Encyclopedia of Systems and Control

Living Edition
| Editors: John Baillieul, Tariq Samad

Model-Predictive Control in Practice

  • Thomas A. Badgwell
  • S. Joe Qin
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5102-9_8-1


This entry provides a brief description of model predictive control (MPC) technology and how it is used in practice. The emphasis here is on refining and chemical plant applications where the technology has achieved its greatest acceptance. After a short description of what MPC is and how it fits into the hierarchy of control functions, the basic algorithm is presented as a sequence of three optimization problems. The steps required for a successful application are then outlined, followed by a summary and outline of likely future directions for MPC technology.


Computer control Mathematical programming Predictive control 
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  1. Bielger LT (2010) Nonlinear programming, concepts, algorithms, and applications to chemical processes. SIAM, PhiladelphiaGoogle Scholar
  2. Cutler CR, Ramaker BL (1979) Dynamic matrix control – a computer control algorithm. Paper presented at the AIChE national meeting, Houston, April 1979Google Scholar
  3. Darby ML, Nikolaou M (2012) MPC: current practice and challenges. Control Eng Pract 20:328–342CrossRefGoogle Scholar
  4. Gary JH, Handwerk GE, Kaiser MJ (2007) Petroleum refining: technology and economics. CRC, New YorkGoogle Scholar
  5. Ljung L (1999) System identification: theory for the user. Prentice Hall, Upper Saddle RiverGoogle Scholar
  6. Maciejowski JM (2002) Predictive control with constraints. Pearson Education Limited, EssexGoogle Scholar
  7. Mayne DQ, Rawlings JB, Rao CV, Scokaert POM (2000) Constrained model predictive control: stability and optimality. Automatica 36:789–814CrossRefMATHMathSciNetGoogle Scholar
  8. Odelson BJ, Rajamani MR, Rawlings JB (2006) A new autocovariance least-squares method for estimating noise covariances. Automatica 42:303–308CrossRefMATHMathSciNetGoogle Scholar
  9. Pannocchia G, Rawlings JB (2003) Disturbance models for offset-free model predictive control. AIChE J 49:426–437CrossRefGoogle Scholar
  10. Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11:733–764CrossRefGoogle Scholar
  11. Rao CV, Wright SJ, Rawlings JB (1998) Application of interior-point methods to model predictive control. J Optim Theory Appl 99:723–757CrossRefMATHMathSciNetGoogle Scholar
  12. Rawlings JB, Mayne DQ (2009) Model predictive control: theory and design. Nob Hill Publishing, MadisonGoogle Scholar
  13. Richalet J, Rault A, Testud JL, Papon J (1978) Model predictive heuristic control: applications to industrial processes. Automatica 14:413–428CrossRefGoogle Scholar
  14. Zagrobelny M, Luo J, Rawlings JB (2012) Quis custodiet ipsos custodies? In: IFAC conference on nonlinear model predictive control 2012, Noordwijkerhout, Aug 2012Google Scholar
  15. Zavala VM, Biegler LT (2009) The advanced-step NMPC controller: optimality, stability, and robustness. Automatica 45:86–93CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.ExxonMobil Research & EngineeringAnnandale, NJUSA
  2. 2.University of Southern CaliforniaLos Angeles, CAUSA