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Explicit Stochastic NMPC

  • Alexandra GrancharovaEmail author
  • Tor Arne Johansen
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 429)

Abstract

This chapter considers two approaches to explicit stochastic NMPC of general constrained nonlinear discrete-time systems in the presence of disturbances and/or parameter uncertainties with known probability distributions. In Section 7.2, an approach to explicit solution of closed-loop (feedback) stochastic NMPC problems for constrained nonlinear systems, described by stochastic parametric models, is considered. The approach constructs a piecewise nonlinear (PWNL) approximation to the optimal sequence of feedback control policies. It is applied to design an explicit feedback stochastic NMPC controller for the cart and spring system. In Section 7.3, an explicit approximate approach to open-loop stochastic NMPC based on Gaussian process models is presented. The Gaussian process models are non-parametric probabilistic black-box models, whose advantage in comparison to the stochastic parametric models is that they provide information about the prediction uncertainty. The approach in Section 7.3 constructs a piecewise linear (PWL) approximation to the optimal control sequence and it is applied to design an explicit stochastic NMPC reference tracking controller for a combustion plant.

Keywords

Gaussian Process Model Predictive Control Combustion Control Gaussian Process Model Nonlinear Model Predictive Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of System Engineering and RoboticsBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of Engineering CyberneticsNorwegian University of Science and TechnologyTrondheimNorway

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