Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2020 59th IEEE Conference on Decision and Control, CDC 2020 |
| Seiten | 1248-1253 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781728174471 |
| Publikationsstatus | Veröffentlicht - 2020 |
Publikationsreihe
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Band | 2020-December |
| ISSN (Print) | 0743-1546 |
| ISSN (elektronisch) | 2576-2370 |
Abstract
We consider the problem of steering a multi-agent system to consensus in their outputs. The agents' dynamics are assumed to be heterogeneous, linear, discrete-time and subject to local convex state and input constraints. We present a sequential distributed model predictive control algorithm that asymptotically steers the agents to consensus in their outputs. In their respective model predictive control problems, the agents minimise the distance of a local target output to those of their neighbours while simultaneously tracking the corresponding target steady-state and input pair. We only require the exchange of these target outputs in the scheme whereas the current state and entire predicted trajectories are not shared.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Steuerung und Optimierung
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Mathematik (insg.)
- Modellierung und Simulation
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2020 59th IEEE Conference on Decision and Control, CDC 2020. 2020. S. 1248-1253 9303838 (Proceedings of the IEEE Conference on Decision and Control; Band 2020-December).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung
}
TY - GEN
T1 - Distributed Model Predictive Control for Consensus of Constrained Heterogeneous Linear Systems
AU - Hirche, M.
AU - Köhler, Philipp N.
AU - Müller, Matthias
AU - Allgöwer, Frank
N1 - Funding information: *This work is funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016; and grant AL 316/11-2 - 244600449.
PY - 2020
Y1 - 2020
N2 - We consider the problem of steering a multi-agent system to consensus in their outputs. The agents' dynamics are assumed to be heterogeneous, linear, discrete-time and subject to local convex state and input constraints. We present a sequential distributed model predictive control algorithm that asymptotically steers the agents to consensus in their outputs. In their respective model predictive control problems, the agents minimise the distance of a local target output to those of their neighbours while simultaneously tracking the corresponding target steady-state and input pair. We only require the exchange of these target outputs in the scheme whereas the current state and entire predicted trajectories are not shared.
AB - We consider the problem of steering a multi-agent system to consensus in their outputs. The agents' dynamics are assumed to be heterogeneous, linear, discrete-time and subject to local convex state and input constraints. We present a sequential distributed model predictive control algorithm that asymptotically steers the agents to consensus in their outputs. In their respective model predictive control problems, the agents minimise the distance of a local target output to those of their neighbours while simultaneously tracking the corresponding target steady-state and input pair. We only require the exchange of these target outputs in the scheme whereas the current state and entire predicted trajectories are not shared.
UR - http://www.scopus.com/inward/record.url?scp=85099880263&partnerID=8YFLogxK
U2 - 10.1109/cdc42340.2020.9303838
DO - 10.1109/cdc42340.2020.9303838
M3 - Conference contribution
SN - 978-1-7281-7446-4
SN - 978-1-7281-7448-8
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1248
EP - 1253
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
ER -