Encyclopedia of Systems and Control

2015 Edition
| Editors: John Baillieul, Tariq Samad

Model-Based Performance Optimizing Control

Reference work entry
DOI: https://doi.org/10.1007/978-1-4471-5058-9_244


In many applications, e.g., in chemical process control, the purpose of control is to achieve an optimal performance of the controlled system despite the presence of significant uncertainties about its behavior and of external disturbances. Tracking of set points is often required for lower-level control loops, but at the system level in most cases, this is not the primary concern and may even be counterproductive. In this entry, the use of dynamic online optimization on a moving finite horizon to realize optimal system performance is discussed. By real-time optimization, a performance-oriented or economic cost criterion is minimized or maximized over a finite horizon while the usual control specifications enter as constraints but not as set points. This approach integrates the computation of optimal set-point trajectories and of the regulation to these trajectories.


Model-predictive control (MPC) Integrated optimization and control Real-time optimization (RTO) Performance optimizing control Process control 
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  1. Angeli D, Amrit R, Rawlings J (2012) On average performance and stability of economic model predictive control. IEEE Trans Autom Control 57(7):1615–1626MathSciNetGoogle Scholar
  2. Bartusiak RD (2005) NMPC: a platform for optimal control of feed- or product-flexible manufacturing. Preprints International workshop on assessment and future directions of NMPC, Freudenstadt, pp 3–14Google Scholar
  3. Diehl M, Amrit R, Rawlings J, Angeli D (2011) A Lyapunov function for economic optimizing model predictive control. IEEE Trans Autom Control 56(3):703–707Google Scholar
  4. Engell S (2006, 2007) Feedback control for optimal process operation. Plenary paper IFAC ADCHEM, Gramado, 2006; J Process Control 17:203–219Google Scholar
  5. Erdem G, Abel S, Morari M, Mazzotti M, Morbidelli M (2004) Automatic control of simulated moving beds. Part II: nonlinear isotherms. Ind Eng Chem Res 43:3895–3907Google Scholar
  6. Finkler T, Lucia S, Dogru M, Engell S (2013) A simple control scheme for batch time minimization of exothermic semi-batch polymerizations. Ind Eng Chem Res 52:5906–5920Google Scholar
  7. Finkler TF, Kawohl M, Piechottka U, Engell S (2014) Realization of online optimizing control in an industrial semi-batch polymerization. J Process Control 24:399–414Google Scholar
  8. Grüne L (2013) Economic receding horizon control without terminal constraints. Automatica 49:725–734Google Scholar
  9. Helbig A, Abel O, Marquardt W (2000) Structural concepts for optimization based control of transient processes. In: Nonlinear model predictive control. Allgöwer F and Zheng A, eds. Birkhäuser, Basel pp 295–311Google Scholar
  10. Idris IAN, Engell S (2012) Economics-based NMPC strategies for the operation and control of a continuous catalytic distillation process. J Process Control 22:1832–1843Google Scholar
  11. Lucia S, Finkler T, Basak D, Engell S (2013) A new Robust NMPC Scheme and its application to a semi-batch reactor example. J Process Control 23:1306–1319Google Scholar
  12. Marlin TE, Hrymak AN (1997) Real-time operations optimization of continuous processes. In: Proceedings of CPC V, Lake Tahoe, AIChE symposium series, vol 93, pp 156–164Google Scholar
  13. Morari M, Stephanopoulos G, Arkun Y (1980) Studies in the synthesis of control structures for chemical processes, part I. AIChE J 26:220–232MathSciNetGoogle Scholar
  14. Ochoa S, Wozny G, Repke J-U (2010) Plantwide optimizing control of a continuous bioethanol production process. J Process Control 20:983–998Google Scholar
  15. Pham LC, Engell S (2011) A procedure for systematic control structure selection with application to reactive distillation. In: Proceedings of 18th IFAC world congress, Milan, pp 4898–4903Google Scholar
  16. Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11:733–764Google Scholar
  17. Rao CV, Rawlings JB, Mayne DQ (2003) Constrained state estimation for nonlinear discrete-time systems. IEEE Trans Autom Control 48:246–258MathSciNetGoogle Scholar
  18. Rawlings J, Amrit R (2009) Optimizing process economic performance using model predictive control In: Nonlinear model predictive control – towards new challenging applications. Springer, Berlin, pp 119–138Google Scholar
  19. Rolandi PA, Romagnoli JA (2005) A framework for online full optimizing control of chemical processes. In: Proceedings of ESCAPE 15. Barcelona, Elsevier, pp 1315–1320Google Scholar
  20. Skogestad S (2000) Plantwide control: the search for the self-optimizing control structure. J Process Control 10:487–507Google Scholar
  21. Toumi A, Engell S (2004) Optimization-based control of a reactive simulated moving bed process for glucose isomerization. Chem Eng Sci 59:3777–3792Google Scholar
  22. Zanin AC, Tvrzska de Gouvea M, Odloak D (2000) Industrial implementation of a real-time optimization strategy for maximizing production of LPG in a FCC unit. Comput Chem Eng 24:525–531Google Scholar

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© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Fakultät Bio- und ChemieingenieurwesenTechnische Universität DortmundDortmundGermany