Details
| Original language | English |
|---|---|
| Title of host publication | 2020 59th IEEE Conference on Decision and Control, CDC 2020 |
| Pages | 1248-1253 |
| Number of pages | 6 |
| ISBN (electronic) | 9781728174471 |
| Publication status | Published - 2020 |
Publication series
| Name | Proceedings of the IEEE Conference on Decision and Control |
|---|---|
| Volume | 2020-December |
| ISSN (Print) | 0743-1546 |
| ISSN (electronic) | 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 subject areas
- Mathematics(all)
- Control and Optimization
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Modelling and Simulation
Cite this
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2020 59th IEEE Conference on Decision and Control, CDC 2020. 2020. p. 1248-1253 9303838 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
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 -