[1] 席裕庚, 李德伟, 林姝.模型预测控制-现状与挑战.自动化学报, 2013, 39(3):222-236 doi: 10.1016/S1874-1029(13)60024-5

Xi Yu-Geng, Li De-Wei, Lin Shu. Model predictive control-status and challenges. Acta Automatica Sinica, 2013, 39(3):222-236 doi: 10.1016/S1874-1029(13)60024-5
[2] Ellis M, Christofides P D. Economic model predictive control of nonlinear time-delay systems:closed-loop stability and delay compensation. AIChE Journal, 2015, 61(12):4152-4165 doi: 10.1002/aic.v61.12
[3] Müller M A, Angeli D, Allgöwer F. On the performance of economic model predictive control with self-tuning terminal cost. Journal of Process Control, 2014, 24(8):1179-1186 doi: 10.1016/j.jprocont.2014.05.009
[4] Wang L, Li N, Li S Y. Performance monitoring of the data-driven subspace predictive control systems based on historical objective function benchmark. Acta Automatica Sinica, 2013, 39(5):542-547 https://www.researchgate.net/publication/270524834_Performance_Monitoring_of_the_Data-driven_Subspace_Predictive_Control_Systems_Based_on_Historical_Objective_Function_Benchmark
[5] Harris T J. Assessment of control loop performance. The Canadian Journal of Chemical Engineering, 1989, 67(5):856-861 doi: 10.1002/cjce.v67:5
[6] 田学民, 罗芝芬, 王平.基于LQG基准的预测控制器经济敏感度分析及调节准则.自动化学报, 2013, 39(10):1735-1740 doi: 10.3724/SP.J.1004.2013.01735

Tian Xue-Min, Luo Zhi-Fen, Wang Ping. LQG-based sensitivity analysis and tuning guidelines in economic performance assessment of predictive controller. Acta Automatica Sinica, 2013, 39(10):1735-1740 doi: 10.3724/SP.J.1004.2013.01735
[7] Liu C Y, Huang B, Wang Q L. Control performance assessment subject to multi-objective user-specified performance characteristics. IEEE Transactions on Control Systems Technology, 2011, 19(3):682-691 doi: 10.1109/TCST.2010.2051669
[8] Ge Z Q, Song Z H. An overview of conventional MSPC methods. Multivariate Statistical Process Control. London:Springer, 2013. 5-11
[9] Yan Z B, Chan C L, Yao Y. Multivariate control performance assessment and control system monitoring via hypothesis test on output covariance matrices. Industrial & Engineering Chemistry Research, 2015, 54(19):5261-5271 https://www.researchgate.net/profile/Yuan_Yao10/publication/275970157_Multivariate_Control_Performance_Assessment_and_Control_System_Monitoring_via_Hypothesis_Test_on_Output_Covariance_Matrices/links/554cdff00cf29752ee81d671.pdf
[10] Jelali M. Statistical process control. Control Performance Management in Industrial Automation. London:Springer, 2013. 209-217
[11] Zhang Q, Li S Y. Performance monitoring and diagnosis of multivariable model predictive control using statistical analysis. Chinese Journal of Chemical Engineering, 2006, 14(2):207-215 doi: 10.1016/S1004-9541(06)60060-8
[12] AlGhazzawi A, Lennox B. Model predictive control monitoring using multivariate statistics. Journal of Process Control, 2009, 19(2):314-327 doi: 10.1016/j.jprocont.2008.03.007
[13] Chen J H, Wang W Y. PCA-ARMA-based control charts for performance monitoring of multivariable feedback control. Industrial & Engineering Chemistry Research, 2010, 49(5):2228-2241 https://www.researchgate.net/publication/231390600_PCA-ARMA-Based_Control_Charts_for_Performance_Monitoring_of_Multivariable_Feedback_Control
[14] Shang C, Huang B, Fan Y, Huang D X. Slow feature analysis for monitoring and diagnosis of control performance. Journal of Process Control, 2016, 39:21-34 doi: 10.1016/j.jprocont.2015.12.004
[15] Das L, Srinivasan B, Rengaswamy R. Multivariate control loop performance assessment with Hurst exponent and Mahalanobis distance. IEEE Transactions on Control Systems Technology, 2016, 24(3):1067-1074 doi: 10.1109/TCST.2015.2468087
[16] Yu J, Qin S J. Statistical MIMO controller performance monitoring. Part I:data-driven covariance benchmark. Journal of Process Control, 2008, 18(3-4):277-296 https://www.researchgate.net/publication/223870439_Statistical_MIMO_Controller_Performance_Monitoring_Part_I_Data-Driven_Covariance_Benchmark
[17] Yu J, Qin S J. Statistical MIMO controller performance monitoring. Part II:performance diagnosis. Journal of Process Control, 2008, 18(3-4):297-319 doi: 10.1016/j.jprocont.2007.09.003
[18] Tian X M, Chen G Q, Chen S. A data-based approach for multivariate model predictive control performance monitoring. Neurocomputing, 2011, 74(4):588-597 doi: 10.1016/j.neucom.2010.09.018
[19] 田学民, 史亚杰, 曹玉苹.基于协方差指标预测的MPC实时性能监控.自动化学报, 2013, 39(5):658-663 http://www.aas.net.cn/CN/abstract/abstract17929.shtml

