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A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Authors

The goal of this contribution is presenting a Theorem which states the asymptotic stability of a feedback controlled system with a Linear Generalized Model Predictive Control (LGMPC). Concerning the asymptotic stability, a sufficient and constructive condition on the weight matrices of the cost function used in the optimization problem in LGMPC for one step prediction horizon is demonstrated. The condition consists of a lower bound for one of these matrices. The obtained condition is explained and discussed by means of some physical considerations. The second part of this contribution is devoted to the saturation case and proves a sufficient condition for obtaining asymptotic stability and saturation avoidance.

Original languageEnglish
Title of host publicationApplied Physics, System Science and Computers II : Proceedings of the 2nd International Conference on Applied Physics, System Science and Computers, APSAC2017
EditorsA. Croitoru, K. Ntalianis
Number of pages7
Volume489
PublisherSpringer Verlag
Publication date2019
Pages251-257
ISBN (print)978-331975604-2
ISBN (electronic)978-3-319-75605-9
DOIs
Publication statusPublished - 2019
Event2rd International Conference on: Applied Physics, System Science and Computers - APSAC2017: Applied Physics, System Science and Computers - Dubrovnik, Croatia
Duration: 27.09.2017 → 29.09.2017
Conference number: 2

    Research areas

  • Engineering - Model predictive control, Optimization, Matrix algebra, Discrete systems, Linear systems

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