Open access
Date
2023Type
- Conference Paper
ETH Bibliography
yes
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Abstract
Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as nonlinear optimization problems. Increasing the number of scenarios improves robustness, while increasing the size of the optimization problems. Mitigating the size of the problem by reducing the number of scenarios requires knowledge about how the uncertainty affects the system. This paper draws from local reduction methods used in semi-infinite optimization to solve robust optimal control problems with parametric uncertainty. We show that nonlinear robust optimal control problems are equivalent to semi-infinite optimization problems and can be solved by local reduction. By iteratively adding interim globally worst-case scenarios to the problem, methods based on local reduction provide a way to manage the total number of scenarios. In particular, we show that local reduction methods find worst case scenarios that are not on the boundary of the uncertainty set. The proposed approach is illustrated with a case study with both parametric and additive time-varying uncertainty. The number of scenarios obtained from local reduction is 101, smaller than in the case when all 2¹⁴⁺³ˣ¹⁹² boundary scenarios are considered. A validation with randomly drawn scenarios shows that our proposed approach reduces the number of scenarios and ensures robustness even if local solvers are used. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000615618Publication status
publishedExternal links
Journal / series
IFAC-PapersOnLineVolume
Pages / Article No.
Publisher
ElsevierEvent
Subject
Optimization; Mathematical programming; Trajectory optimization; Uncertainty; Iterative methods; Numerical simulation; Robust controlOrganisational unit
03751 - Lygeros, John / Lygeros, John
Funding
787845 - Optimal control at large (EC)
Notes
This work has received funding from the EPSRC (Engineering and Physical Sciences) under the Active Building Centre project (reference number: EP/V012053/1) - conceptual work and writing. M. Zagorowska also acknowledges funding from the European Research Council (ERC) under the H2020 Advanced Grant no. 787845 (OCAL) - writing and submission.More
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ETH Bibliography
yes
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