Fast Model Predictive Control with soft constraints

Arthur Richards*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

29 Citations (Scopus)


This paper describes a fast optimization algorithm for Model Predictive Control (MPC) with soft constraints. The method relies on the Kreisselmeier-Steinhauser function to provide a smooth approximation of the penalty function for a soft constraint. This is analogous to the approximation of a hard constraint by a smooth logarithmic barrier function. By introducing this approximation directly into the objective of an interior point optimization, there is no need for additional slack variables to capture constraint violation. Simulation results show significant speed-up compared to using slack variables.

Original languageEnglish
Pages (from-to)51-59
Number of pages9
JournalEuropean Journal of Control
Publication statusPublished - 1 Sep 2015
EventEuropean Control Conference - Zurich, United Kingdom
Duration: 17 Jul 201319 Jul 2013

Bibliographical note

Date of Acceptance: 19/05/2015


  • Interior point methods
  • Model Predictive Control
  • Soft constraints


Dive into the research topics of 'Fast Model Predictive Control with soft constraints'. Together they form a unique fingerprint.

Cite this