Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2017 American Control Conference, ACC 2017 |
| Seiten | 5636-5641 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781509059928 |
| Publikationsstatus | Veröffentlicht - 29 Juni 2017 |
| Extern publiziert | Ja |
| Veranstaltung | 2017 American Control Conference (ACC) - Seattle, WA, USA Dauer: 24 Mai 2017 → 26 Mai 2017 |
Publikationsreihe
| Name | Proceedings of the American Control Conference |
|---|---|
| ISSN (Print) | 0743-1619 |
Abstract
The stability proofs of Model Predictive Control without terminal constraints and/or cost are tightly based upon the principle of optimality, which does not hold in most currently employed approaches to Stochastic MPC. In this paper, we first provide a stability proof for Stochastic Model Predictive Control without terminal cost or constraints under the assumption of optimization over feedback laws and propagation of the probability density functions of predicted states. Based thereon, we highlight why the proof does not remain valid if approximations such as parametrized feedback laws or relaxations on the chance constraints are employed and provide tightened assumptions that are sufficient to establish closed-loop stability. General statements valid for nonlinear systems are provided along with examples and computational simplifications in the case of linear systems.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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2017 American Control Conference, ACC 2017. 2017. S. 5636-5641 7963832 (Proceedings of the American Control Conference).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Stabilizing stochastic MPC without terminal constraints
AU - Lorenzen, Matthias
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Publisher Copyright: © 2017 American Automatic Control Council (AACC).
PY - 2017/6/29
Y1 - 2017/6/29
N2 - The stability proofs of Model Predictive Control without terminal constraints and/or cost are tightly based upon the principle of optimality, which does not hold in most currently employed approaches to Stochastic MPC. In this paper, we first provide a stability proof for Stochastic Model Predictive Control without terminal cost or constraints under the assumption of optimization over feedback laws and propagation of the probability density functions of predicted states. Based thereon, we highlight why the proof does not remain valid if approximations such as parametrized feedback laws or relaxations on the chance constraints are employed and provide tightened assumptions that are sufficient to establish closed-loop stability. General statements valid for nonlinear systems are provided along with examples and computational simplifications in the case of linear systems.
AB - The stability proofs of Model Predictive Control without terminal constraints and/or cost are tightly based upon the principle of optimality, which does not hold in most currently employed approaches to Stochastic MPC. In this paper, we first provide a stability proof for Stochastic Model Predictive Control without terminal cost or constraints under the assumption of optimization over feedback laws and propagation of the probability density functions of predicted states. Based thereon, we highlight why the proof does not remain valid if approximations such as parametrized feedback laws or relaxations on the chance constraints are employed and provide tightened assumptions that are sufficient to establish closed-loop stability. General statements valid for nonlinear systems are provided along with examples and computational simplifications in the case of linear systems.
UR - http://www.scopus.com/inward/record.url?scp=85027044178&partnerID=8YFLogxK
U2 - 10.23919/ACC.2017.7963832
DO - 10.23919/ACC.2017.7963832
M3 - Conference contribution
T3 - Proceedings of the American Control Conference
SP - 5636
EP - 5641
BT - 2017 American Control Conference, ACC 2017
T2 - 2017 American Control Conference (ACC)
Y2 - 24 May 2017 through 26 May 2017
ER -