该方法确保了稳定性和安全性,防止干扰和攻击,但不包括Zeno行为.
科学领域:
- 控制理论 控制理论
- 系统工程 系统工程
- 网络化系统 网络化系统
背景情况:
- 非线性联网的马科维斯跳跃系统由于系统不确定性和潜在的攻击,在控制方面存在挑战.
- 事件触发的观察员对于减少网络控制系统中的通信和计算负载至关重要.
- 滑动模式控制提供了稳健性,但需要对复杂系统进行仔细设计.
研究的目的:
- 为非线性联网马科维斯跳跃系统开发一种自适应的滑动模式控制策略.
- 设计一个与神经网络集成的事件触发观察者,以增强系统监控.
- 为了确保有限时间的融合,随机稳定性与H∞性能,并防止Zeno行为.
主要方法:
- 一个基于神经网络的事件触发机制与观察者集成.
- 设计了一种全新的整体滑动表面,并推导滑动模式和错误动态.
- 针对非线性干扰和恶意攻击的自适应补偿器的开发.
- 基于观察者的事件触发的滑动模式控制器的设计.
- 使用线性矩阵不等式 (LMI) 和排除Zeno行为进行稳定性分析.
主要成果:
- 拟议的控制方案确保了与滑动表面的有限时间趋同.
- 对于滑动模式动态,已经证明了具有规定的H∞性能水平的随机稳定性.
- 事件触发机制有效地降低了系统负载,同时保持了控制性能.
- 成功排除了Zeno的行为,确保了实际实施.
- 使用单环操纵器模型的模拟结果验证了控制方案的有效性.
结论:
- 开发的自适应滑动模式控制策略对于非线性联网的马科维斯跳跃系统是有效的.
- 神经网络和事件触发观察者的集成提高了系统的稳定性和效率.
- 提出的方法提供了安全和稳定的控制解决方案,防止干扰和攻击.
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