A Computationally Efficient Model Predictive Control Strategy for Linear Systems with Integer Inputs

Petros Karamanakos, Tobias Geyer, Ralph Kennel

Research output: Contribution to journalArticleScientificpeer-review

54 Citations (Scopus)
10 Downloads (Pure)

Abstract

For linear systems with integer inputs, the model predictive control problem with output reference tracking is formulated as an integer least-squares (ILS) problem. The ILS problem is solved using a modified sphere decoding algorithm, which is a particular branch-and-bound method. To reduce the computational complexity of the sphere decoder, a reduction algorithm is added as a preprocessing stage to reshape the search space in which the integer solution lies. The computational complexity of the proposed algorithm is modest, enabling its implementation in a real-time system even when considering long prediction horizons. A variable-speed drive system with a three-level voltage source inverter serves as an illustrative example to demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish
Pages (from-to)1463-1471
Number of pages9
JournalIEEE Transactions on Control Systems Technology
Volume24
Issue number4
DOIs
Publication statusPublished - Jul 2016
Externally publishedYes
Publication typeA1 Journal article-refereed

Keywords

  • Drive systems
  • integer least-squares (ILS) problem
  • integer programming
  • Lenstra-Lenstra-Lovàsz (LLL) lattice basis reduction
  • model predictive control (MPC)
  • power electronics
  • sphere decoding

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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