Details
| Original language | English |
|---|---|
| Pages (from-to) | 257-263 |
| Number of pages | 7 |
| Journal | IFAC-PapersOnLine |
| Volume | 54 |
| Issue number | 6 |
| Early online date | 9 Sept 2021 |
| Publication status | Published - 2021 |
| Event | 7th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021) - Bratislava, Slovakia Duration: 11 Jul 2021 → 14 Jul 2021 Conference number: 7 |
Abstract
We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a terminal cost and a terminal set constraint on an extended state containing past input-output values. We provide an explicit design procedure for the corresponding terminal ingredients that only uses measured input-output data. Further, we prove that the MPC scheme based on these terminal ingredients exponentially stabilizes the desired setpoint in closed loop. Finally, we illustrate the advantages over existing data-driven MPC approaches with a numerical example.
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
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In: IFAC-PapersOnLine, Vol. 54, No. 6, 2021, p. 257-263.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - On the design of terminal ingredients for data-driven MPC
AU - Berberich, Julian
AU - Köhler, Johannes
AU - Müller, Matthias A.
AU - Allgöwer, Frank
N1 - Conference code: 7
PY - 2021
Y1 - 2021
N2 - We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a terminal cost and a terminal set constraint on an extended state containing past input-output values. We provide an explicit design procedure for the corresponding terminal ingredients that only uses measured input-output data. Further, we prove that the MPC scheme based on these terminal ingredients exponentially stabilizes the desired setpoint in closed loop. Finally, we illustrate the advantages over existing data-driven MPC approaches with a numerical example.
AB - We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge. The scheme includes a terminal cost and a terminal set constraint on an extended state containing past input-output values. We provide an explicit design procedure for the corresponding terminal ingredients that only uses measured input-output data. Further, we prove that the MPC scheme based on these terminal ingredients exponentially stabilizes the desired setpoint in closed loop. Finally, we illustrate the advantages over existing data-driven MPC approaches with a numerical example.
UR - http://www.scopus.com/inward/record.url?scp=85117892449&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2101.05573
DO - 10.48550/arXiv.2101.05573
M3 - Conference article
AN - SCOPUS:85117892449
VL - 54
SP - 257
EP - 263
JO - IFAC-PapersOnLine
JF - IFAC-PapersOnLine
SN - 2405-8963
IS - 6
T2 - 7th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021)
Y2 - 11 July 2021 through 14 July 2021
ER -