上海交通大学学报

上海交通大学学报（自然版） ›› 2011, Vol. 45 ›› Issue (08): 1113-1118.

• 自动化技术、计算机技术 • 上一篇    下一篇

一种数据驱动的预测控制器性能监控方法

张光明，李柠，李少远   

  1. (上海交通大学 自动化系，系统控制与信息处理教育部重点实验室，上海 200240)
  • 出版日期:2011-08-30 发布日期:2011-08-30
  • 基金资助:

    国家高技术研究发展计划（863）项目（2009AA04Z162），国家自然科学基金资助项目（60825302，60934007，61074061），上海市优秀学科带头人计划项目（09XD14202300），上海市教委重点学科建设项目（10JC1403400），上海市“曙光计划”(08GG04)

A DataDriven Approach for Model Predictive Control Performance Monitoring

 ZHANG  Guang-Ming, LI  Ning, LI  Shao-Yuan   

  1. (Department of Automation, Shanghai Jiaotong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China)
  • Online:2011-08-30 Published:2011-08-30

摘要： 提出一种数据驱动的预测控制器性能监控方法.基于马氏距离的综合性能指标，推导了性能指标的基准，以实现对预测控制器性能下降的及时检测.考虑导致预测控制器性能下降的4种常见原因，提出了基于马氏距离性能指标的性能诊断方法，即通过提取过程变量中主元和误差子空间的马氏统计量作为性能特征，利用支持向量机构造分类器，实现了预测控制器的性能诊断.最后，通过WoodBerry过程仿真，验证了所提方法在预测控制器性能监控中的有效性.

关键词: 数据驱动, 预测控制, 马氏距离, 性能监控, 性能诊断

Abstract: A datadriven approach for model predictive control performance monitoring was proposed. An overall performance index based on Mahalanobis distance was introduced with its benchmark deduced to achieve higher monitoring performance. To identify the root cause of performance degradation, Mahalanobis distance based performance diagnosis method was proposed. Performance signatures are extracted from both principal component and residual subspace, and a classifier is constructed to identify four common performance degradation patterns. The effectiveness of the proposed method was demonstrated in a case study of the WoodBerry distillation system.

Key words: datadriven, model predictive control, Mahalanobis distance, performance monitoring, performance diagnosis

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