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

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Standard

A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation. / Mercorelli, Paolo.
Applied Physics, System Science and Computers II : Proceedings of the 2nd International Conference on Applied Physics, System Science and Computers, APSAC2017. Hrsg. / A. Croitoru; K. Ntalianis. Band 489 Springer Verlag, 2019. S. 251-257 (Lecture Notes in Electrical Engineering; Band 489).

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet

Harvard

Mercorelli, P 2019, A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation. in A Croitoru & K Ntalianis (Hrsg.), Applied Physics, System Science and Computers II : Proceedings of the 2nd International Conference on Applied Physics, System Science and Computers, APSAC2017. Bd. 489, Lecture Notes in Electrical Engineering, Bd. 489, Springer Verlag, S. 251-257, 2rd International Conference on: Applied Physics, System Science and Computers - APSAC2017, Dubrovnik, Kroatien, 27.09.17. https://doi.org/10.1007/978-3-319-75605-9_35

APA

Mercorelli, P. (2019). A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation. In A. Croitoru, & K. Ntalianis (Hrsg.), Applied Physics, System Science and Computers II : Proceedings of the 2nd International Conference on Applied Physics, System Science and Computers, APSAC2017 (Band 489, S. 251-257). (Lecture Notes in Electrical Engineering; Band 489). Springer Verlag. https://doi.org/10.1007/978-3-319-75605-9_35

Vancouver

Mercorelli P. A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation. in Croitoru A, Ntalianis K, Hrsg., Applied Physics, System Science and Computers II : Proceedings of the 2nd International Conference on Applied Physics, System Science and Computers, APSAC2017. Band 489. Springer Verlag. 2019. S. 251-257. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-3-319-75605-9_35

Bibtex

@inbook{37e5edf285fe477f93ecaf7196f93c0b,
title = "A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation",
abstract = "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.",
keywords = "Engineering, Model predictive control, Optimization, Matrix algebra, Discrete systems, Linear systems",
author = "Paolo Mercorelli",
year = "2019",
doi = "10.1007/978-3-319-75605-9_35",
language = "English",
isbn = "978-331975604-2",
volume = "489",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "251--257",
editor = "A. Croitoru and K. Ntalianis",
booktitle = "Applied Physics, System Science and Computers II",
address = "Germany",
note = "2rd International Conference on: Applied Physics, System Science and Computers - APSAC2017 : Applied Physics, System Science and Computers, APSAC2017 ; Conference date: 27-09-2017 Through 29-09-2017",

}

RIS

TY - CHAP

T1 - A sufficient asymptotic stability condition in generalised model predictive control to avoid input saturation

AU - Mercorelli, Paolo

N1 - Conference code: 2

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Engineering

KW - Model predictive control

KW - Optimization

KW - Matrix algebra

KW - Discrete systems

KW - Linear systems

UR - http://www.scopus.com/inward/record.url?scp=85049357275&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-75605-9_35

DO - 10.1007/978-3-319-75605-9_35

M3 - Article in conference proceedings

AN - SCOPUS:85049357275

SN - 978-331975604-2

VL - 489

T3 - Lecture Notes in Electrical Engineering

SP - 251

EP - 257

BT - Applied Physics, System Science and Computers II

A2 - Croitoru, A.

A2 - Ntalianis, K.

PB - Springer Verlag

T2 - 2rd International Conference on: Applied Physics, System Science and Computers - APSAC2017

Y2 - 27 September 2017 through 29 September 2017

ER -

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