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Part of the book series: Advances in Industrial Control ((AIC))

Abstract

This chapter gives a brief introduction to the objectives, principle, tasks, and challenges of control performance management (CPM). A basic procedure for CPM, assessment benchmarks, are given. The key dates of the development of CPM technology and a literature survey are described.

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References

  • Ahsan Q, Grosvenor RI, Prickett PW (2004) Distributed control loop performance monitoring architecture. In: Proc control 2004, University of Bath, UK, ID-054

    Google Scholar 

  • Åkesson IN (2003) Plant loop auditing in practice. In: VDI-Berichte 1756, Proc GMA-Kongress: Automation und Information in Wirtschaft und Gesellschaft. Baden-Baden, Germany, pp 927–934

    Google Scholar 

  • Åström KJ (1979) Introduction to stochastic control. Academic Press, San Diego

    Google Scholar 

  • Åström KJ (1991) Assessment of achievable performance of simple feedback loops. Int J Adapt Control Signal Process 5:3–19

    Article  Google Scholar 

  • Bezergianni S, Georgakis C (2003) Evaluation of controller performance-use of models derived by subspace identification. Int J Adapt Control Signal Process 17:527–552

    Article  MATH  Google Scholar 

  • Bialkowski WL (1993) Dreams vs. reality: a view from both sides of the gap. Pulp & Paper Canada 94:19–27

    Google Scholar 

  • Box GEP, Jenkins GM (1970) Time series analysis: forcasting and control. Holden-Day, Oakland

    Google Scholar 

  • Brisk ML (2004) Process control: potential benefits and wasted opportunities. In: Proc Asian control confer, Melbourne, Australia, pp 10–16

    Google Scholar 

  • Choudhury MAAS, Kariwala V, Shah SL, Douke H, Takada H, Thornhill NF (2005a) A simple test to confirm control valve stiction. In: Proc IFAC world congress, Praha

    Google Scholar 

  • Choudhury MAAS, Thornhill NF, Shah SL, Shook DS (2006a) Automatic detection and quantification of stiction in control valves. Control Eng Pract 14:1395–1412

    Article  Google Scholar 

  • Choudhury MAAS, Shook D, Shah SL (2006b) Linear or nonlinear? A bicoherence based metric of nonlinearity measure. In: Proc IFAC symposium SAFEPROCESS, Beijing, China

    Google Scholar 

  • Choudhury MAAS, Shah SL, Thornhill NF (2008) Diagnosis of process nonlinearities and valve stiction—data driven approaches. Springer, London

    MATH  Google Scholar 

  • Desborough L, Harris T (1992) Performance assessment measures for univariate feedback control. Can J Chem Eng 70:1186–1197

    Article  Google Scholar 

  • Desborough L, Harris T (1993) Performance assessment measures for univariate feedforward/ feedback control. Can J Chem Eng 71:605–616

    Article  Google Scholar 

  • Desborough L, Miller R (2002) Increasing customer value of industrial control performance monitoring—Honeywell’s experience. AIChE Symp Ser 98(326):153–186

    Google Scholar 

  • DeVries W, Wu S (1978) Evaluation of process control effectiveness and diagnosis of variation in paper basis weight via multivariate time-series analysis. IEEE Trans Autom Control 23:702–708

    Article  Google Scholar 

  • Dittmar R, Bebar M, Reinig G (2003) Control Loop Performance Monitoring: Motivation, Methoden, Anwendungswünsche. Automtech Prax 45:94–103

    Google Scholar 

  • Ender D (1993) Process control performance: not as good as you think. Control Eng 40:180–190

    Google Scholar 

  • Eriksson P, Isaksson AJ (1994) Some aspects of control loop performance monitoring. In: Proc IEEE confer control applications, Glasgow, Scotland, pp 1029–1034

    Chapter  Google Scholar 

  • Ettaleb L (1999) Control loop performance assessment and oscillation detection. PhD thesis, University of British, Columbia, Canada

    Google Scholar 

  • Farenzena M, Trierweiler JO (2006) Variability matrix: a new tool to improve the plant performance. In: Proc IFAC ADCHEM, Gramado, Brazil, pp 893–898

    Google Scholar 

  • Forsman K, Stattin A (1999) A new criterion for detecting oscillations in control loops. In: Proc Europ control confer, Karlsruhe, Germany

    Google Scholar 

  • Gao J, Patwardhan RS, Akamatsu K, Hashimoto Y, Emoto G, Shah SL, Huang B (2003) Performance evaluation of two industrial MPC controllers. Control Eng Pract 11:1371–1387

    Article  Google Scholar 

  • Grimble MJ (2002b) Restricted structure controller tuning and performance assessment. IEE Proc Part D, Control Theory Appl 149:8–16

