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Modeling and Identification for NonlinearModel Predictive Control: Requirements,Current Status and Future Research Needs

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Nonlinear Model Predictive Control

Part of the book series: Progress in Systems and Control Theory ((PSCT,volume 26))

Abstract

In this paper, we present a critical look into modeling/identification strategies for nonlinear model predictive control applications. General modeling requirements for nonlinear model predictive control applications are discussed. Then, strengths and weaknesses of fundamental modeling as well as various empirical modeling methods are examined in light of these requirements. A promising new paradigm of nonlinear subspace identification is put forth. We conclude the paper with a discussion of future research needs.

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Authors and Affiliations

  1. School of Chemical Engineering, Purdue University, CHME, West Lafayette, IN 47907-1283

    Jay H. Lee

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  1. Jay H. Lee
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Editors and Affiliations

  1. Institut für Systemtheorie technischer Prozesse, Universität Stuttgart, Pfaffenwaldring 9, Stuttgart, 70550, Germany

    Frank Allgöwer

  2. Department of Chemical Engineering, University of Massachusetts at Amherst, 159 Goessmann Lab, Amherst, 01003-3110, MA, USA

    Alex Zheng

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© 2000 Springer Basel AG

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Lee, J.H. (2000). Modeling and Identification for NonlinearModel Predictive Control: Requirements,Current Status and Future Research Needs. In: Allgöwer, F., Zheng, A. (eds) Nonlinear Model Predictive Control. Progress in Systems and Control Theory, vol 26. Birkhäuser, Basel. https://doi.org/10.1007/978-3-0348-8407-5_15

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  • DOI: https://doi.org/10.1007/978-3-0348-8407-5_15

  • Publisher Name: Birkhäuser, Basel

  • Print ISBN: 978-3-0348-9554-5

  • Online ISBN: 978-3-0348-8407-5

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