Open access
Date
2022Type
- Conference Paper
ETH Bibliography
yes
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Abstract
We present a novel data-driven distributionally robust Model Predictive Control formulation for unknown discrete-time linear time-invariant systems affected by unknown and possibly unbounded additive uncertainties. We use off-line collected data and an approximate model of the dynamics to formulate a finite-horizon optimization problem. To account for both the uncertainty related to the dynamics and the disturbance acting on the system, we resort to a distributionally robust formulation that optimizes the cost expectation while satisfying Conditional Value-at-Risk constraints with respect to the worst-case probability distributions of the uncertainties within an ambiguity set defined using the Wasserstein metric. Using results from the distributionally robust optimization literature we derive a tractable finite-dimensional convex optimization problem with finite-sample guarantees for the class of convex piecewise affine cost and constraint functions. The performance of the proposed algorithm is demonstrated in closed-loop simulation on a simple numerical example. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000584148Publication status
publishedExternal links
Book title
2022 IEEE 61st Conference on Decision and Control (CDC)Pages / Article No.
Publisher
IEEEEvent
Organisational unit
03751 - Lygeros, John / Lygeros, John
Funding
787845 - Optimal control at large (EC)
Related publications and datasets
Is supplemented by: https://doi.org/10.3929/ethz-b-000584149
Is new version of: https://doi.org/10.3929/ethz-b-000584189
Notes
Conference lecture held on December 8, 2022More
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ETH Bibliography
yes
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