基于最小方差基准的非线性系统控制性能评估研究
|
摘要
控制性能评估技术(CPA)兴起于上世纪80年代末,是工业过程自动化领域一项重要的辅助管理技术。控制性能评估的主要目标是在尽量不影响当前控制系统正常运行的情况下,对控制器的性能做出评价,得出其与理论最优的基准控制器的性能差距。进而,在控制性能不满足设计要求时,对造成性能不良的原因进行诊断并给出改进决策。控制性能评估技术在维护自动化系统安全高效运行中发挥了积极作用,其主要应用于石油化工、水处理、发酵及制药等工业过程领域。
控制性能评估技术在理论研究和工程应用中都已取得许多成果。但作为一门综合技术,现有研究还远未覆盖控制性能评估技术的全部阶段,即使作为研究最多的性能基准选择和评估方面,分析对象也主要限于线性系统,非线性系统性能评估因其复杂性致使研究成果甚少。针对此问题,在最小方差性能基准框架下,本文结合当前控制性能评估技术的发展现状,分别在以下几个方面展开研究：
(1)提出一种切换结构的最小方差控制性能评估策略。考虑到许多实际工业过程运行初期表现为线性系统,但随时间推移可能因外部因素影响使其出现非线性特征。传统性能评估技术实施中一般忽略该类变化而直接使用线性评估方法,评估结果存在偏差。对一类特定结构的非线性系统,在进行控制性能评估之前首先使用基于高阶统计量的方法进行非线性检测分析,如检测结果为线性,采用线性评估方法,否则,采用非线性评估方法。通过使用多项式级数近似非线性环节,将最小方差控制性能下限的估计问题转换为最小二乘辨识问题,在系统辨识的同时实现对非线性系统的控制性能评估。
(2)将方差分析技术用于非线性系统的控制性能评估,首先提出一类特定结构的SISO非线性系统的最小方差控制性能评估方法,进而扩展到非线性前馈/反馈控制系统。针对一类结构为非线性过程模型叠加线性(部分非线性)干扰模型的SISO非线性系统,分析了其最小方差控制性能下限的表达形式,并指出此性能下限和非线性系统的b个超前干扰驱动源直接相关。通过正交最小二乘辨识方法获得系统闭环模型,在此基础上由方差分解公式计算得到非线性系统的最小方差控制性能指标。进一步,文章证明了两种不同结构的非线性前馈/反馈控制系统最小方差性能下限的存在性,分析了方差分析技术用于其控制性能评估研究的可行性,并结合迭代正交最小二乘辨识方法实现对非线性前馈/反馈控制系统进行性能评估。最后通过一研究实例验证了该方法的有效性。
(3)针对现有非线性系统广义最小方差(NGMV)控制性能评估方法的不足,提出一种改进的性能评估指标。最小方差控制器因其输出过于激烈以及鲁棒性差而难以实际运用,广义最小方差控制器通过误差权和控制权参数的引入可对控制量幅值进行限制。现有对NGMV控制系统进行性能评估直接使用线性方法,其理论前提是NGMV控制器作用下的系统闭环输出可退化为一个线性移动平均过程。但作为动态系统,此理论前提很容易受模型变化或外部干扰等因素影响而被破坏,导致评估结果存在误差或完全不具有指导意义。文章在方差分析技术的基础上,分析广义最小方差控制作用下的非线性系统闭环输出方差的组成结构,提出一种基于方差分解的NGMV性能评估方法,仿真研究证明了其有效性。
Control performance assessment (CPA) began to blossom in the late1980s, and it is an important asset-management technology in the field of industrial process automation. The main objective of CPA is to evaluate the performance of controller without disturbing the normal operation of the control systems, and find the lack with respect to the theoretical best optimized contoller. When the performance of a running controller deviates from the desired aim, we must determinate the reasons of bringing poor performance and point out what should be done to improve the controller. The technology of CPA plays a significant part for maintenance industrial automatic systems with safety and high efficiency work. The application of CPA has been registered in the industrial process fields of petrochemicals, water treatment, ferment engineering, pharmacetical engineering, and so on.
There are many researches and industrial applications in control performance assessment. However, because CPA is a synthetical technology, the existing studies are far from containing all the stages. Even in the first step of CPA study that central task is to select performance benchmarks and estimate the performance of running controllers, the most existing methods work on the assumption that the control systems are linear processes. The papers for nonlinear systems in performance assessment have been infrequently written due to its complexity. For the above mentioned problems, this thesis will focus on the following research topics within the framework of minimum variance benchmark.
(1) A switching strategy of control performance assessment based on minimum variance is proposed. Many practical industrial processes can be seen as linear systems at the beginning of running, but with the passage of time, the control loops possibly include nonlinearites from external influence. The estimates of most traditional CPA methods will bring mistake because of failing to notice the nonlinearites. The nonlinearity detection tests based on higher order statistics are used to quantify the nonlinear property presented in the output signals before selecting performance assessment methods. If nonlinearity index(NLI) shows that the control systems are linear, then traditional linear performance assessment methods are adopted, otherwise the new nonlinear method is used. Farther, the problem of nonlinear minimum variance performance assessment is converted into model identification with the help of Volterra series. Finally, a simulation example indicates that the proposed algorithm gives better minimum variance performance bound (MVPLB) compared with existing methods for linear system performance assessment.
(2) The technology of variance analysis is introduced to the study of control performance assessment for nonlinear systems. First,.a performance estimated method for a specific class of SISO nonlinear systems is proposed. Subsequently, the similar method is expanded into the performance assessment of nonlinear feedforward/feedback control systems. For a class of nonlinear SISO processes that can be described by the superposition of a nonlinear dynamic model and additive linear or partially nonlinear disturbance, the expression of the minimum variance performance lower bound is derived and the conclusion that the MVPLB only depends on the driving forces of most recent ahead disturbances is obtained. Then, the model of closed loop system is identified by using orthogonal least square algorithm. Moreover, based on the achieved model, the minimum variance performance index of nonlinear systems is calculated by using variance decomposition formula. In addition, the existence of MVPLB for two nonlinear feedforward/feedback control systems with different structure is proved in this thesis. And, whether it is appropriate that the variance analysis technology can be used for evaluating the performance of nonlinear feedforward/feedback control systems is analysed. Then, the problem of performance assessment of nonlinear feedforward/feedback control systems is solved with the help of iterative orthogonal least squares identification method. Finally, a simulation example indicates that the algorithm proposed in this paper gives better effect compared with the existing methods.
(3) To the shortage of current performance assessment methods for nonlinear systems based on generalized minimum variance (GMV) benchmark, a novel performance index is explored and exploited. The actual application of minimum variance controller is difficult because of the violence of control signal changes and the worst robust performance. Due to the introducing of error and control weighting terms, the magnitude of GMV controller can be restricted. For the present study of estimating the performance of nonlinear GMV control systems, the traditional linear methods are directly adopted. These linear methods are effective because of the output signals of nonlinear control systems in effect of theoretical nonlinear GMV controllers can be described by linear moving average processes. However, this precondition may be easily distorted by model changes and external disturbances in practice, so the results of linear performance assessment methods are inaccurate or at all meaningless. On the basis of variance analysis technology, the configuration of variance of closed-loop output signals is analysed, and a nonlinear GMV control performance assessment method of nonlinear systems is proposed. Finally, a simulation example indicates that the algorithm is effective.
