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Explicit Nonlinear Model Predictive Control Theory and Applications /

Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time...

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Auteurs principaux: Grancharova, Alexandra. (Auteur, http://id.loc.gov/vocabulary/relators/aut), Johansen, Tor Arne. (http://id.loc.gov/vocabulary/relators/aut)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:English
Publié: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Édition:1st ed. 2012.
Collection:Lecture Notes in Control and Information Sciences, 429
Sujets:
Control engineering.
Computational complexity.
System theory.
Statistical physics.
Control and Systems Theory.
Complexity.
Systems Theory, Control.
Applications of Nonlinear Dynamics and Chaos Theory.
Accès en ligne:https://doi.org/10.1007/978-3-642-28780-0
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Table des matières:
  • Multi-parametric Programming
  • Nonlinear Model Predictive Control
  • Explicit NMPC Using mp-QP Approximations of mp-NLP
  • Explicit NMPC via Approximate mp-NLP
  • Explicit MPC of Constrained Nonlinear Systems with Quantized Inputs
  • Explicit Min-Max MPC of Constrained Nonlinear Systems with Bounded Uncertainties
  • Explicit Stochastic NMPC
  • Explicit NMPC Based on Neural Network Models
  • Semi-Explicit Distributed NMPC.

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