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An algorithm for estimating upper bound horizon in model predictive control

  • Guangren Duan*
  • , Yong Sun
  • , Maorui Zhang
  • , Ze Zhang
  • *Corresponding author for this work

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

Abstract

The upper bound horizon in model predictive control (MPC) problem is computed by a new non-iterative algorithm. The global stability of the MPC problem is guaranteed by solving the infinite horizon constrained linear quadratic regulator (LQR) problem. While the infinite horizon constrained LQR problem can be transformed into the finite horizon constrained LQR problem based on the upper bound horizon equally. There are some algorithms for estimating the upper bound horizon, however, they need expensive computation or give a big value. Then an new algorithm to estimate the upper bound horizon is presented by the linear programming. It only need to solve a linear programming problem for online application. Finally, the comparison among some methods is given by an example. The proposed algorithm has less conservative than that of other algorithms in recent literatures.

Original languageEnglish
Title of host publicationProceedings of the 2011 Chinese Control and Decision Conference, CCDC 2011
Pages823-827
Number of pages5
DOIs
StatePublished - 2011
Event2011 Chinese Control and Decision Conference, CCDC 2011 - Mianyang, China
Duration: 23 May 201125 May 2011

Publication series

NameProceedings of the 2011 Chinese Control and Decision Conference, CCDC 2011

Conference

Conference2011 Chinese Control and Decision Conference, CCDC 2011
Country/TerritoryChina
CityMianyang
Period23/05/1125/05/11

Keywords

  • Constrained Finite Horizon
  • Constrained Linear Quadratic Regulation
  • Model Predictive Control
  • Upper Bound Horizon

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