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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/804825

Title: A vector quantization approach to scenario generation for stochastic NMPC
Author/Creator: Goodwin, Graham C.; Østergaard, Jan; Quevedo, Daniel E.; Feuer, Arie
Description: This paper describes a novel technique for scenario generation aimed at closed loop stochastic nonlinear model predictive control. The key ingredient in the algorithm is the use of vector quantization methods. We also show how one can impose a tree structure on the resulting scenarios. Finally, we briefly describe how the scenarios can be used in large scale stochastic nonlinear model predictive control problems and we illustrate by a specific problem related to optimal mine planning.
Relation: Nonlinear Model Predictive Control: Towards New Challenging Applications p. 235-248
Relation: Lecture Notes in Control and Information Sciences 384/2009
Publisher Link: http://dx.doi.org/10.1007/978-3-642-01094-1_19
Date: 2009
Publisher: Springer-Verlag
Keyword(s): scenario generation; closed loop control; stochastic nonlinear model predictive control; vector quantization
Resource Type: book chapter
Rights: The original publication is available at www.springerlink.com
Identifier: http://hdl.handle.net/1959.13/804825
Identifier: ISBN:9783642010934
Language: eng

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