事件触发的模糊神经网络 PID 控制非线性气体混合过程
Wenbo Dong1, Songyuan Wang2, Zhaozhao Zhang3
1Beijing WenyanShun Technology Co., Ltd., 100085, Beijing, China. wenbo202506@163.com.
Scientific reports
|November 17, 2025
概括
一种新的事件触发模糊神经网络PID控制方法提高了气体混合精度. 这种智能控制系统减少了频繁的更新,提高了性能和效率.
科学领域:
- 控制工程 控制工程 控制工程
- 人工智能的人工智能
- 化学工程是化学工程的重要组成部分.
背景情况:
- 气体混合系统经常受到控制精度低和控制器更新过度的影响.
- 传统的控制方法与天然气度过程中固有的非线性动态作斗争.
研究的目的:
- 提出一个由事件触发的模糊神经网络 PID 控制 (ET-FNN-PID) 方法,用于增强气体混合.
- 为了提高控制精度和减少气体混合应用中控制器更新的频率.
主要方法:
- 开发了一个基于数据的Takagi-Sugeno (TS) 模糊神经网络模型,使用关键的操作变量.
- 实现了一个事件触发机制,具有固定的值,以最大限度地减少控制器的操作.
- 使用梯度下降来在线调整模糊神经网络PID控制器的参数.
主要成果:
- ET-FNN-PID控制器使用真实气体数据准确地捕获了非线性系统行为.
- 与传统方法相比,实现了精确的气体度控制,更新频率大大降低.
- 在时间触发和标准FNN-PID控制器中表现出优越的性能.
结论:
- 拟议的ET-FNN-PID方法提供了优越的气体混合控制性能.
- 这种方法提高了能源效率,并通过优化控制有助于减少排放.
- 事件触发机制有效地减少了机械磨损和计算负载.
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