控制性能评估技术在理论研究和工程应用中都已取得许多成果。但作为一门综合技术,现有研究还远未覆盖控制性能评估技术的全部阶段,即使作为研究最多的性能基准选择和评估方面,分析对象也主要限于线性系统,非线性系统性能评估因其复杂性致使研究成果甚少。针对此问题,在最小方差性能基准框架下,本文结合当前控制性能评估技术的发展现状,分别在以下几个方面展开研究：
(1)提出一种切换结构的最小方差控制性能评估策略。考虑到许多实际工业过程运行初期表现为线性系统,但随时间推移可能因外部因素影响使其出现非线性特征。传统性能评估技术实施中一般忽略该类变化而直接使用线性评估方法,评估结果存在偏差。对一类特定结构的非线性系统,在进行控制性能评估之前首先使用基于高阶统计量的方法进行非线性检测分析,如检测结果为线性,采用线性评估方法,否则,采用非线性评估方法。通过使用多项式级数近似非线性环节,将最小方差控制性能下限的估计问题转换为最小二乘辨识问题,在系统辨识的同时实现对非线性系统的控制性能评估。
(2)将方差分析技术用于非线性系统的控制性能评估,首先提出一类特定结构的SISO非线性系统的最小方差控制性能评估方法,进而扩展到非线性前馈/反馈控制系统。针对一类结构为非线性过程模型叠加线性(部分非线性)干扰模型的SISO非线性系统,分析了其最小方差控制性能下限的表达形式,并指出此性能下限和非线性系统的b个超前干扰驱动源直接相关。通过正交最小二乘辨识方法获得系统闭环模型,在此基础上由方差分解公式计算得到非线性系统的最小方差控制性能指标。进一步,文章证明了两种不同结构的非线性前馈/反馈控制系统最小方差性能下限的存在性,分析了方差分析技术用于其控制性能评估研究的可行性,并结合迭代正交最小二乘辨识方法实现对非线性前馈/反馈控制系统进行性能评估。最后通过一研究实例验证了该方法的有效性。
(3)针对现有非线性系统广义最小方差(NGMV)控制性能评估方法的不足,提出一种改进的性能评估指标。最小方差控制器因其输出过于激烈以及鲁棒性差而难以实际运用,广义最小方差控制器通过误差权和控制权参数的引入可对控制量幅值进行限制。现有对NGMV控制系统进行性能评估直接使用线性方法,其理论前提是NGMV控制器作用下的系统闭环输出可退化为一个线性移动平均过程。但作为动态系统,此理论前提很容易受模型变化或外部干扰等因素影响而被破坏,导致评估结果存在误差或完全不具有指导意义。文章在方差分析技术的基础上,分析广义最小方差控制作用下的非线性系统闭环输出方差的组成结构,提出一种基于方差分解的NGMV性能评估方法,仿真研究证明了其有效性。
Control performance assessment (CPA) began to blossom in the late1980s, and it is an important asset-management technology in the field of industrial process automation. The main objective of CPA is to evaluate the performance of controller without disturbing the normal operation of the control systems, and find the lack with respect to the theoretical best optimized contoller. When the performance of a running controller deviates from the desired aim, we must determinate the reasons of bringing poor performance and point out what should be done to improve the controller. The technology of CPA plays a significant part for maintenance industrial automatic systems with safety and high efficiency work. The application of CPA has been registered in the industrial process fields of petrochemicals, water treatment, ferment engineering, pharmacetical engineering, and so on.
There are many researches and industrial applications in control performance assessment. However, because CPA is a synthetical technology, the existing studies are far from containing all the stages. Even in the first step of CPA study that central task is to select performance benchmarks and estimate the performance of running controllers, the most existing methods work on the assumption that the control systems are linear processes. The papers for nonlinear systems in performance assessment have been infrequently written due to its complexity. For the above mentioned problems, this thesis will focus on the following research topics within the framework of minimum variance benchmark.
(1) A switching strategy of control performance assessment based on minimum variance is proposed. Many practical industrial processes can be seen as linear systems at the beginning of running, but with the passage of time, the control loops possibly include nonlinearites from external influence. The estimates of most traditional CPA methods will bring mistake because of failing to notice the nonlinearites. The nonlinearity detection tests based on higher order statistics are used to quantify the nonlinear property presented in the output signals before selecting performance assessment methods. If nonlinearity index(NLI) shows that the control systems are linear, then traditional linear performance assessment methods are adopted, otherwise the new nonlinear method is used. Farther, the problem of nonlinear minimum variance performance assessment is converted into model identification with the help of Volterra series. Finally, a simulation example indicates that the proposed algorithm gives better minimum variance performance bound (MVPLB) compared with existing methods for linear system performance assessment.
(2) The technology of variance analysis is introduced to the study of control performance assessment for nonlinear systems. First,.a performance estimated method for a specific class of SISO nonlinear systems is proposed. Subsequently, the similar method is expanded into the performance assessment of nonlinear feedforward/feedback control systems. For a class of nonlinear SISO processes that can be described by the superposition of a nonlinear dynamic model and additive linear or partially nonlinear disturbance, the expression of the minimum variance performance lower bound is derived and the conclusion that the MVPLB only depends on the driving forces of most recent ahead disturbances is obtained. Then, the model of closed loop system is identified by using orthogonal least square algorithm. Moreover, based on the achieved model, the minimum variance performance index of nonlinear systems is calculated by using variance decomposition formula. In addition, the existence of MVPLB for two nonlinear feedforward/feedback control systems with different structure is proved in this thesis. And, whether it is appropriate that the variance analysis technology can be used for evaluating the performance of nonlinear feedforward/feedback control systems is analysed. Then, the problem of performance assessment of nonlinear feedforward/feedback control systems is solved with the help of iterative orthogonal least squares identification method. Finally, a simulation example indicates that the algorithm proposed in this paper gives better effect compared with the existing methods.
(3) To the shortage of current performance assessment methods for nonlinear systems based on generalized minimum variance (GMV) benchmark, a novel performance index is explored and exploited. The actual application of minimum variance controller is difficult because of the violence of control signal changes and the worst robust performance. Due to the introducing of error and control weighting terms, the magnitude of GMV controller can be restricted. For the present study of estimating the performance of nonlinear GMV control systems, the traditional linear methods are directly adopted. These linear methods are effective because of the output signals of nonlinear control systems in effect of theoretical nonlinear GMV controllers can be described by linear moving average processes. However, this precondition may be easily distorted by model changes and external disturbances in practice, so the results of linear performance assessment methods are inaccurate or at all meaningless. On the basis of variance analysis technology, the configuration of variance of closed-loop output signals is analysed, and a nonlinear GMV control performance assessment method of nonlinear systems is proposed. Finally, a simulation example indicates that the algorithm is effective.
引文
1. Jelali M. An overview of control performance assessment technology and industrial applications[J]. Control Engineering Practice,2006,14(5):441-466.
2. Huang B. Bayesian methods for control loop monitoring and diagnosis[J]. Journal of Process Control, 2008,18(9):829-838.
3. Thornhill N F, Hagglund T. Detection and diagnosis of oscillation in control loops [J]. Control Engineering Practice,1997,5(10):1343-1354.
4. Schafer J, Cinar A. Multivariable MPC performance assessment, monitoring and diagnosis[C]. In: Proceedings of the IFAC World Congress. Barcelona, Spain,2002
5. Haber R, Keviczky L. Nonlinear system identification,Input-output modelling approach [M]. Dordrecht:Kluwer Academic Publishers,1999.
6. Rao T S, Gabr M M. A test for linearity and stationarity of time series.[J]. Journal of Time Series Analysis,1980,1(1):145-158.
7. Hinich, Melvin J. Testing for gaussianity and linearity of a stationary time series[J]. Journal of time series analysis,1982,3(3):169-176.
8. Fackrell J W A. Bispectral analysis of speech signals[D]:[PhD thesis]. The University of Edinburgh, UK,1996.
9. Yuan J. Testing linearity for stationary time series using the sample interquartile range[J]. Journal of Time Series Analysis,1999,21(6):713-722.
10. Choudhury M A A S, Shaha S L, Thornhillb N F. Diagnosis of poor control-loop performance using higher-order statistics[J]. Automatica,2004,40(10):1719-1728.
11. Hagglund T. A control-loop performance monitor[J]. Control Engineering Practice,1995,3(11): 1543-1551.
12. Forsman K, Stattin A. A new criterion for detecting oscillations in control loops[C]. In:Proceedings of the European control conference. Karlsruhe, Germany,1999
13. Mia T, Seborg D E. Automatic detection of excessively oscillatory feedback control loops[C]. In: Proceedings of the IEEE Conference on Control Applications. Kohala Coast-Island, USA,1999
14. Ettaleb L. Control loop performance assessment and oscillation detection[D]:[PhD thesis]. University of British Columbia, Canada,1999.
15. Ettaleb L, Davies M S, Dumont G A, et al. Monitoring oscillations in a multiloop system[C]. In: Proceedings of the IEEE Conference on Control Applications. Dearborn, USA,1996.859-863.
16. Thornhill N F, Shah S L, Huang B. Detection of distributed oscillations and root-cause diagnosis[C]. In:Proceedings of the 4th IFAC Workshop on On-line Fault Detection and Super Vision in the Chemical Process Industries. Chejudo Island, Korea,2001.167-172.
17. Thornhill N F, Huang B, Zhang H. Detection of multiple oscillations in control loops[J]. Journal of Process Control,2003,13(1):91-100.
18. Horch A. A simple method for detection of stiction in control valves[J]. Control Engineering Practice, 1999,7(10):1221-1231.
19. Kano M, Maruta H, Kugemoto H, ef al. Practical model and detection algorithm for valve stiction[C]. In:Proceedings of IFAC symposium on Dynamics and Control of Process Systems. Cambridge,2004.
20. Singhal A, Salsbury T I.A simple method for detecting valve stiction in oscillating control loops[J]. Journal of Process Control,2005,15(4):371-382.
21. Yoshiyuki Y. An automatic method for detection of valve stiction in process control loops [J]. Control Engineering Practice,2006,14(5):503-510.
22. Jelali M. Estimation of valve stiction in control loops using separable least-squares and global search algorithms [J]. Journal of Process Control,2008,18(8):632-642.
23. Zabiri H, Ramasamy M. NLPCA as a diagnostic tool for control valve stiction[J]. Journal of Process Control,2009,19(8):1368-1376.
24. Saneej B, Chitralekha, Shah S L, et al. Detection and quantification of valve stiction by the method of unknown input estimation [J]. Journal of Process Control,2010,20(2):206-216.
