Abstract
This chapter deals with extensions of the MV benchmark that need substantially more information about the plant than just the time delay. An extension of the MV benchmark is the approach of generalised MV (GMV) benchmarking, minimising a weighted sum of the control error and control effort. More general but rigorous extensions are the linear-quadratic Gaussian (LQG) benchmark and the model-predictive control (MPC) assessment. These benchmarks are useful when more information on controller performance, such as how much can the output variance be reduced without significantly affecting the controller output variance is needed, or for cases where actuator wear is a concern. This chapter provides an overview of these advanced methods. It is particularly shown how to use routine operating data to distinguish between poor performance due to plant–model mismatch and that due to improper tuning of the MPC controller. Moreover, performance measures that estimate potential benefit from re-identification of the process model or re-tuning of the controller are introduced. This is essential in MPC monitoring, as a process model is a substantial component of the MPC controller.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This assumes a system model of the ARMAX type. If a model of the ARIMAX type, i.e. with integrating disturbance term, is considered, u(k) has to be replaced by Δu(k).
- 2.
The more general form of this control law is R(q)u(k)=−S(q)y(k)+T(q)r(k), referred to as the RST regulator.
- 3.
In this section, MATLAB routines kindly provided by Julien et al. (2004) have been used for the computation of infinite-horizon MPCs and the corresponding performance curves.
References
Agarwal N, Huang B, Tamayo EC (2007a) Assessing MPC performance. Part 1: probabilistic approach for constraint analysis. Ind Eng Chem Res 46:8101–8111
Agarwal N, Huang B, Tamayo EC (2007b) Assessing MPC performance. Part 2: Bayesian approach for constraint tuning. Ind Eng Chem Res 46:8112–8119
Akande S, Huang B, Lee K-H (2009) MPC constraint analysis—Bayesian approach via a continuous-valued profit function. Ind Eng Chem Res 18:3944–3954
Allgöwer F, Zheng A (eds) (2000) Nonlinear model predictive control. Birkhäuser, Basel
Anderson BDO, Moore JB (1991) Optimal control: linear quadratic methods. Prentice Hall, New York
Åström KJ (1979) Introduction to stochastic control. Academic Press, San Diego
Åström KJ, Wittenmark P (1997) Computer controlled systems: theory and design. Prentice Hall, New York
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
Boyd S, Barratt C (1991) Linear control design. Prentice Hall, New York
Camacho EF, Bordons C (1999) Model predictive control. Springer, Berlin
Clarke DW, Scattolini R (1991) Constrained receding horizon predictive control. Proc IEE D 138:347–354
Clarke DW, Mohtadi C, Tuffs PS (1987a) Generalized predictive control. Part I: the basic algorithm. Automatica 23:137–148
Clarke DW, Mohtadi C, Tuffs PS (1987b) Generalized predictive control. Part II: extensions and interpretations. Automatica 23:149–160
Clegg A (2002) Benchmarking as an aid to identifying under-performing control loops. www.isc-ltd.com/benchmark/learning_centre/aclegg.html
Cutler CR, Ramaker BL (1980) Dynamic matrix control—a computer control algorithm. In: Proc joint automatic control confer, San Francisco, USA
Favoreel W, Moor BD, van Overschee P (1999) Model-free subspace-based LQG-design. In: Proc Amer control confer, San Diego, pp 3372–3376
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
GarcÃa CE, Morshedi AM (1986) Quadratic programming solution of dynamic matrix control (QDMC). Chem Eng Commun 46:73–87
GarcÃa CE, Prett DM, Morari M (1989) Model predictive control: theory and practice—a survey. Automatica 25:335–348
Grimble MJ (2002a) Controller performance benchmarking and tuning using generalised minimum variance control. Automatica 38:2111–2119
Grimble MJ (2006a) Robust industrial control systems. Wiley, New York
Grimble MJ (2006b) Design of generalized minimum variance controllers for nonlinear systems. Int J Control Autom Syst 4:281–292
Grimble MJ, Johnson MA (1988) Optimal control and stochastic estimation, volumes 1 and 2. Wiley, New York
Grimble MJ, Majecki P (2004) Weighting selection for controller benchmarking and tuning. Tech report ICC/219/Dec 2004, University of Strathclyde, Glasgow
Grimble MJ, Majecki P (2005) New ideas in performance assessment and benchmarking of nonlinear systems. www.isc-ltd.com/benchmark/workshop/NGMV.pdf
Grimble MJ, Uduehi D (2001) Process control loop benchmarking and revenue optimization. In: Proc Amer control confer, Arlington, USA
Harris TJ (1985) AÂ comparative study of model based control strategies. In: Proc Amer control confer, Boston, USA
Harris TJ (2004) Statistical properties of quadratic-type performance indices. J Process Control 14:899–914
Harris TJ, Davis JH (1992) An iterative method for matrix spectral factorization. SIAM J Sci Stat Comput 13:531–540
Huang B, Kadali R (2008) Dynamic modelling, predictive control and performance monitoring. Springer, Berlin
Huang B, Shah SL (1999) Performance assessment of control loops. Springer, Berlin
Hugo AJ (2006) Performance assessment of single-loop industrial controllers. J Process Control 16:785–794
Julien RH, Foley MW, Cluett WR (2004) Performance assessment using a model predictive control benchmark. J Process Control 14:441–456
