A distributed framework for linear adaptive MPC
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Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network systems featuring multiple agents and limited communication. To solve the problem in a distributed manner, structure is imposed on the control design ingredients without sacrificing performance. Decentralized and distributed adaptation schemes that allow for a reduction of the uncertainty online compatibly with the network topology are also proposed. The algorithm ensures robust constraint satisfaction, recursive feasibility and finite gain L2 stability, and yields lower closed-loop cost compared to robust distributed MPC in simulations.
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Publication status
published
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Editor
Book title
2021 60th IEEE Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
460 - 465
Publisher
IEEE
Event
60th Conference on Decision and Control (CDC 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former)
03751 - Lygeros, John / Lygeros, John
Notes
Conference lecture held on December 13, 2021
Funding
178890 - Modeling, Identification and Control of Periodic Systems in Energy Applications (SNF)
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