25. Hagglund T. A shape-analysis approach for diagnosis of stiction in control valves [J]. Control Engineering Practice,2011,19(8):782-789.
26. Ale Mohammad M, Huang B. Compensation of control valve stiction through controller tuning [J]. Journal of Process Control,2012,22(9):1800-1819.
27. Hagglund T. Automatic detection of sluggish control loops[J]. Control Engineering Practice,1999, 7(12):1505-1511.
28. Kuehl P, Horch A. Detection of sluggish control loops-experiences and improvements [J]. Control Engineering Practice,2005,13(8):1019-1025.
29. Gerry J, Ruel M. How to measure and combat valve stiction on line[C]. In:Proceedings of the ISA. Houston, TX,2001
30. Chien I L, Fruehauf P S. Consider IMC tuning to improve controller performance[J]. Chemical Engineering Progress,1990,86:33-41.
31. Chen D, Seborg D E. PI/PID controller design based on direct synthesis and disturbance rejection[J]. Industrial and Engineering Chemistry Research,2002,41(19):4807-4822.
32. Skogestad S. Simple analytic rules for model reduction and PID controller tuning[J]. Journal of Process Control,2003,13(4):291-309.
33. DeVries W, Wu S. Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate timeseries analysis[J]. IEEE Transactions on Automatic Control,1978,23(4): 702-708.
34. Harris T J. Assessment of control loop performance[J]. Canadian Journal of Chemical Engineering, 1989,67(5):856-861.
35. Desborough L D, Harris T J. Performance assessment measures for univariate feedback control[J]. The Canadian Journal of Chemical Engineering,1992,70(6):1186-1197.
36. Tyler M, Morari M. Performance assessment for unstable and nonminimum-phase systems[C]. In: Preprints IFAC Workshop On-line Fault Detection Supervision Chemical Process Industries. Newcastle upon Tyne,UK,1995
37. Tyler M, Morari M. Performance monitoring of control systems using likelihood methods[J]. Automatica,1996,32(8):1145-1162.
38. Lynch C B, Dumont G A. Control loop performance monitoring[J]. IEEE Transactions on Control Technology,1996,4(2):185-192.
39. Qin S J. Control performance monitoring-a review and assessment[J]. Computers and Chemical Engineering,1998,23(2):173-186.
40. Perrier M, Roche A A. Towards mill-wide evaluation of control loop performance[C]. In:Proceedings of the Control Systems. Whistler, Canada,1992.205-209.
41. Ko B S, Edgar T F. Performance assessment of cascade control loops[J]. AIChE Journal,2000,46(2): 281-291.
42. Seppala C T, Harris T J, Bacon D W. Time series methods for dynamic analysis of multiple controlled variables[J]. Journal of Process Control,2002,12(2):257-276.
43. Thornhill N F, Huang B, Shah S L. Controller performance assessment in set point tracking and regulatory control[J]. International Journal of Adaptive Control and Signal Processing,2003,17(7): 709-727.
44. McNabb C A, Qin S J. Projection based MIMO control performance monitoring:Ⅱ-Measured disturbances and setpoint changes [J]. Journal of Process Control,2005,15(1):89-102.
45. Li Z, Evans R J. Minimum-variance control of linear time varying systems[J]. Automatica,1997, 33(8):1531-1537.
46. Huang B. Performance assessment of processes with abrupt changes of disturbances[J]. Canadian Journal of Chemical Engineering,1999,77(5):1044-1054.
47. Huang B. Minimum variance control and performance assessment of time variant processes[J]. Journal of Process Control,2002,12(6):707-719.
48. Olaleye F, Huang B, Tamayo E. Performance assessment of control loops with time varying disturbance dynamics[J]. Journal of Process Control,2004,14(8):867-877.
49. Olaleye F, Huang B, Tamayo E. Feedforward and feedback controller performance assessment of linear time-variant processes[J]. Industrial and Engineering Chemistry Research,2004,43(2):589-596.
50. Olaleye F, Huang B, Tamayo E. Industrial applications of feedback controller performance assessment of time-variant processes[J]. Industrial and Engineering Chemistry Research,2004,43(2):597-607.
51. Xu F W, Huang B, Tamayo E C. Performance assessment of MIMO control systems with time-variant disturbance dynamics[J]. Computers and Chemical Engineering,2008,32(9):2144-2154.
52. Desborough L, Harris T. Performance assessment measures for univariate feedforward/feedback control[J]. Canadian Journal of Chemical Engineering,1993,71(4):605-616.
53. Stanfelj N, Marlin T, MacGregor J. Monitoring and diagnosing process control performance:The single loop case[J]. Industrial and Engineering Chemistry Research,1993,32(2):301-314.
54. Chen J H, Yea Y Z. Assessment and diagnosis of feedforward/feedback control system[C]. In: American Control Conference. Portland, OR, USA,2005.490-495.
55. Huang B, Shah S L, Miller R. Feedforward plus Feedback Controller Performance Assessment of MIMO systems [J]. IEEE Transactions on Control Systems Technology,2000,8(3):580-587.
56. Harris T, Boudreau F, MacGregor J F. Performance assessment using of multivariable feedback controllers [J]. Automatica,1996,32(11):1505-1518.
57. Rogozinski M W, Paplinski A P, Gibbard M J. An algorithm for the calculation of a nilpotent interactor matrix for linear multivariable systems[J]. IEEE transactions on Automatic Control,1987,32(3):234-237.
58. Huang B, Shah S L, Kwok E K. Good, bad or optimal?Performance assessment of multivariable processes[J]. Automatica,1997,33(6):1175-1183.
59. Kamrunnahar M, Huang B, Fisher D G. Estimation of Markov parameters and timedelay interactormatrix [J]. Chemical Engineering Science,2000,55(17):3353-3363.
60. Kamrunnahar M, Fisher D G, Huang B. Performance assessment and robustness analys using an ARMarkov approach[J]. Journal of Process Control 2004,14(8):915-925.
61. Harris T J, Seppala C T, Desborough L D. A review of performance monitoring and assessment techniques for univariate and multivariate control systems[J]. Journal of Process Control,1999,9(1):1-17.
62. Shah S L, Patwardhan R, Huang B. Multivariate controller performance assessment:methods, applications and challenges[C]. In:Chemical Process Control-CPC. Arizona, US,2002.190-207.
63. Kadali R, Huang B. Multivariate control performance assessment without interactor matrix[C]. In: IFAC advanced control of chemical processes,2003.61-66.
64. Huang B, Steven X D, Thornhill N. Practical solutions to multivariate feedback control performance assessment problem:reduced a priori knowledge of interactor matrices[J]. Journal of Process Control,2005, 15(5):573-583.
65. Xia H, Majecki P. Performance assessment systems based on I/O delay.information [J]. Journal of Process Control,2006,16(4):373-383.
66. Huang B, Kadali R. Dynamic Modeling, Predictive Control and Performance Monitoring[M]:Springer, 2008.
67. Wang X R, Huang H, Chen T W. Multirate minimum variance control design and control performance assessment:a data-driven subspace approach[J]. IEEE Transactions on Control Systems Technology,2007, 15(1):65-74.
68. Yang H, LI S Y. Data-driven subspace approach to MIMO minimum variance control performance assessment[C]. In:Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China,2012
69. Ko B S, Edgar T F. Performance assessment of multivariable feedback control systems[J]. Automatica, 2001,37(6):899-905.
70. McNabb C A, Qin S J. Projection based MIMO control performance monitoring:Ⅰ—covariance monitoring in state space[J]. Journal of Process Control,2003,13(8):739-757.
71. Harris T. Interpretations of multivariate performance assessment indices[J]. Journal of Process Control, 2009,19(4):701-710.
72. Yuan Q L, Lennox b. Control performance assessment for multivariable systems based on a modified relative variance technique[J]. Journal of Process Control,2009,19(3):489-497.
73. Yu J, Qin S J. MIMO control performance monitoring using left/right diagonal interactors[J]. Journal of Process Control,2009,19(8):1267-1276.
74. Bezergianni S, Ozkan L. On the assessment of multivariable controllers using closed loop data. Part I: Identification of system models[J]. Journal of Process Control 2012,22(1):125-131.
75. Horch A, Isaksson A J. A modified index for control performance assessment[J]. Journal of Process Control,1999,9(6):475-483.
76. Kendra S, Cinar A. Controller performance assessment by frequency domain techniques[J]. Journal of Process Control,1997,7(3):181-194.
77. Bezergianni S, Georgakis C. Controller performance assessment based on minimum and open-loop output variance[J]. Control Engineering Practice,2000,8(7):791-797.
78. Gao J, Patwardhan R S, Akamatsu K, et al. Performance evaluation of two industrial MPC controllers[J]. Control Engineering Practice,2003,11(1):1371-1387.
79. Patwardhan R S, Shah S, Emoto G, et al. Performance analysis of model-based predictive controllers: An industrial study[C]. In:Proceedings of the AIChE. Miami, USA,1998
80. Thornhill N F, Oettinger M, Fedenczuk M S. Refinery-wide control loop performance assessment[J]. Journal of Process Control,1999,9(2):109-124.
