Active exploration in adaptive model predictive control
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Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
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OPEN ACCESS
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Abstract
A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant systems subject to bounded disturbances and parametric uncertainty in the state-space matrices. Online set-membership identification is performed to reduce the uncertainty and thus control affects both the informativity of identification and the system’s performance. The main contribution of the paper is to include this dual effect in the MPC optimization problem using a predicted worst-case cost in the objective function. This allows the controller to perform active exploration, that is, the control input reduces the uncertainty in the regions of the parameter space that have most influence on the performance. Additionally, the MPC algorithm ensures robust constraint satisfaction of state and input constraints. Advantages of the proposed algorithm are shown by comparing it to a passive adaptive MPC algorithm from the literature.
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Publication status
published
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Editor
Book title
2020 59th IEEE Conference on Decision and Control (CDC)
Journal / series
Volume
Pages / Article No.
6186 - 6191
Publisher
IEEE
Event
59th IEEE Conference on Decision and Control (CDC 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
08814 - Smith, Roy (Tit.-Prof.) (ehemalig) / Smith, Roy (Tit.-Prof.) (former)
Notes
Funding
178890 - Modeling, Identification and Control of Periodic Systems in Energy Applications (SNF)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000552506