Suboptimal search strategies with bounded computational complexity to solve long-horizon direct model predictive control problems

Petros Karamanakos, Tobias Geyer, Ralph Kennel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

39 Scopus citations

Abstract

Search algorithms that reduce the time to solve the direct model predictive control (MPC) problem are proposed in this paper. By allowing for suboptimal solutions, the computational complexity of the underlying optimization problem can be significantly reduced, albeit by sacrificing (to a certain degree) optimality. Two approaches are presented and discussed. The first approach requires quadratic time, making it a very efficient candidate for solving the examined problem. Thanks to the second approach, a preset upper limit on the operations performed in real time is not exceeded, thus guaranteeing realtime termination in all runs. To highlight the effectiveness of the introduced strategies, a variable speed drive system with a three-level voltage source inverter is used as an illustrative example.

Original languageEnglish
Title of host publication2015 IEEE Energy Conversion Congress and Exposition, ECCE 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages334-341
Number of pages8
ISBN (Electronic)9781467371506
DOIs
StatePublished - 27 Oct 2015
Event7th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2015 - Montreal, Canada
Duration: 20 Sep 201524 Sep 2015

Publication series

Name2015 IEEE Energy Conversion Congress and Exposition, ECCE 2015

Conference

Conference7th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2015
Country/TerritoryCanada
CityMontreal
Period20/09/1524/09/15

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