81. Grimble M J. Controller performance benchmarking and tuning using generalized minimum variance control[J]. Automatica,2002,38(12):2111-2119.
82. Grimble M J, Majecki P. Weighting selection for controller benchmarking and tuning[EB/OL]. http://www.isc-ltd.com/benchmark/methods/pam-12-tn-1-vl.pdf.2004-12-06
83. Zhao Y, Su H Y, Chu J, et al. Multivariable control performance assessment based on generalized minimum variance benchmark[J]. Chinese Journal of Chemical Engineering,2010,18(1):86-94.
84.张彤,王庆林,张子迎.一类线性时变多变量系统的广义最小方差控制和性能评价[J].系统仿真学报,2008,20(13)：3521-3524.
85. Huang B, Shah S L. Performance assessment of control loops:Theory and Applications[M]. London: Springer Verlag,1999.
86. Kammer L, Bitmead R, Bartlett P. Signal-based testing of LQ-optimality of controllers. [C]. In: Proceedings of the 35th IEEE Conference on Decision and Control,1996.3620-3624
87. Kammer L, Bitmead R, Bartlett P. Optimal controller properties from closed-loop experiments[J]. Automatica,1998,34(1):83-91.
88. Kadali R, Huang B. Controller performance analysis with LQG benchmark obtained under closed loop conditions[J]. ISA Transactions,2002,41(4):521-537.
89. Kozub D J. Controller performance monitoring and diagnosis.Industrial perspective[C]. In: Proceedings of the IFAC world congress. Barcelona, Spain,2002
90. Dai C, Yang S H. Controller performance assessment with a LQG benchmark obtained by using the subspace method[C]. In:Proceedings of the Control. UK University of Bath, ID-066,2004
91. Zhao C, Zhao Y, Su H Y, et al. Economic performance assessment of advanced process control with LQG benchmarking[J]. Journal of Process Control,2009,19(4):557-569.
92. Danesh Pour N, Huang B, Shah S L. Performance assessment of advanced supervisory-regulatory control systems with subspace LQG benchmark[J]. Automatica,2010,46(8):1363-1368.
93. Eriksson P, Isaksson A J. Some aspects of control loop performance monitoring. [C]. In:Proceedings of the IEEE conference on control applications. Glasgow, Scotland,1994.1029-1034.
94. Ko B S, Edgar T F. Assessment of achievable PI control performance for linear processes with dead time[C]. In:Proceedings of the American Control Conference. Philadelphia, USA,1998
95. Ko B S, Edgar T F. PID control performance assessment:The single-loop case[J]. AIChE Journal,2004, 50(6):1211-1218.
96. Rivera D, Morari M. Low-order SISO controller tuning methods for the H2, H∞ and μ objective functions.[J]. Automatica,1990,26(2):361-369.
97. Grimble M J. Restricted structure control loop performance assessment for PID controllers and state-space systems[J]. Asian Journal of Control,2003,5(1):39-57.
98. Veronesi M, Visioli A. Performance assessment and retuning of PID controllers for integral processes[J]. Journal of Process Control,2010,20(3):261-269.
99. Yu Z P, Wang J D, Huang B, et al. Performance assessment of PID control loops subject to setpoint changes[J]. Journal of Process Control,2011,21(8):1164-1171.
100. Fu R W, Xie L, Song Z H, et al. PID control performance assessment using iterative convex programming[J]. Journal of Process Control,2012,22(9):1793-1799.
101.Julien R H, Foley M W, Cluett W R. Performance assessment using a model predictive control benchmark[J]. Journal of Process Control,2004,14(4):441-456.
102. Zhang Y, Henson M A. A performance measure for constrained model predictive controllers[C]. In: Proceedings of the European Control Conference. Karlsruhe, Germany,1999
103. Ko B S, Edgar T F. Performance assessment of constrained model predictive control systems[J]. AIChE Journal,2001,47(6):1363-1371.
104. Dumont G A, Kammer L, Allison B J, et al. Control performance monitoring:New developments and practical issues[C]. In:Proceedings of the IFAC World Congress. Barcelona, Spain,2002
105. Kozub D J. Controller performance monitoring and diagnosis:Experiences and challenges[C]. In: Proceedings of the Chemical Process Control Conference. Lake Tahoe, USA,1996.83-96.
106. Kesavan P, Lee J H. Diagnostic tools for multivariable model-based control systems[J]. Industrial and Engineering Chemistry Research,1997,36(7):2725-2738.
107. Xia C, Howell J. Loop status monitoring and fault localization[J]. Journal of Process Control,2003, 13(7):679-691.
108. Swanda A, Seborg D E. Controller performance assessment based on setpoint response data[C]. In: Proceedings of the American Control Conference. San Diego, USA,1999.3863-3867.
109. Ingimundarson A. Performance assessment using a normalized gradient[J]. Journal of Process Control, 2006,16(10):1013-1020.
110. Harris T J, Seppala C T, Jofriet P J, et al. Plant-wide feedback control performance assessment using an expert system framework [J]. Control Engineering Practice,1996,4(9):1297-1303.
111.Joronen T. A simple fuzzy statistical evaluation for process control performance[C]. In:IEEE International Conference on Artificial Intelligence Systems,2002
112.苑明哲,于海斌,王宏等.控制性能评估[J].吉林大学学报,2004,第15届中国过程控制会议专辑：80-83.
113.刘春雨,王庆林.控制性能评价与监控理论的研究进展[J].火力与指挥控制,2009,34(4)：1-5.
114.傅若玮,宋执环.控制系统性能评估的研究现状与展望[J].华中科技大学学报,2009,增刊：226-229.
115.薛美盛,Fei Q,张毅等.控制回路性能评估综述[J].控制工程,2009,16(5)：507-511.
116.张彤,王庆林.前馈反馈线性时变多变量控制系统性能评价[J].信息与控制,2007,36(6)： 720-726.
117.张学莲,胡立生,曹广益.鲁棒模型预测控制系统的评估基准[J].化工学报,2008,59(7)：1859-1862.
118.施文俊.一类非线性系统的性能评估的研究[D]:[PhD thesis]上海：上海交通大学,2009.
119.张登峰,赵宇,赵超等.一类变时延时变过程的控制方差性能评估[J].控制理论和应用,2009,26(9)：1041-1045.
120.刘小燕,张泉灵,苏宏业.PID控制器的性能监控与评估[J].计算机与应用化学,2010,27(1)：91-94.
121.王印松,张彩.基于设定值响应的PI控制系统性能评估[J].控制工程,2007,14(1)：31-33.
122.周猛飞,王树青,金晓明.加热炉先进控制系统经济性能评估[J].计算机与应用化学,2010,27(1)：82-86.
123.王宇红,王学剑.基于协方差基准的模型预测控制性能评价与监视[J].信息与控制,2010,39(6)：694-699.
124.张亮,郑建荣,夏春明.过程控制系统中振荡检测与诊断技术进展[J].化工自动化及仪表,2011,38(9)：1037-1042.
125.林晓钟,谢磊.基于数据驱动的多工况过程控制性能评估研究[J].哈尔滨工业大学学报,2012,44(11)：81-86.
126. Qi F, Huang B. Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences[J]. Automatica,2011,47(7):1349-1356.
127. Harris T J, Yu W. Controller assessment for a class of non-linear systems[J]. Journal of Process Control,2007,17(7):607-619.
128. Chen J H, Kong C K. Performance assessment for iterative learning control of batch units[J]. Journal of Process Control,2009,19(6):1043-1053.
129. Yu W, Wilson D I, Young B R. Control performance assessment for nonlinear systems [J]. Journal of Process Control,2010,20(10):1235-1242.
130. Bjorklund S. A survey and comparison of time delay estimation methods in linear systems[D]:[PhD thesis]. Lund Institute of Technology, Sweden,2003.
131. Bjorklund S, Ljung L. A review of time-delay estimation techniques [C]. In:Proceedings of the 42nd IEEE Conference on Decision and Control. Maui, Hawaii USA,2003,2502-2507.
132. Isaksson M. A comparison of some approaches to time-delay estimation[D]:[PhD thesis]. Stockholm: Royal Institute of Technology, Sweden,1997.
133. Wahlberg B. System Identification using Laguerre Models[J]. IEEE Transactions on Automatic Control,1991,36(5):551-562.
134. Isaksson A J, Horch A, Dumont G A. Event-triggered deadtime estimation from closed-loop data[C]. In:Proceedings of the American Control Conference,2001,3280-3285.
135. Elnaggar A, Dumont G A, Elshafei A L. Delay estimation using variable regression[C]. In: Proceedings of the American Control Conference. Boston, USA,1991,2812-2817.
136. Isaksson M, Horch A, Dumont G A. Event-triggered deadtime estimation—Comparison of methods[C]. In:Proceedings of the Control Systems. Halifax, Canada,2000,171-178.
