自动化学报2017，Vol.43Issue(2)：271-279,9.DOI:10.16383/j.aas.2017.c150782
基于PLS交叉积矩阵非相似度分析的MPC性能监控与诊断
MPC Performance Monitoring and Diagnosis Based on Dissimilarity Analysis of PLS Cross-product Matrix
摘要
Abstract
Performance monitoring methods for control systems based on output covariance matrix can not sufficiently exploit the correlation between the process variables and output variables.To solve this problem,a performance monitoring and diagnosis method based on dissimilarity analysis of partial least squares (PLS) cross-product matrix is proposed for multivariate model predictive control (MPC) systems.Firstly,the PLS cross-product matrix,which contains the correlation information of augmented process variables and output variables,is constructed.And dissimilarity analysis is carried out to transform dissimilarity comparison of cross-product matrixes to eigenvalue comparison of transformed matrixes.Then,using the l eigenvalues,which include the maximum dissimilarity information,a new performance index is constructed to monitor the performance of MPC system.Finally,the index is further improved to meet the requirement of diagnosing the root cause of performance deterioration.Simulation results on the Wood-Berry binary distillation column demonstrate that the proposed method can effectively enhance the monitoring performance and accurately locate the source of performance deterioration.关键词
模型预测控制/性能监控与诊断/偏最小二乘/交叉积矩阵/非相似度分析Key words
Model predictive control (MPC)/performance monitoring and diagnosis/partial least squares (PLS)/cross-product matrix/dissimilarity analysis引用本文复制引用
尚林源,田学民,曹玉苹,蔡连芳..基于PLS交叉积矩阵非相似度分析的MPC性能监控与诊断[J].自动化学报,2017,43(2):271-279,9.基金项目
国家自然科学基金(61273160,61403418),中央高校基本科研业务费专项资金(15CX06063A),山东省自然科学基金(ZR2014FL016,ZR2016FQ21)资助 Supported by National Natural Science Foundation of China (61273160,61403418),the Fundamental Research Funds for the Central Universities (15CX06063A),and Natural Science Foundation of Shandong Province (ZR2014FL016,ZR2016FQ21) （61273160,61403418）