    Article  Google Scholar 

  • Grimble MJ (2003) Restricted structure control loop performance assessment for PID controllers and state-space systems. Asian J Control 5:39–57

    Article  Google Scholar 

  • Grimble MJ, Uduehi D (2001) Process control loop benchmarking and revenue optimization. In: Proc Amer control confer, Arlington, USA

    Google Scholar 

  • Hägglund T (1995) A control-loop performance monitor. Control Eng Pract 3:1543–1551

    Article  Google Scholar 

  • Harris TJ (1989) Assessment of closed loop performance. Can J Chem Eng 67:856–861

    Article  Google Scholar 

  • Harris T, Seppala CT (2001) Recent developments in performance monitoring and assessment techniques. In: Proc chemical process control confer, Tuscon, USA

    Google Scholar 

  • Harris T, Seppala CT, Desborough LD (1999) A review of performance monitoring and assessment techniques for univariate and multivariate control systems. J Process Control 9:1–17

    Article  Google Scholar 

  • Harris T, Boudreau F, MacGregor JF (1996a) Performance assessment using of multivariable feedback controllers. Automatica 32:1505–1518

    Article  MathSciNet  MATH  Google Scholar 

  • Harris T, Seppala CT, Jofriet PJ, Surgenor BW (1996b) Plant-wide feedback control performance assessment using an expert-system framework. Control Eng Pract 4:1297–1303

    Article  Google Scholar 

  • He QP, Wang J, Pottmannn M, Qin SJ (2007) A curve fitting method for detecting valve stiction in oscillating control loops. Ind Eng Chem Res 46:4549–4560

    Article  Google Scholar 

  • Horch A (1999) A simple method for detection of stiction in control valves. Control Eng Pract 7:1221–1231

    Article  Google Scholar 

  • Horch A (2000) Condition monitoring of control loops. PhD thesis, Royal Institute of Technology, Stockholm, Sweden

    Google Scholar 

  • Horch A, Isaksson AJ (1999) A modified index for control performance assessment. J Process Control 9:475–483

    Article  Google Scholar 

  • Horton EC, Foley MW, Kwok KE (2003) Performance assessment of level controllers. Int J Adapt Control Signal Process 17:663–684

    Article  MATH  Google Scholar 

  • Huang B (2002) Minimum variance control and performance assessment of time variant processes. J Process Control 12:707–719

    Article  MATH  Google Scholar 

  • Huang B (2003) A pragmatic approach towards assessment of control loop performance. Int J Adapt Control Signal Process 17:489–608

    Article  Google Scholar 

  • Huang B, Shah SL (1998) Practical issues in multivariable feedback control performance assessment. J Process Control 8:421–430

    Article  Google Scholar 

  • Huang B, Shah SL, Kwok EK (1997a) Good, bad or optimal? Performance assessment of multivariable processes. Automatica 33:1175–1183

    Article  MathSciNet  MATH  Google Scholar 

  • Huang B, Shah SL, Kwok EK, Zurcher J (1997b) Performance assessment of multivariate control loops on a paper-machine headbox. Can J Chem Eng 75:134–142

    Article  Google Scholar 

  • Huang B, Shah SL, Fujii H (1997c) The unitary interactor matrix and its estimation from closed-loop data. J Process Control 7:195–207

    Article  Google Scholar 

  • Huang B, Shah SL, Badmus L, Vishnubhotla A (1999) Control performance assessment: an enterprise asset management solution. www.matrikon.com/download/products/lit/processdoctor_pa_eam.pdf

  • Huang B, Shah SL, Miller R (2000) Feedforward plus feedback controller performance assessment of MIMO systems. IEEE Trans Control Syst Technol 8:580–587

    Article  Google Scholar 

  • Huang B, Ding SX, Qin J (2005a) Closed-loop subspace identification: an orthogonal projection approach. J Process Control 15:53–66

    Article  Google Scholar 

  • Huang B, Ding SX, Thornhill N (2005b) Practical solutions to multivariable feedback control performance assessment problem: reduced a priori knowledge of interactor matrices. J Process Control 15:573–583

    Article  Google Scholar 

  • Hugo AJ (1999) Process controller performance monitoring and assessment. www.controlartsinc.com/Support/Articles/PerformanceAssessment.PDF

  • Jelali M (2006c) An overview of control performance assessment technology and industrial applications. Control Eng Pract 14:441–466

    Article  Google Scholar 

  • Jelali M (2008) Estimation of valve stiction in control loops using separable least-squares and global search algorithms. J Process Control 18:632–642

    Article  Google Scholar 

  • Jelali M, Huang B (eds) (2010) Detection and diagnosis of stiction in control loops: state of the art and advanced methods. Springer, Berlin

    Google Scholar 

  • Jofriet P, Seppala C, Harvey M, Surgenor B, Harris T (1995) An expert system for control loop performance analysis. In: Proc annual meeting, technical section, Canadian pulp and paper association, pp B41–B49