137. Wu Y. Time delay estimation of non-Gaussian signal in unknown Gaussian noises using third-order cumulants[J]. IEEE Electronics letters,2002,38(16):930-931.
138.王昭,赵俊渭,陈钟.一种空间相关高斯噪声背景下的时变时延估计算法[J].电子与信息学报,2003,25(6)：728-734.
139. Hinich M J, Wilson G R. Time delay estimation using the cross bispectrum[J]. IEEE Transactions on Signal Processing,1992,40(1):106-113.
140. LIANG Y, Leyman A R. Time delay estimation using higher order statistics[J]. IEEE Electronics letters,1997,33(9):751-753.
141.丁锋.系统辨识新论[M].北京：科学出版社,2013.
142. Leontaritis I J, Billings S A. Input-output parametric models for nonlinear systems,part I: deterministic nonlinear systems[J]. International Journal of Control,1985,41(4):303-328.
143. Leontaritis I J, Billings S A. Input-output parametric models for nonlinear systems, part Ⅱ:stochastic nonlinear systems[J]. International Journal of Control,1985,41(4):329-344.
144. Korenberg M J, Billings S A, Liu Y P, et al. Orthogonal parameter estimation algorithm for non-linear stochastic systems[J]. International Journal of Control,1988,48(1):193-210.
145. Billings S A, Chen S, Korenverg M J. Identification of MIMO nonlinear systems using a forward-regression orthogonal estimator[J]. International Journal of Control,1989,49:2157-2189.
146. Chen S, Billings S A, Luo W. Orthogonal least squares methods and their application to nonlinear system identification[J]. International Journal of Control,1989,50(5):1873-1896.
147. Chen S, Billings S A. Representations of nonlinear systems:the NARMAX model [J]. International Journal of Control,1989,49(3):1013-1032.
148. Sales K R, Billings S A. Self-tuning control of non-linear ARMAX models[J]. International Journal of Control,1990,51(4):753-769.
149. Grimble M J. Non-linear generalized minimum variance feedback, feedforward and tracking control [J]. Automatica,2005,41(6):957-969.
150. Tong H. Nonlinear time series[M]. New York:Oxford University Press,1990.
151. Hernandez E, Arkun Y. Stability of nonlinear polynomial ARMA models and their inverse[J]. International Journal of Control,1996,63(5):885-906.
152. Maner B R, Doyle Ⅲ F J. Polymerization reactor control using autoregressive-plus Volterra-based MPC[J]. American Institute Chemical Engineering Journal,1997,43(7):1763-1784.
153. Delport R. Process Identification Using Second Order Volterra Models for Nonlinear Model Predictive Control Design of Flotation Circuits[D]:[PhD thesis]. University of Pretoria,2004.
154. Abonyi J, Babuka R, Ayala B M, et al. Identification and control of nonlinear systems using fuzzy Hammerstein models[J]. Industrial and Engineering Chemistry Research,2000,39(11):4302-4314.
155. Fruzzetti K P, Palazoglu A, McDonald K A. Nonlinear model predictive control using Hammerstein models[J]. Journal of Process Control,1997,7(1):31-41.
156. Gomez J C, Baeyens E. Identification of block-oriented nonlinear systems using orthonormal bases[J]. Journal of Process Control,2004,14(6):685-697.
157.欧文,韩崇昭Volterra泛函级数辨识中维数灾难的一种解决方法[J].西安交通大学学报,2001,35(6)：658-660.
158.Bittanti S, Piroddi L. Minimum variance control of a class of nonlinear plants with neural networks[C]. In:Third International Conference on Artificial Neural Networks. Politecnicodi Milano, Italy,1993, 168-172.
159.Yu W, Wilson D I, Young B R. Control performance assessment in the presence of valve stiction[C]. In: The Eleventh IASTED International Conference on Intelligent Systems and Control, ISC 2008, Orlando, FL, USA,2008.379-384.
160. Yu W, Wilson D I, Young B R. Eliminating valve stiction nonlinearities for control performance assessment,[C]. In:International Symposium on Advanced Control of Chemical Processes,2009. Istanbul, Turkey,2009.526-531.
161. Yu W, Wilson D I, Young B R. Nonlinear control performance assessment in the presence of valve stiction[J]. Journal of Process Control,2010,20(6):754-761.
162. Yu W, Wilson D I, Young B R. Control performance assessment for block-oriented nonlinear systems[C]. In:IEEE International Conference on Control and Automation. Xiamen, China,2010, 1151-1156.
163. Doyle Ⅲ F J, Pearson R K, Ogunnaike B A. Identification and control using volterra models[M]. London:Springer,2002.
164. Diaz H, Desrochers A A. Modeling of nonlinear discrete-time systems from input-output data[J]. Automatica,1988,24(5):629-641.
165. Haber R, Unbehauen H. Structural identification of nonlinear dynamic systems—a survey on input/output approaches [J]. Automatica 1990,26(4):651-677.
166. Archer G E B, Saltelli A, Sobol I M. Sensitivity measures, Anova-like techniques and the use of bootstrap[J]. Journal of Statistical Computations and Simulations,1997,58(2):99-120.
167. Saltelli A K C, Scott E M. Sensitivity analysis[M]. New York:Wiley,2000.
168. Sobol I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates[J]. Mathematics and Computer in Simulation,2001,55(1):271-280.
169. Yu W, Wilson D I, Young B R, et al. Variance decomposition of nonlinear systems[C]. In:IEEE International Conference on Control and Automation. Christchurch, New Zealand,2009.738-744.
170. Harris T J, Yu W. Variance decompositions of nonlinear dynamic stochastic systems[J]. Journal of Process Control,2010,20(2):195-205.
171. Grimble M J. Generalized minimum variance control law revisited[J]. Optimal Control Applications and Methods,1988,9(1):63-77.
172. Majecki P, Grimble M J. Controller prformance design and assessment using nonlinear generalized minimum variance benchmark:scalar case[C]. In:Control 2004. University of Bath, UK,2004
173.方兆本,缪柏其.随机过程[M].北京：科学出版社,2011.
174. Chon K H, Korenberg M J, Holstein-Rathlou N H. Application of fast orthogonal search to linear and nonlinear stochastic systems[J]. Annals of Biomedical Engineering,1997,25(5):793-801.
175. Korenberg M J. Identifying nonlinear difference equation and functional expansion representations: the fast orthogonal algorithm[J]. Annals of Biomedical Engineering,1988,16(1):123-142.
176. Terasvirta T, Digk D V, Medeiros M C. Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series:A re-examination[J]. International Journal of Forecasting 2005,21(4):755-774.
177. Chen S, Billings S A. Neural networks for nonlinear dynamic system modeling and identification [J]. International Journal of Control,1992,56(2):319-346.
178. Sternad M, Soderstrom T. LQG-optimal feedforward regulators [J]. Automatica,1988,24(4):557-561.
179. Folake B O, Huang B, Edgar T. Feedforward and feedback controller performance assessment of linear time-variant processes[J]. Industrial and Engineering Chemistry Research,2004,43(2):589-596.
180. Fan J, Yao Q. Nonlinear time series:nonparametric and parametric methods[M]. New York:Springer Series in Statistics,2005.
2. Huang B. Bayesian methods for control loop monitoring and diagnosis[J]. Journal of Process Control, 2008,18(9):829-838.
3. Thornhill N F, Hagglund T. Detection and diagnosis of oscillation in control loops [J]. Control Engineering Practice,1997,5(10):1343-1354.
4. Schafer J, Cinar A. Multivariable MPC performance assessment, monitoring and diagnosis[C]. In: Proceedings of the IFAC World Congress. Barcelona, Spain,2002
5. Haber R, Keviczky L. Nonlinear system identification,Input-output modelling approach [M]. Dordrecht:Kluwer Academic Publishers,1999.
6. Rao T S, Gabr M M. A test for linearity and stationarity of time series.[J]. Journal of Time Series Analysis,1980,1(1):145-158.
7. Hinich, Melvin J. Testing for gaussianity and linearity of a stationary time series[J]. Journal of time series analysis,1982,3(3):169-176.
8. Fackrell J W A. Bispectral analysis of speech signals[D]:[PhD thesis]. The University of Edinburgh, UK,1996.
9. Yuan J. Testing linearity for stationary time series using the sample interquartile range[J]. Journal of Time Series Analysis,1999,21(6):713-722.
10. Choudhury M A A S, Shaha S L, Thornhillb N F. Diagnosis of poor control-loop performance using higher-order statistics[J]. Automatica,2004,40(10):1719-1728.
11. Hagglund T. A control-loop performance monitor[J]. Control Engineering Practice,1995,3(11): 1543-1551.
12. Forsman K, Stattin A. A new criterion for detecting oscillations in control loops[C]. In:Proceedings of the European control conference. Karlsruhe, Germany,1999
13. Mia T, Seborg D E. Automatic detection of excessively oscillatory feedback control loops[C]. In: Proceedings of the IEEE Conference on Control Applications. Kohala Coast-Island, USA,1999
14. Ettaleb L. Control loop performance assessment and oscillation detection[D]:[PhD thesis]. University of British Columbia, Canada,1999.
