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A Computationally Efficient Scheduled Model Predictive Control Algorithm for Control of a Class of Constrained Nonlinear Systems

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Assessment and Future Directions of Nonlinear Model Predictive Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 358))

Abstract

We present an overview of our results on stabilizing scheduled output feedback Model Predictive Control (MPC) algorithm for constrained nonlinear systems based on our previous publications [19, 20]. Scheduled MPC provides an important alternative to conventional nonlinear MPC formulations and this paper addresses the issues involved in its implementation and analysis, within the context of the NMPC05 workshop. The basic formulation involves the design of a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm provides a general framework for scheduled output feedback MPC design.

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Author information

Authors and Affiliations

  1. Deparment of Chemical Engineering, Lehigh University, 111 Research Drive, Bethlehem, PA, 18017, USA

    Mayuresh V. Kothare

  2. GE Water and Process Technology, 4636 Somerton Road, Trevose, PA, 19053, USA

    Zhaoyang Wan

Authors
  1. Mayuresh V. Kothare
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  2. Zhaoyang Wan
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Editor information

Editors and Affiliations

  1. Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, 70550, Stuttgart, Germany

    Rolf Findeisen  & Frank Allgöwer  & 

  2. Chemical Engineering Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Lorenz T. Biegler

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Kothare, M.V., Wan, Z. (2007). A Computationally Efficient Scheduled Model Predictive Control Algorithm for Control of a Class of Constrained Nonlinear Systems. In: Findeisen, R., Allgöwer, F., Biegler, L.T. (eds) Assessment and Future Directions of Nonlinear Model Predictive Control. Lecture Notes in Control and Information Sciences, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72699-9_4

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  • DOI: https://doi.org/10.1007/978-3-540-72699-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72698-2

  • Online ISBN: 978-3-540-72699-9

  • eBook Packages: EngineeringEngineering (R0)

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