Details
| Original language | English |
|---|---|
| Pages (from-to) | 1961 - 1966 |
| Number of pages | 6 |
| Journal | IEEE Control Systems Letters |
| Volume | 8 |
| Early online date | 7 Jun 2024 |
| Publication status | Published - 19 Jul 2024 |
Abstract
This paper introduces a novel class of terminal regions and cost functions for tube model predictive control (TMPC). Our focus is on polytopic configuration-constrained TMPC schemes, which offer flexibility by introducing a significant amount of variables to model the shape of the propagated sets. This flexibility, however, comes with a challenge, namely, to enforce stability efficiently. To address this challenge, we propose tailored terminal regions and cost functions enabling efficient and stable TMPC implementations, without relying on regularity assumptions about the control system or configuration templates. Numerical case studies demonstrate the effectiveness and performance of the proposed control scheme.
Keywords
- Convex functions, Convex Optimization, Costs, Electron tubes, Model Predictive Control, Robust Control, Robust control, Stability analysis, Uncertainty, Vectors, model predictive control, convex optimization
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Control and Optimization
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In: IEEE Control Systems Letters, Vol. 8, 19.07.2024, p. 1961 - 1966.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - On Stabilizing Terminal Costs and Regions for Configuration-Constrained Tube MPC
AU - Houska, Boris
AU - Muller, Matthias A.
AU - Villanueva, Mario E.
N1 - Publisher Copyright: Authors
PY - 2024/7/19
Y1 - 2024/7/19
N2 - This paper introduces a novel class of terminal regions and cost functions for tube model predictive control (TMPC). Our focus is on polytopic configuration-constrained TMPC schemes, which offer flexibility by introducing a significant amount of variables to model the shape of the propagated sets. This flexibility, however, comes with a challenge, namely, to enforce stability efficiently. To address this challenge, we propose tailored terminal regions and cost functions enabling efficient and stable TMPC implementations, without relying on regularity assumptions about the control system or configuration templates. Numerical case studies demonstrate the effectiveness and performance of the proposed control scheme.
AB - This paper introduces a novel class of terminal regions and cost functions for tube model predictive control (TMPC). Our focus is on polytopic configuration-constrained TMPC schemes, which offer flexibility by introducing a significant amount of variables to model the shape of the propagated sets. This flexibility, however, comes with a challenge, namely, to enforce stability efficiently. To address this challenge, we propose tailored terminal regions and cost functions enabling efficient and stable TMPC implementations, without relying on regularity assumptions about the control system or configuration templates. Numerical case studies demonstrate the effectiveness and performance of the proposed control scheme.
KW - Convex functions
KW - Convex Optimization
KW - Costs
KW - Electron tubes
KW - Model Predictive Control
KW - Robust Control
KW - Robust control
KW - Stability analysis
KW - Uncertainty
KW - Vectors
KW - model predictive control
KW - convex optimization
UR - http://www.scopus.com/inward/record.url?scp=85195427904&partnerID=8YFLogxK
U2 - 10.1109/LCSYS.2024.3411517
DO - 10.1109/LCSYS.2024.3411517
M3 - Article
AN - SCOPUS:85195427904
VL - 8
SP - 1961
EP - 1966
JO - IEEE Control Systems Letters
JF - IEEE Control Systems Letters
ER -