    Google Scholar 

  • Julien RH, Foley MW, Cluett WR (2004) Performance assessment using a model predictive control benchmark. J Process Control 14:441–456

    Article  Google Scholar 

  • Kano M, Maruta H, Kugemoto H, Shimizu K (2004) Practical model and detection algorithm for valve stiction. In: Proc IFAC symp DYCOPS, Boston, USA

    Google Scholar 

  • Ko B-S, Edgar TF (1998) Assessment of achievable PI control performance for linear processes with dead time. In: Proc Amer control confer, Philadelphia, USA

    Google Scholar 

  • Ko B-S, Edgar TF (2001a) Performance assessment of constrained model predictive control systems. AIChE J 47:1363–1371

    Article  Google Scholar 

  • Ko B-S, Edgar TF (2001b) Performance assessment of multivariable feedback control systems. Automatica 37:899–905

    Article  MathSciNet  MATH  Google Scholar 

  • Ko B-S, Edgar TF (2004) PID control performance assessment: the single-loop case. AIChE J 50:1211–1218

    Article  Google Scholar 

  • Kozub DJ (2002) Controller performance monitoring and diagnosis. Industrial perspective. In: Proc IFAC world congress, Barcelona, Spain

    Google Scholar 

  • Kozub DJ, Garcia C (1993) Monitoring and diagnosis of automated controllers in the chemical process industries. In: Proc AIChE, St Louis, USA

    Google Scholar 

  • Lynch C, Dumont GA (1996) Control loop performance monitoring. IEEE Trans Control Syst Technol 18:151–192

    Google Scholar 

  • Majecki P, Grimble MJ (2004a) Controller performance design and assessment using nonlinear generalized minimum variance benchmark: scalar case. In: Proc control 2004, University of Bath, UK, ID-232

    Google Scholar 

  • Majecki P, Grimble MJ (2004b) GMV and restricted-structure GMV controller performance assessment—multivariable case. In: Proc Amer control confer, Boston, USA, vol 1, pp 697–702

    Google Scholar 

  • McNabb CA, Qin SJ (2003) Projection based MIMO control performance monitoring: I—covariance monitoring in state space. J Process Control 13:739–757

    Article  Google Scholar 

  • Miao T, Seborg DE (1999) Automatic detection of excessively oscillatory feedback control loops. In: Proc IEEE confer control applications. Kohala Coast-Island, USA

    Google Scholar 

  • Olaleye F, Huang B, Tamayo E (2004a) Performance assessment of control loops with time varying disturbance dynamics. J Process Control 14:867–877

    Article  Google Scholar 

  • Olaleye F, Huang B, Tamayo E (2004b) Feedforward and feedback controller performance assessment of linear time-variant processes. Ind Eng Chem Res 43:589–596

    Article  Google Scholar 

  • Ordys AW, Uduehi D, Johnson MA (eds) (2007) Process control performance assessment: from theory to implementation. Springer, Berlin

    MATH  Google Scholar 

  • Patwardhan RS (1999) Studies in synthesis and analysis of model predictive controllers. PhD thesis, University of Alberta, Canada

    Google Scholar 

  • Patwardhan RS, Shah S, Emoto G, Fujii H (1998) Performance analysis of model-based predictive controllers: an industrial study. In: Proc AIChE, Miami, USA

    Google Scholar 

  • Paulonis MA, Cox JW (2003) A practical approach for large-scale controller performance assessment, diagnosis, and improvement. J Process Control 13:155–168

    Article  Google Scholar 

  • Qin SJ (1998) Control performance monitoring—a review and assessment. Comput Chem Eng 23:173–186

    Article  Google Scholar 

  • Rakar A, Zorzut S, Jovan V (2004) Assessment of production performance by means of KPI. In: Proc control 2004, University of Bath, UK, ID-073

    Google Scholar 

  • Ruel M (2002) Learn how to assess and improve control loop performance. In: Proc ISA, Chicago, USA

    Google Scholar 

  • Ruel M (2003) The conductor directs this orchestra. Intech, November:20–22

    Google Scholar 

  • Schäfer J, Çinar A (2002) Multivariable MPC performance assessment, monitoring and diagnosis. In: Proc IFAC world congress, Barcelona, Spain

    Google Scholar 

  • Seborg DE, Edgar TF, Mellichamp DA (2004) Process dynamics and control. Wiley, New York

    Google Scholar 

  • Shah SL, Patwardhan R, Huang B (2001) Multivariate controller performance analysis: methods, applications and challenges. In: Proc chemical process control confer, Tucson, USA, pp 187–219

    Google Scholar 

  • Shinskey FG (1990) How good are our controllers in absolute performance and robustness? Meas Control 23:114–121