15. Ettaleb L, Davies M S, Dumont G A, et al. Monitoring oscillations in a multiloop system[C]. In: Proceedings of the IEEE Conference on Control Applications. Dearborn, USA,1996.859-863.
16. Thornhill N F, Shah S L, Huang B. Detection of distributed oscillations and root-cause diagnosis[C]. In:Proceedings of the 4th IFAC Workshop on On-line Fault Detection and Super Vision in the Chemical Process Industries. Chejudo Island, Korea,2001.167-172.
17. Thornhill N F, Huang B, Zhang H. Detection of multiple oscillations in control loops[J]. Journal of Process Control,2003,13(1):91-100.
18. Horch A. A simple method for detection of stiction in control valves[J]. Control Engineering Practice, 1999,7(10):1221-1231.
19. Kano M, Maruta H, Kugemoto H, ef al. Practical model and detection algorithm for valve stiction[C]. In:Proceedings of IFAC symposium on Dynamics and Control of Process Systems. Cambridge,2004.
20. Singhal A, Salsbury T I.A simple method for detecting valve stiction in oscillating control loops[J]. Journal of Process Control,2005,15(4):371-382.
21. Yoshiyuki Y. An automatic method for detection of valve stiction in process control loops [J]. Control Engineering Practice,2006,14(5):503-510.
22. Jelali M. Estimation of valve stiction in control loops using separable least-squares and global search algorithms [J]. Journal of Process Control,2008,18(8):632-642.
23. Zabiri H, Ramasamy M. NLPCA as a diagnostic tool for control valve stiction[J]. Journal of Process Control,2009,19(8):1368-1376.
24. Saneej B, Chitralekha, Shah S L, et al. Detection and quantification of valve stiction by the method of unknown input estimation [J]. Journal of Process Control,2010,20(2):206-216.
25. Hagglund T. A shape-analysis approach for diagnosis of stiction in control valves [J]. Control Engineering Practice,2011,19(8):782-789.
26. Ale Mohammad M, Huang B. Compensation of control valve stiction through controller tuning [J]. Journal of Process Control,2012,22(9):1800-1819.
27. Hagglund T. Automatic detection of sluggish control loops[J]. Control Engineering Practice,1999, 7(12):1505-1511.
28. Kuehl P, Horch A. Detection of sluggish control loops-experiences and improvements [J]. Control Engineering Practice,2005,13(8):1019-1025.
29. Gerry J, Ruel M. How to measure and combat valve stiction on line[C]. In:Proceedings of the ISA. Houston, TX,2001
30. Chien I L, Fruehauf P S. Consider IMC tuning to improve controller performance[J]. Chemical Engineering Progress,1990,86:33-41.
31. Chen D, Seborg D E. PI/PID controller design based on direct synthesis and disturbance rejection[J]. Industrial and Engineering Chemistry Research,2002,41(19):4807-4822.
32. Skogestad S. Simple analytic rules for model reduction and PID controller tuning[J]. Journal of Process Control,2003,13(4):291-309.
33. DeVries W, Wu S. Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate timeseries analysis[J]. IEEE Transactions on Automatic Control,1978,23(4): 702-708.
34. Harris T J. Assessment of control loop performance[J]. Canadian Journal of Chemical Engineering, 1989,67(5):856-861.
35. Desborough L D, Harris T J. Performance assessment measures for univariate feedback control[J]. The Canadian Journal of Chemical Engineering,1992,70(6):1186-1197.
36. Tyler M, Morari M. Performance assessment for unstable and nonminimum-phase systems[C]. In: Preprints IFAC Workshop On-line Fault Detection Supervision Chemical Process Industries. Newcastle upon Tyne,UK,1995
37. Tyler M, Morari M. Performance monitoring of control systems using likelihood methods[J]. Automatica,1996,32(8):1145-1162.
38. Lynch C B, Dumont G A. Control loop performance monitoring[J]. IEEE Transactions on Control Technology,1996,4(2):185-192.
39. Qin S J. Control performance monitoring-a review and assessment[J]. Computers and Chemical Engineering,1998,23(2):173-186.
40. Perrier M, Roche A A. Towards mill-wide evaluation of control loop performance[C]. In:Proceedings of the Control Systems. Whistler, Canada,1992.205-209.
41. Ko B S, Edgar T F. Performance assessment of cascade control loops[J]. AIChE Journal,2000,46(2): 281-291.
42. Seppala C T, Harris T J, Bacon D W. Time series methods for dynamic analysis of multiple controlled variables[J]. Journal of Process Control,2002,12(2):257-276.
43. Thornhill N F, Huang B, Shah S L. Controller performance assessment in set point tracking and regulatory control[J]. International Journal of Adaptive Control and Signal Processing,2003,17(7): 709-727.
44. McNabb C A, Qin S J. Projection based MIMO control performance monitoring:Ⅱ-Measured disturbances and setpoint changes [J]. Journal of Process Control,2005,15(1):89-102.
45. Li Z, Evans R J. Minimum-variance control of linear time varying systems[J]. Automatica,1997, 33(8):1531-1537.
46. Huang B. Performance assessment of processes with abrupt changes of disturbances[J]. Canadian Journal of Chemical Engineering,1999,77(5):1044-1054.
47. Huang B. Minimum variance control and performance assessment of time variant processes[J]. Journal of Process Control,2002,12(6):707-719.
48. Olaleye F, Huang B, Tamayo E. Performance assessment of control loops with time varying disturbance dynamics[J]. Journal of Process Control,2004,14(8):867-877.
49. Olaleye F, Huang B, Tamayo E. Feedforward and feedback controller performance assessment of linear time-variant processes[J]. Industrial and Engineering Chemistry Research,2004,43(2):589-596.
50. Olaleye F, Huang B, Tamayo E. Industrial applications of feedback controller performance assessment of time-variant processes[J]. Industrial and Engineering Chemistry Research,2004,43(2):597-607.
51. Xu F W, Huang B, Tamayo E C. Performance assessment of MIMO control systems with time-variant disturbance dynamics[J]. Computers and Chemical Engineering,2008,32(9):2144-2154.
52. Desborough L, Harris T. Performance assessment measures for univariate feedforward/feedback control[J]. Canadian Journal of Chemical Engineering,1993,71(4):605-616.
53. Stanfelj N, Marlin T, MacGregor J. Monitoring and diagnosing process control performance:The single loop case[J]. Industrial and Engineering Chemistry Research,1993,32(2):301-314.
54. Chen J H, Yea Y Z. Assessment and diagnosis of feedforward/feedback control system[C]. In: American Control Conference. Portland, OR, USA,2005.490-495.
55. Huang B, Shah S L, Miller R. Feedforward plus Feedback Controller Performance Assessment of MIMO systems [J]. IEEE Transactions on Control Systems Technology,2000,8(3):580-587.
56. Harris T, Boudreau F, MacGregor J F. Performance assessment using of multivariable feedback controllers [J]. Automatica,1996,32(11):1505-1518.
57. Rogozinski M W, Paplinski A P, Gibbard M J. An algorithm for the calculation of a nilpotent interactor matrix for linear multivariable systems[J]. IEEE transactions on Automatic Control,1987,32(3):234-237.
58. Huang B, Shah S L, Kwok E K. Good, bad or optimal?Performance assessment of multivariable processes[J]. Automatica,1997,33(6):1175-1183.
59. Kamrunnahar M, Huang B, Fisher D G. Estimation of Markov parameters and timedelay interactormatrix [J]. Chemical Engineering Science,2000,55(17):3353-3363.
60. Kamrunnahar M, Fisher D G, Huang B. Performance assessment and robustness analys using an ARMarkov approach[J]. Journal of Process Control 2004,14(8):915-925.
61. Harris T J, Seppala C T, Desborough L D. A review of performance monitoring and assessment techniques for univariate and multivariate control systems[J]. Journal of Process Control,1999,9(1):1-17.
62. Shah S L, Patwardhan R, Huang B. Multivariate controller performance assessment:methods, applications and challenges[C]. In:Chemical Process Control-CPC. Arizona, US,2002.190-207.
63. Kadali R, Huang B. Multivariate control performance assessment without interactor matrix[C]. In: IFAC advanced control of chemical processes,2003.61-66.
64. Huang B, Steven X D, Thornhill N. Practical solutions to multivariate feedback control performance assessment problem:reduced a priori knowledge of interactor matrices[J]. Journal of Process Control,2005, 15(5):573-583.
65. Xia H, Majecki P. Performance assessment systems based on I/O delay.information [J]. Journal of Process Control,2006,16(4):373-383.
66. Huang B, Kadali R. Dynamic Modeling, Predictive Control and Performance Monitoring[M]:Springer, 2008.
67. Wang X R, Huang H, Chen T W. Multirate minimum variance control design and control performance assessment:a data-driven subspace approach[J]. IEEE Transactions on Control Systems Technology,2007, 15(1):65-74.
