Robust Nonlinear Optimal Control via System Level Synthesis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866908473577439232 |
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| author | Leeman, Antoine P. Köhler, Johannes Zanelli, Andrea Bennani, Samir Zeilinger, Melanie N. |
| author_facet | Leeman, Antoine P. Köhler, Johannes Zanelli, Andrea Bennani, Samir Zeilinger, Melanie N. |
| contents | This paper 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 over-bound 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_04943 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Robust Nonlinear Optimal Control via System Level Synthesis Leeman, Antoine P. Köhler, Johannes Zanelli, Andrea Bennani, Samir Zeilinger, Melanie N. Optimization and Control Systems and Control This paper 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 over-bound 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. |
| title | Robust Nonlinear Optimal Control via System Level Synthesis |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2301.04943 |