基于事件触发的预测代学习控制,用于非线性网络系统的随机数据包损失补偿
Qiongxia Yu1, Zhihao Fan1, Xuhui Bu1
1Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China.
ISA transactions
|March 8, 2024
概括
一种新的事件触发式预测代学习控制方法可以补偿网络系统中随机数据包丢失. 这种方法提高了控制性能,并减少了未知的非线性系统的通信负载.
科学领域:
- 控制工程 控制工程 控制工程
- 网络化系统 网络化系统
- 非线性系统是非线性系统.
背景情况:
- 网络系统面临随机数据包丢失 (RPL) 的挑战,降低了控制性能.
- 代学习控制 (ILC) 对于重复的任务是有效的,但对数据丢失敏感.
- 现有的方法可能无法充分解决RPL和通信效率问题.
研究的目的:
- 提出一种新的事件触发预测代学习控制 (ET-PILC) 方法.
- 在未知的非线性网络系统中增强对随机数据包丢失 (RPL) 的稳定性.
- 为了减少通信和计算开销.
主要方法:
- 使用历史和预测数据开发了一个随机数据包损失补偿 (RPLC) 机制.
- 设计了一个基于RPLC机制的事件触发条件.
- 理论上分析了建模和跟踪错误的融合.
主要成果:
- 拟议的RPLC机制有效地减轻了RPL造成的性能下降.
- 事件触发条件节约了通信资源和计算负载.
- 理论分析证实了错误的趋同,并通过模拟验证.
结论:
- 具有RPLC的新型ET-PILC对具有RPL的未知非线性网络系统有效.
- 该方法实现了强大的控制性能,同时优化了资源利用.
- 这项工作在网络控制系统工程方面取得了重大进展.
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