68. Yang H, LI S Y. Data-driven subspace approach to MIMO minimum variance control performance assessment[C]. In:Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China,2012
69. Ko B S, Edgar T F. Performance assessment of multivariable feedback control systems[J]. Automatica, 2001,37(6):899-905.
70. McNabb C A, Qin S J. Projection based MIMO control performance monitoring:Ⅰ—covariance monitoring in state space[J]. Journal of Process Control,2003,13(8):739-757.
71. Harris T. Interpretations of multivariate performance assessment indices[J]. Journal of Process Control, 2009,19(4):701-710.
72. Yuan Q L, Lennox b. Control performance assessment for multivariable systems based on a modified relative variance technique[J]. Journal of Process Control,2009,19(3):489-497.
73. Yu J, Qin S J. MIMO control performance monitoring using left/right diagonal interactors[J]. Journal of Process Control,2009,19(8):1267-1276.
74. Bezergianni S, Ozkan L. On the assessment of multivariable controllers using closed loop data. Part I: Identification of system models[J]. Journal of Process Control 2012,22(1):125-131.
75. Horch A, Isaksson A J. A modified index for control performance assessment[J]. Journal of Process Control,1999,9(6):475-483.
76. Kendra S, Cinar A. Controller performance assessment by frequency domain techniques[J]. Journal of Process Control,1997,7(3):181-194.
77. Bezergianni S, Georgakis C. Controller performance assessment based on minimum and open-loop output variance[J]. Control Engineering Practice,2000,8(7):791-797.
78. Gao J, Patwardhan R S, Akamatsu K, et al. Performance evaluation of two industrial MPC controllers[J]. Control Engineering Practice,2003,11(1):1371-1387.
79. Patwardhan R S, Shah S, Emoto G, et al. Performance analysis of model-based predictive controllers: An industrial study[C]. In:Proceedings of the AIChE. Miami, USA,1998
80. Thornhill N F, Oettinger M, Fedenczuk M S. Refinery-wide control loop performance assessment[J]. Journal of Process Control,1999,9(2):109-124.
81. Grimble M J. Controller performance benchmarking and tuning using generalized minimum variance control[J]. Automatica,2002,38(12):2111-2119.
82. Grimble M J, Majecki P. Weighting selection for controller benchmarking and tuning[EB/OL]. http://www.isc-ltd.com/benchmark/methods/pam-12-tn-1-vl.pdf.2004-12-06
83. Zhao Y, Su H Y, Chu J, et al. Multivariable control performance assessment based on generalized minimum variance benchmark[J]. Chinese Journal of Chemical Engineering,2010,18(1):86-94.
84.张彤,王庆林,张子迎.一类线性时变多变量系统的广义最小方差控制和性能评价[J].系统仿真学报,2008,20(13)：3521-3524.
85. Huang B, Shah S L. Performance assessment of control loops:Theory and Applications[M]. London: Springer Verlag,1999.
86. Kammer L, Bitmead R, Bartlett P. Signal-based testing of LQ-optimality of controllers. [C]. In: Proceedings of the 35th IEEE Conference on Decision and Control,1996.3620-3624
87. Kammer L, Bitmead R, Bartlett P. Optimal controller properties from closed-loop experiments[J]. Automatica,1998,34(1):83-91.
88. Kadali R, Huang B. Controller performance analysis with LQG benchmark obtained under closed loop conditions[J]. ISA Transactions,2002,41(4):521-537.
89. Kozub D J. Controller performance monitoring and diagnosis.Industrial perspective[C]. In: Proceedings of the IFAC world congress. Barcelona, Spain,2002
90. Dai C, Yang S H. Controller performance assessment with a LQG benchmark obtained by using the subspace method[C]. In:Proceedings of the Control. UK University of Bath, ID-066,2004
91. Zhao C, Zhao Y, Su H Y, et al. Economic performance assessment of advanced process control with LQG benchmarking[J]. Journal of Process Control,2009,19(4):557-569.
92. Danesh Pour N, Huang B, Shah S L. Performance assessment of advanced supervisory-regulatory control systems with subspace LQG benchmark[J]. Automatica,2010,46(8):1363-1368.
93. Eriksson P, Isaksson A J. Some aspects of control loop performance monitoring. [C]. In:Proceedings of the IEEE conference on control applications. Glasgow, Scotland,1994.1029-1034.
94. Ko B S, Edgar T F. Assessment of achievable PI control performance for linear processes with dead time[C]. In:Proceedings of the American Control Conference. Philadelphia, USA,1998
95. Ko B S, Edgar T F. PID control performance assessment:The single-loop case[J]. AIChE Journal,2004, 50(6):1211-1218.
96. Rivera D, Morari M. Low-order SISO controller tuning methods for the H2, H∞ and μ objective functions.[J]. Automatica,1990,26(2):361-369.
97. Grimble M J. Restricted structure control loop performance assessment for PID controllers and state-space systems[J]. Asian Journal of Control,2003,5(1):39-57.
98. Veronesi M, Visioli A. Performance assessment and retuning of PID controllers for integral processes[J]. Journal of Process Control,2010,20(3):261-269.
99. Yu Z P, Wang J D, Huang B, et al. Performance assessment of PID control loops subject to setpoint changes[J]. Journal of Process Control,2011,21(8):1164-1171.
100. Fu R W, Xie L, Song Z H, et al. PID control performance assessment using iterative convex programming[J]. Journal of Process Control,2012,22(9):1793-1799.
101.Julien R H, Foley M W, Cluett W R. Performance assessment using a model predictive control benchmark[J]. Journal of Process Control,2004,14(4):441-456.
102. Zhang Y, Henson M A. A performance measure for constrained model predictive controllers[C]. In: Proceedings of the European Control Conference. Karlsruhe, Germany,1999
103. Ko B S, Edgar T F. Performance assessment of constrained model predictive control systems[J]. AIChE Journal,2001,47(6):1363-1371.
104. Dumont G A, Kammer L, Allison B J, et al. Control performance monitoring:New developments and practical issues[C]. In:Proceedings of the IFAC World Congress. Barcelona, Spain,2002
105. Kozub D J. Controller performance monitoring and diagnosis:Experiences and challenges[C]. In: Proceedings of the Chemical Process Control Conference. Lake Tahoe, USA,1996.83-96.
106. Kesavan P, Lee J H. Diagnostic tools for multivariable model-based control systems[J]. Industrial and Engineering Chemistry Research,1997,36(7):2725-2738.
107. Xia C, Howell J. Loop status monitoring and fault localization[J]. Journal of Process Control,2003, 13(7):679-691.
108. Swanda A, Seborg D E. Controller performance assessment based on setpoint response data[C]. In: Proceedings of the American Control Conference. San Diego, USA,1999.3863-3867.
109. Ingimundarson A. Performance assessment using a normalized gradient[J]. Journal of Process Control, 2006,16(10):1013-1020.
110. Harris T J, Seppala C T, Jofriet P J, et al. Plant-wide feedback control performance assessment using an expert system framework [J]. Control Engineering Practice,1996,4(9):1297-1303.
111.Joronen T. A simple fuzzy statistical evaluation for process control performance[C]. In:IEEE International Conference on Artificial Intelligence Systems,2002
112.苑明哲,于海斌,王宏等.控制性能评估[J].吉林大学学报,2004,第15届中国过程控制会议专辑：80-83.
113.刘春雨,王庆林.控制性能评价与监控理论的研究进展[J].火力与指挥控制,2009,34(4)：1-5.
114.傅若玮,宋执环.控制系统性能评估的研究现状与展望[J].华中科技大学学报,2009,增刊：226-229.
115.薛美盛,Fei Q,张毅等.控制回路性能评估综述[J].控制工程,2009,16(5)：507-511.
116.张彤,王庆林.前馈反馈线性时变多变量控制系统性能评价[J].信息与控制,2007,36(6)： 720-726.
117.张学莲,胡立生,曹广益.鲁棒模型预测控制系统的评估基准[J].化工学报,2008,59(7)：1859-1862.
118.施文俊.一类非线性系统的性能评估的研究[D]:[PhD thesis]上海：上海交通大学,2009.
119.张登峰,赵宇,赵超等.一类变时延时变过程的控制方差性能评估[J].控制理论和应用,2009,26(9)：1041-1045.
120.刘小燕,张泉灵,苏宏业.PID控制器的性能监控与评估[J].计算机与应用化学,2010,27(1)：91-94.
121.王印松,张彩.基于设定值响应的PI控制系统性能评估[J].控制工程,2007,14(1)：31-33.
122.周猛飞,王树青,金晓明.加热炉先进控制系统经济性能评估[J].计算机与应用化学,2010,27(1)：82-86.
123.王宇红,王学剑.基于协方差基准的模型预测控制性能评价与监视[J].信息与控制,2010,39(6)：694-699.
124.张亮,郑建荣,夏春明.过程控制系统中振荡检测与诊断技术进展[J].化工自动化及仪表,2011,38(9)：1037-1042.
125.林晓钟,谢磊.基于数据驱动的多工况过程控制性能评估研究[J].哈尔滨工业大学学报,2012,44(11)：81-86.
