Details
| Original language | English |
|---|---|
| Article number | 111543 |
| Number of pages | 14 |
| Journal | AUTOMATICA |
| Volume | 163 |
| Early online date | 6 Feb 2024 |
| Publication status | Published - May 2024 |
Abstract
This paper is about robust Model Predictive Control (MPC) for linear systems with additive and multiplicative uncertainty. A novel class of configuration-constrained polytopic robust forward invariant tubes is introduced, which admit a joint parameterization of their facets and vertices. They are the foundation for the development of novel Configuration-Constrained Tube MPC (CCTMPC) controllers that freely optimize the shape of their polytopic tube, subject to conic vertex configuration constraints, as well as associated vertex control laws by solving convex optimization problems online. It is shown that CCTMPC is—under appropriate assumptions—systematically less conservative than Rigid- and Homothetic- Tube MPC. Additionally, it is proven that there exist control systems for which CCTMPC is less conservative than Elastic Tube MPC, Disturbance Affine Feedback MPC, and Fully Parameterized Tube MPC.
Keywords
- Convex optimization, Model Predictive Control, Robust control
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
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In: AUTOMATICA, Vol. 163, 111543, 05.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Configuration-Constrained Tube MPC
AU - Villanueva, Mario Eduardo
AU - Müller, Matthias A.
AU - Houska, Boris
N1 - Funding Information: This work was supported by the German Research Foundation (DFG) – 279734922 . The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Daniele Casagrande under the direction of Editor Florian Dorfler.
PY - 2024/5
Y1 - 2024/5
N2 - This paper is about robust Model Predictive Control (MPC) for linear systems with additive and multiplicative uncertainty. A novel class of configuration-constrained polytopic robust forward invariant tubes is introduced, which admit a joint parameterization of their facets and vertices. They are the foundation for the development of novel Configuration-Constrained Tube MPC (CCTMPC) controllers that freely optimize the shape of their polytopic tube, subject to conic vertex configuration constraints, as well as associated vertex control laws by solving convex optimization problems online. It is shown that CCTMPC is—under appropriate assumptions—systematically less conservative than Rigid- and Homothetic- Tube MPC. Additionally, it is proven that there exist control systems for which CCTMPC is less conservative than Elastic Tube MPC, Disturbance Affine Feedback MPC, and Fully Parameterized Tube MPC.
AB - This paper is about robust Model Predictive Control (MPC) for linear systems with additive and multiplicative uncertainty. A novel class of configuration-constrained polytopic robust forward invariant tubes is introduced, which admit a joint parameterization of their facets and vertices. They are the foundation for the development of novel Configuration-Constrained Tube MPC (CCTMPC) controllers that freely optimize the shape of their polytopic tube, subject to conic vertex configuration constraints, as well as associated vertex control laws by solving convex optimization problems online. It is shown that CCTMPC is—under appropriate assumptions—systematically less conservative than Rigid- and Homothetic- Tube MPC. Additionally, it is proven that there exist control systems for which CCTMPC is less conservative than Elastic Tube MPC, Disturbance Affine Feedback MPC, and Fully Parameterized Tube MPC.
KW - Convex optimization
KW - Model Predictive Control
KW - Robust control
UR - http://www.scopus.com/inward/record.url?scp=85183941088&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2208.12554
DO - 10.48550/arXiv.2208.12554
M3 - Article
AN - SCOPUS:85183941088
VL - 163
JO - AUTOMATICA
JF - AUTOMATICA
SN - 0005-1098
M1 - 111543
ER -