Output-feedback model predictive control under dynamic uncertainties using integral quadratic constraints
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916912667033600 |
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| author | Schwenkel, Lukas Köhler, Johannes Müller, Matthias A. Allgöwer, Frank |
| author_facet | Schwenkel, Lukas Köhler, Johannes Müller, Matthias A. Allgöwer, Frank |
| contents | In this work, we propose an output-feedback tube-based model predictive control (MPC) scheme for linear systems under dynamic uncertainties that are described via integral quadratic constraints (IQC). By leveraging IQCs, a large class of nonlinear and dynamic uncertainties can be addressed. We leverage recent IQC synthesis tools to design a dynamic controller and an estimator that are robust to these uncertainties and minimize the size of the resulting constraint tightening in the MPC. Thereby, we show that the robust estimation problem using IQCs with peak-to-peak performance can be convexified. We guarantee recursive feasibility, robust constraint satisfaction, and input-to-state stability of the resulting MPC scheme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00196 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Output-feedback model predictive control under dynamic uncertainties using integral quadratic constraints Schwenkel, Lukas Köhler, Johannes Müller, Matthias A. Allgöwer, Frank Systems and Control Optimization and Control In this work, we propose an output-feedback tube-based model predictive control (MPC) scheme for linear systems under dynamic uncertainties that are described via integral quadratic constraints (IQC). By leveraging IQCs, a large class of nonlinear and dynamic uncertainties can be addressed. We leverage recent IQC synthesis tools to design a dynamic controller and an estimator that are robust to these uncertainties and minimize the size of the resulting constraint tightening in the MPC. Thereby, we show that the robust estimation problem using IQCs with peak-to-peak performance can be convexified. We guarantee recursive feasibility, robust constraint satisfaction, and input-to-state stability of the resulting MPC scheme. |
| title | Output-feedback model predictive control under dynamic uncertainties using integral quadratic constraints |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2504.00196 |