Model predictive control for embedded applications

Panagiotis Vouzis, Leonidas G. Bleris, Mark Arnold, Mayuresh V. Kothare

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Model predictive control (MPC) refers to a family of control algorithms that compute a sequence of manipulated variables by solving an optimization problem, incorporating explicit knowledge of the plant model and incorporating feedback information. Applications that can readily benefit from embedded MPC chip range from automotive and avionics to microchemical systems, drug-delivery systems, and fuel cells. An overview of the platforms that have appeared in the literature for embedding MPC in applications with small physical size, such as fuel processors is presented. This is an abstract of a paper presented at the AIChE Annual Meeting (San Francisco, CA 11/12-17/2006).

Original languageEnglish (US)
Title of host publicationAIChE Annual Meeting, Conference Proceedings
StatePublished - 2006
Externally publishedYes
Event2006 AIChE Annual Meeting - San Francisco, CA, United States
Duration: Nov 12 2006Nov 17 2006

Other

Other2006 AIChE Annual Meeting
Country/TerritoryUnited States
CitySan Francisco, CA
Period11/12/0611/17/06

ASJC Scopus subject areas

  • Biotechnology
  • Chemical Engineering(all)
  • Bioengineering
  • Safety, Risk, Reliability and Quality

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