在MIMO闭环控制系统中对模型与工厂不匹配的数据驱动诊断
Dan Ling1,2, Tengfei Jiang1,2, Junwei Sun1,2
1College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, People's Republic of China.
ACS omega
|April 28, 2025
概括
这项研究引入了一种新的方法来诊断控制系统中的模型植物不匹配. 它量化了差异,识别了错误的通道,并区分了与控制器更改的不匹配.
科学领域:
- 过程控制 过程控制
- 系统工程 系统工程
- 模型验证模型验证
背景情况:
- 有效的控制系统性能在很大程度上依赖于准确的过程模型.
- 量化工艺模型与实际工厂之间的差异对于可靠的控制设计至关重要.
- 现有的方法可能无法充分将模型植物不匹配与其他系统变异隔离开来.
研究的目的:
- 在内部模型控制 (IMC) 框架内提出一种新的方法来诊断模型-工厂不匹配.
- 开发用于评估整体过程模型质量,个别输出准确度和特定输入输出通道性能的指标.
- 为了使模型-工厂不匹配与控制器参数变化的隔离.
主要方法:
- 使用内部模型控制 (IMC) 框架进行诊断.
- 白化系统输出并识别外部干扰模型.
- 基于已识别的干扰和控制模型,开发一个过程模型残留物.
- 定义三个不同的模型质量指数.
主要成果:
- 介绍了一种用于模型植物不匹配诊断的新方法.
- 该方法成功地识别了控制系统中不匹配的子频道.
- 模型植物不匹配可以有效地从控制器参数的变化中隔离出来.
- 在模拟列过程和田纳西东曼基准上表现出能力.
结论:
- 拟议的方法提供了一种可靠的方法来量化和诊断模型植物不匹配.
- 定义的模型质量指数为过程模型准确性提供了有价值的见解.
- 这种技术通过确保模型忠实性来提高控制系统的可靠性.
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