126. Qi F, Huang B. Bayesian methods for control loop diagnosis in the presence of temporal dependent evidences[J]. Automatica,2011,47(7):1349-1356.
127. Harris T J, Yu W. Controller assessment for a class of non-linear systems[J]. Journal of Process Control,2007,17(7):607-619.
128. Chen J H, Kong C K. Performance assessment for iterative learning control of batch units[J]. Journal of Process Control,2009,19(6):1043-1053.
129. Yu W, Wilson D I, Young B R. Control performance assessment for nonlinear systems [J]. Journal of Process Control,2010,20(10):1235-1242.
130. Bjorklund S. A survey and comparison of time delay estimation methods in linear systems[D]:[PhD thesis]. Lund Institute of Technology, Sweden,2003.
131. Bjorklund S, Ljung L. A review of time-delay estimation techniques [C]. In:Proceedings of the 42nd IEEE Conference on Decision and Control. Maui, Hawaii USA,2003,2502-2507.
132. Isaksson M. A comparison of some approaches to time-delay estimation[D]:[PhD thesis]. Stockholm: Royal Institute of Technology, Sweden,1997.
133. Wahlberg B. System Identification using Laguerre Models[J]. IEEE Transactions on Automatic Control,1991,36(5):551-562.
134. Isaksson A J, Horch A, Dumont G A. Event-triggered deadtime estimation from closed-loop data[C]. In:Proceedings of the American Control Conference,2001,3280-3285.
135. Elnaggar A, Dumont G A, Elshafei A L. Delay estimation using variable regression[C]. In: Proceedings of the American Control Conference. Boston, USA,1991,2812-2817.
136. Isaksson M, Horch A, Dumont G A. Event-triggered deadtime estimation—Comparison of methods[C]. In:Proceedings of the Control Systems. Halifax, Canada,2000,171-178.
137. Wu Y. Time delay estimation of non-Gaussian signal in unknown Gaussian noises using third-order cumulants[J]. IEEE Electronics letters,2002,38(16):930-931.
138.王昭,赵俊渭,陈钟.一种空间相关高斯噪声背景下的时变时延估计算法[J].电子与信息学报,2003,25(6)：728-734.
139. Hinich M J, Wilson G R. Time delay estimation using the cross bispectrum[J]. IEEE Transactions on Signal Processing,1992,40(1):106-113.
140. LIANG Y, Leyman A R. Time delay estimation using higher order statistics[J]. IEEE Electronics letters,1997,33(9):751-753.
141.丁锋.系统辨识新论[M].北京：科学出版社,2013.
142. Leontaritis I J, Billings S A. Input-output parametric models for nonlinear systems,part I: deterministic nonlinear systems[J]. International Journal of Control,1985,41(4):303-328.
143. Leontaritis I J, Billings S A. Input-output parametric models for nonlinear systems, part Ⅱ:stochastic nonlinear systems[J]. International Journal of Control,1985,41(4):329-344.
144. Korenberg M J, Billings S A, Liu Y P, et al. Orthogonal parameter estimation algorithm for non-linear stochastic systems[J]. International Journal of Control,1988,48(1):193-210.
145. Billings S A, Chen S, Korenverg M J. Identification of MIMO nonlinear systems using a forward-regression orthogonal estimator[J]. International Journal of Control,1989,49:2157-2189.
146. Chen S, Billings S A, Luo W. Orthogonal least squares methods and their application to nonlinear system identification[J]. International Journal of Control,1989,50(5):1873-1896.
147. Chen S, Billings S A. Representations of nonlinear systems:the NARMAX model [J]. International Journal of Control,1989,49(3):1013-1032.
148. Sales K R, Billings S A. Self-tuning control of non-linear ARMAX models[J]. International Journal of Control,1990,51(4):753-769.
149. Grimble M J. Non-linear generalized minimum variance feedback, feedforward and tracking control [J]. Automatica,2005,41(6):957-969.
150. Tong H. Nonlinear time series[M]. New York:Oxford University Press,1990.
151. Hernandez E, Arkun Y. Stability of nonlinear polynomial ARMA models and their inverse[J]. International Journal of Control,1996,63(5):885-906.
152. Maner B R, Doyle Ⅲ F J. Polymerization reactor control using autoregressive-plus Volterra-based MPC[J]. American Institute Chemical Engineering Journal,1997,43(7):1763-1784.
153. Delport R. Process Identification Using Second Order Volterra Models for Nonlinear Model Predictive Control Design of Flotation Circuits[D]:[PhD thesis]. University of Pretoria,2004.
154. Abonyi J, Babuka R, Ayala B M, et al. Identification and control of nonlinear systems using fuzzy Hammerstein models[J]. Industrial and Engineering Chemistry Research,2000,39(11):4302-4314.
155. Fruzzetti K P, Palazoglu A, McDonald K A. Nonlinear model predictive control using Hammerstein models[J]. Journal of Process Control,1997,7(1):31-41.
156. Gomez J C, Baeyens E. Identification of block-oriented nonlinear systems using orthonormal bases[J]. Journal of Process Control,2004,14(6):685-697.
157.欧文,韩崇昭Volterra泛函级数辨识中维数灾难的一种解决方法[J].西安交通大学学报,2001,35(6)：658-660.
158.Bittanti S, Piroddi L. Minimum variance control of a class of nonlinear plants with neural networks[C]. In:Third International Conference on Artificial Neural Networks. Politecnicodi Milano, Italy,1993, 168-172.
159.Yu W, Wilson D I, Young B R. Control performance assessment in the presence of valve stiction[C]. In: The Eleventh IASTED International Conference on Intelligent Systems and Control, ISC 2008, Orlando, FL, USA,2008.379-384.
160. Yu W, Wilson D I, Young B R. Eliminating valve stiction nonlinearities for control performance assessment,[C]. In:International Symposium on Advanced Control of Chemical Processes,2009. Istanbul, Turkey,2009.526-531.
161. Yu W, Wilson D I, Young B R. Nonlinear control performance assessment in the presence of valve stiction[J]. Journal of Process Control,2010,20(6):754-761.
162. Yu W, Wilson D I, Young B R. Control performance assessment for block-oriented nonlinear systems[C]. In:IEEE International Conference on Control and Automation. Xiamen, China,2010, 1151-1156.
163. Doyle Ⅲ F J, Pearson R K, Ogunnaike B A. Identification and control using volterra models[M]. London:Springer,2002.
164. Diaz H, Desrochers A A. Modeling of nonlinear discrete-time systems from input-output data[J]. Automatica,1988,24(5):629-641.
165. Haber R, Unbehauen H. Structural identification of nonlinear dynamic systems—a survey on input/output approaches [J]. Automatica 1990,26(4):651-677.
166. Archer G E B, Saltelli A, Sobol I M. Sensitivity measures, Anova-like techniques and the use of bootstrap[J]. Journal of Statistical Computations and Simulations,1997,58(2):99-120.
167. Saltelli A K C, Scott E M. Sensitivity analysis[M]. New York:Wiley,2000.
168. Sobol I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates[J]. Mathematics and Computer in Simulation,2001,55(1):271-280.
169. Yu W, Wilson D I, Young B R, et al. Variance decomposition of nonlinear systems[C]. In:IEEE International Conference on Control and Automation. Christchurch, New Zealand,2009.738-744.
170. Harris T J, Yu W. Variance decompositions of nonlinear dynamic stochastic systems[J]. Journal of Process Control,2010,20(2):195-205.
171. Grimble M J. Generalized minimum variance control law revisited[J]. Optimal Control Applications and Methods,1988,9(1):63-77.
172. Majecki P, Grimble M J. Controller prformance design and assessment using nonlinear generalized minimum variance benchmark:scalar case[C]. In:Control 2004. University of Bath, UK,2004
173.方兆本,缪柏其.随机过程[M].北京：科学出版社,2011.
174. Chon K H, Korenberg M J, Holstein-Rathlou N H. Application of fast orthogonal search to linear and nonlinear stochastic systems[J]. Annals of Biomedical Engineering,1997,25(5):793-801.
175. Korenberg M J. Identifying nonlinear difference equation and functional expansion representations: the fast orthogonal algorithm[J]. Annals of Biomedical Engineering,1988,16(1):123-142.
176. Terasvirta T, Digk D V, Medeiros M C. Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series:A re-examination[J]. International Journal of Forecasting 2005,21(4):755-774.
177. Chen S, Billings S A. Neural networks for nonlinear dynamic system modeling and identification [J]. International Journal of Control,1992,56(2):319-346.
178. Sternad M, Soderstrom T. LQG-optimal feedforward regulators [J]. Automatica,1988,24(4):557-561.
179. Folake B O, Huang B, Edgar T. Feedforward and feedback controller performance assessment of linear time-variant processes[J]. Industrial and Engineering Chemistry Research,2004,43(2):589-596.
180. Fan J, Yao Q. Nonlinear time series:nonparametric and parametric methods[M]. New York:Springer Series in Statistics,2005.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |