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Nonlinear Programming Strategies for State Estimation and Model Predictive Control

  • Victor M. Zavala
  • Lorenz T. Biegler
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 384)

Abstract

Sensitivity-based strategies for on-line moving horizon estimation (MHE) and nonlinear model predictive control (NMPC) are presented both from a stability and computational perspective. These strategies make use of full-space interior-point nonlinear programming (NLP) algorithms and NLP sensitivity concepts. In particular, NLP sensitivity allows us to partition the solution of the optimization problems into background and negligible on-line computations, thus avoiding the problem of computational delay even with large dynamic models. We demonstrate these developments through a distributed polymerization reactor model containing around 10,000 differential and algebraic equations (DAEs).

Keywords

large-scale MHE NMPC nonlinear programming sensitivity interior-point methods sparse linear algebra 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Victor M. Zavala
    • 1
  • Lorenz T. Biegler
    • 1
  1. 1.Department of Chemical EngineeringCarnegie Mellon UniversityUSA

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