Robust Nonlinear Optimal Control via System Level Synthesis
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Author / Creator
Date
2025-07
Publication Type
Journal Article
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yes
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Abstract
This article addresses the problem of finite horizon constrained robust optimal control for nonlinear systems subject to norm-bounded disturbances. To this end, the underlying uncertain nonlinear system is decomposed based on a first-order Taylor series expansion into a nominal system and an error (deviation) described as an uncertain linear time-varying system. This decomposition allows us to leverage system level synthesis to jointly optimize an affine error feedback, a nominal nonlinear trajectory, and, most importantly, a dynamic linearization error overbound used to ensure robust constraint satisfaction for the nonlinear system. The proposed approach thereby results in less conservative planning compared with state-of-the-art techniques. We demonstrate the benefits of the proposed approach to control the rotational motion of a rigid body subject to state and input constraints.
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published
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Journal / series
Volume
70 (7)
Pages / Article No.
4780 - 4787
Publisher
IEEE
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Date collected
Date created
Subject
NL predictive control; nonlinear systems; optimal control; robust control; system level synthesis (SLS)
Organisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
Notes
This work has been supported by the European Space Agency under OSIP 4000133352, the Swiss Space Center, and the Swiss National Science Foundation under NCCR Automation.
Funding Info about funding
180545 - NCCR Automation (phase I) (SNF)
Related publications and datasets
Is new version of: https://doi.org/10.3929/ethz-b-000611661
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