    Google Scholar 

  • Shinskey FG (1996) Process-control systems: application, design, and tuning. McGraw Hill, New York

    Google Scholar 

  • Shunta JP (1995) Achieving world class manufacturing through process control. Prentice Hall, New York

    Google Scholar 

  • Singhal A, Salsbury TI (2005) A simple method for detecting valve stiction in oscillating control loops. J Process Control 15:371–382

    Article  Google Scholar 

  • Spencer MA, Elliot RM (1997/1998) Improving instrumentation and control systems performance. Pet Technol Q Winter:93–97

    Google Scholar 

  • Stanfelj N, Marlin TE, MacGregor JF (1993) Monitoring and diagnosis of process control performance: the single-loop case. Ind Eng Chem Res 67:856–861

    Google Scholar 

  • Swanda A, Seborg DE (1997) Evaluating the performance of PID-type feedback control loops using normalized settling time. In: Proc IFAC ADCHEM, Banff, Canada, pp 301–306

    Google Scholar 

  • Swanda A, Seborg DE (1999) Controller performance assessment based on setpoint response data. In: Proc Amer control confer, San Diego, USA, pp 3863–3867

    Google Scholar 

  • Thornhill NF, Hägglund T (1997) Detection and diagnosis of oscillation in control loops. Control Eng Pract 5:1343–1354

    Article  Google Scholar 

  • Thornhill NF, Horch A (2007) Advances and new directions in plant-wide disturbance detection and diagnosis. Control Eng Pract 15:1196–1206

    Article  Google Scholar 

  • Thornhill NF, Oettinger M, Fedenczuk MS (1999) Refinery-wide control loop performance assessment. J Process Control 9:109–124

    Article  Google Scholar 

  • Thornhill NF, Shah SL, Huang B (2001) Detection of distributed oscillations and root-cause diagnosis. In: Proc CHEMFAS, Chejudo Island, Korea, pp 167–172

    Google Scholar 

  • Thornhill NF, Cox J, Paulonis M (2003a) Diagnosis of plant-wide oscillation through data-driven analysis and process understanding. Control Eng Pract 11:1481–1490

    Article  Google Scholar 

  • Thornhill NF, Huang B, Shah SL (2003b) Controller performance assessment in set point tracking and regulatory control. Int J Adapt Control Signal Process 17:709–727

    Article  MATH  Google Scholar 

  • Thornhill NF, Huang B, Zhang H (2003c) Detection of multiple oscillations in control loops. J Process Control 13:91–100

    Article  Google Scholar 

  • Torres BS, de Carvalho FB de Oliveira Fonseca M, Filho CS (2006) Performance assessment of control loops—cases studies. In: Proc IFAC ADCHEM, Gramado, Brasil

    Google Scholar 

  • Tyler M, Morari M (1995) Performance assessment for unstable and nonminimum-phase systems. In: Preprints IFAC workshop on-line fault detection supervision chemical process industries, Newcastle upon Tyne, UK

    Google Scholar 

  • Tyler M, Morari M (1996) Performance monitoring of control systems using likelihood methods. Automatica 32:1145–1162

    Article  MathSciNet  MATH  Google Scholar 

  • Xia C, Howell J (2003) Loop status monitoring and fault localization. J Process Control 13:679–691

    Article  Google Scholar 

  • Xia H, Majecki P, Ordys A, Grimble MJ (2003) Controller benchmarking based on economic benefits. In: Proc Europ control confer, Cambridge, UK, pp 2393–2398

    Google Scholar 

  • Xia H, Majecki P, Ordys A, Grimble MJ (2006) Performance assessment of MIMO systems based on I/O delay information. J Process Control 16:373–383

    Article  Google Scholar 

  • Xu F, Huang B, Tamayo EC (2006a) Assessment of economic performance of model predictive control through variance/constraint tuning. In: Proc IFAC ADCHEM, Gramado, Brazil, pp 899–904

    Google Scholar 

  • Xu F, Lee K-H, Huang B (2006b) Monitoring control performance via structured closed-loop response subject to output variance/covariance upper bound. J Process Control 16:971–984

    Article  Google Scholar 

  • Yamashita Y (2006) An automatic method for detection of valve stiction in process control loops. Control Eng Pract 14:503–510

    Article  Google Scholar 

  • Zhang Y, Henson MA (1999) A performance measure for constrained model predictive controllers. In: Proc Europ control confer, Karlsruhe, Germany

    Google Scholar 

  • Ziegler JG, Nichols NB (1943) Process lags in automatic control circuits. Trans Am Soc Mech Eng 65:433–444

    Google Scholar 

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Jelali, M. (2013). Introduction. In: Control Performance Management in Industrial Automation. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-4546-2_1

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  • DOI: https://doi.org/10.1007/978-1-4471-4546-2_1

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