Kadali R, Huang B (2002a) Estimation of the dynamic matrix and noise model for model predictive control using closed-loop data. Ind Eng Chem Res 41:842–852
Kadali R, Huang B (2002b) Controller performance analysis with LQG benchmark obtained under closed loop conditions. ISA Trans 41:521–537
Kammer LC, Bitmead RR, Barlett PL (1996) Signal-based testing of LQ-optimality of controllers. In: Proc IEEE confer decision control, Kobe, Japan, pp 3620–3624
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
Ko B-S, Edgar TF (2001a) Performance assessment of constrained model predictive control systems. AIChE J 47:1363–1371
Kouvaritakis B, Cannon M (eds) (2001) Nonlinear predictive control: theory and practice. The Institute of Electrical Engineers
Kozub DJ (2002) Controller performance monitoring and diagnosis. Industrial perspective. In: Proc IFAC world congress, Barcelona, Spain
Kucera V (1979) Discrete linear control: the polynomial equations approach. Wiley, New York
Kwakernaak H, Sivan R (1972) Linear optimal control systems. Wiley, New York
Lee JH, Cooley B (1997) Recent advances in model predictive control and other related areas. In: Proc chemical process control AICHE and CACHE, pp 201—216b
Lee K-H, Huang B, Tamayo EC (2008) Sensitivity analysis for selective constraint and variability tuning in performance assessment of industrial MPC. Control Eng Pract 16:1195–1215
MacGregor JF (1977) Discrete stochastic control with input constraints. IEE Proc, Control Theory Appl 124:732–734
Maciejowski JM (2001) Predictive control with constraints. Prentice Hall, New York
Mayne DQ (1997) Nonlinear model predictive control: an assessment. In: Proc chemical process control AICHE and CACHE, pp 217–231
Mayne DQ, Rawlings JB, Rao CV, Scokaert POM (2000) Constrained model predictive control: stability and optimality. Automatica 26:789–814
McIntosh AR, Shah SL, Fisher DG (1991) Analysis and tuning of adaptive generalized predictive control. Can J Chem Eng 69:97–110
Montgomery DC, Runger GC (1992) Applied statistics and probability for engineers. Wiley, New York
Morari M, Lee JH (1991) Model predictive control: the good, the bad, and the ugly. In: Proc chemical process control, pp 271–296
Moudgalya KM (2007) Digital control. Wiley, New York
Moudgalya KM, Shah SL (2004) A polynomial based first course in digital control. In: Proc IEEE intern symp computer aided control systems design, Taipei, Taiwan, pp 190–195
Patwardhan RS, Shah SL (2002) Issues in performance diagnostics of model-based controllers. J Process Control 12:413–427
Patwardhan RS, Shah S, Emoto G, Fujii H (1998) Performance analysis of model-based predictive controllers: an industrial study. In: Proc AIChE, Miami, USA
Prett DM, GarcÃa CE (1988) Fundamental process control. Butterworths, Stonehan
Qin SJ, Badgwell TA (1997) An overview of industrial model predictive control technology. In: Kantor JC, GarcÃa CE, Carnaham B (eds) Int confer chemical process control. AIChE symposium series, vol 93, pp 232–256
Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11:733–764
Rawlings JB, Muske KR (1993) The stability of constrained receding horizon control. IEEE Trans Autom Control 38:1512–1516
Rawlings JB, Meadows ES, Muske K (1994) Nonlinear model predictive control: a tutorial and survey. In: Proc IFAC ADCHEM, Japan
Richalet J, Rault A, Testud JL, Papon J (1978) Model predictive heuristic control: applications to an industrial process. Automatica 14:413–428
Ricker NL (1991) Model predictive control: state of the art. In: Proc chemical process control, pp 419–444
Schäfer J, Çinar A (2002) Multivariable MPC performance assessment, monitoring and diagnosis. In: Proc IFAC world congress, Barcelona, Spain
Seborg DE, Edgar TF, Mellichamp DA (2004) Process dynamics and control. Wiley, New York
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
Soeterboeck R (1992) Predictive control: a unified approach. Prentice Hall, New York
Uduehi D, Ordys A, Grimble MJ, Majecki P, Xia H (2007a) Controller benchmarking procedures—data-driven methods. In: Ordys AW, Uduehi D, Johnson MA (eds) Process control performance assessment. Springer, Berlin, pp 81–126
Uduehi D, Ordys A, Grimble MJ, Majecki P, Xia H (2007b) Controller benchmarking procedures—model-based methods. In: Ordys AW, Uduehi D, Johnson MA (eds) Process control performance assessment. Springer, Berlin, pp 127–168
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
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
Xu F, Huang B, Akande S (2007) Performance assessment of model predictive control for variability and constraint tuning. Ind Eng Chem Res 46:1208–1219
Xu F, Huang B, Tamayo EC (2008) Performance assessment of MIMO control systems with time-variant disturbance dynamics. Comput Chem Eng 32:2144–2154
Zhang Y, Henson MA (1999) AÂ performance measure for constrained model predictive controllers. In: Proc Europ control confer, Karlsruhe, Germany
Zhao C, Zhao Y, Su H, Huang H (2009) Economic performance assessment of advanced process control with LQG benchmarking. J Process Control 19:557–569
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag London
About this chapter
Cite this chapter
Jelali, M. (2013). Advanced Control Performance Assessment. In: Control Performance Management in Industrial Automation. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-4546-2_4
Download citation
DOI: https://doi.org/10.1007/978-1-4471-4546-2_4
Publisher Name: Springer, London
Print ISBN: 978-1-4471-4545-5
Online ISBN: 978-1-4471-4546-2
eBook Packages: EngineeringEngineering (R0)