Robust constrained model predictive control for nonlinear systems: a comparative study

Mayuresh V. Kothare, Vesna Nevistic, Manfred Morari

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

Abstract

We compare two approaches to controlling nonlinear systems using model predictive control (MPC) techniques. In the first approach, we use an inner feedback loop to linearize the nonlinear plant and use the resulting linear model to synthesize a controller strategy based on standard MPC techniques. The nonlinear constraints resulting from the feedback linearization are handled using an iterative method. In the second approach, we approximate the nonlinear system by a linear time-varying (LTV) system and design a stabilizing receding horizon state-feedback control law using optimization techniques based on linear matrix inequalities (LMIs).

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
PublisherIEEE
Pages2884-2885
Number of pages2
Volume3
StatePublished - 1995
Externally publishedYes
EventProceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA
Duration: Dec 13 1995Dec 15 1995

Other

OtherProceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4)
CityNew Orleans, LA, USA
Period12/13/9512/15/95

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Kothare, M. V., Nevistic, V., & Morari, M. (1995). Robust constrained model predictive control for nonlinear systems: a comparative study. In Proceedings of the IEEE Conference on Decision and Control (Vol. 3, pp. 2884-2885). IEEE.