Tian Xue-Min, Shi Ya-Jie, Cao Yu-Ping. Real-time performance monitoring of MPC based on covariance index prediction. Acta Automatica Sinica, 2013, 39(5):658-663 http://www.aas.net.cn/CN/abstract/abstract17929.shtml
[20] Li C, Huang B, Zheng D, Qian F. Multi-input-multi-output (MIMO) control system performance monitoring based on dissimilarity analysis. Industrial & Engineering Chemistry Research, 2014, 53(47):18226-18235 https://www.researchgate.net/publication/280263633_Multi-input-Multi-output_MIMO_Control_System_Performance_Monitoring_Based_on_Dissimilarity_Analysis?_sg=bUAdqxwkqagD1P7Xssbk_W35jkhbuN3StQIZWLR6_velPNM2YQYCmNSFTRbbaa3JcYxG3f9PvUzbQZVTCN3h1w
[21] Wold H. Soft modeling by latent variables:the nonlinear iterative partial least squares approach. Perspectives in Probability and Statistics:Papers in Honour of M.S. Bartlett. New York:Academic Press, 1975. 520-540
[22] Höskuldsson A. PLS regression methods. Journal of Chemometrics, 1988, 2(3):211-228 doi: 10.1002/(ISSN)1099-128X
[23] Rosipal R, Trejo L J, Matthews B. Kernel PLS-SVC for linear and nonlinear classification. In:Proceedings of the 20th International Conference on Machine Learning. Washington D.C., USA, 2003. 640-647
[24] Barker M, Rayens W. Partial least squares for discrimination. Journal of Chemometrics, 2003, 17(3):166-173 doi: 10.1002/(ISSN)1099-128X
[25] Kano M, Hasebe S, Hashimoto I, Ohno H. Statistical process monitoring based on dissimilarity of process data. AIChE Journal, 2002, 48(6):1231-1240 doi: 10.1002/(ISSN)1547-5905
[26] Yuan Q L, Lennox B, McEwan M. Analysis of multivariable control performance assessment techniques. Journal of Process Control, 2009, 19(5):751-760 doi: 10.1016/j.jprocont.2008.10.001
[27] Tan S, Wang F L, Peng J, Chang Y Q, Wang S. Multimode process monitoring based on mode identification. Industrial & Engineering Chemistry Research, 2012, 51(1):374-388 https://www.researchgate.net/publication/263957976_Multimode_Process_Monitoring_Based_on_Mode_Identification
[28] Zheng Y, Qin S J, Wang F L. PLS-based similarity analysis for mode identification in multimode manufacturing processes. In:Proceedings of the 9th IFAC Symposium on Advanced Control of Chemical Processes. Whistler, British Columbia, Canada:Elsevier, 2015, 48(8):777-782