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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.00681 |
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| _version_ | 1866909826247819264 |
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| author | Teutsch, Johannes Kerz, Sebastian Wollherr, Dirk Leibold, Marion |
| author_facet | Teutsch, Johannes Kerz, Sebastian Wollherr, Dirk Leibold, Marion |
| contents | We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundamental lemma and requires only a single persistently exciting input-output data trajectory. Compared to current state-of-the-art approaches, we do not rely on availability of exact disturbance data. Instead, we leverage a novel parameterization of the unknown disturbance data considering consistency with the measured data and the system class. This allows for deterministic approximation of the chance constraints in a sampling-based fashion. A robust constraint on the first predicted step enables recursive feasibility, closed-loop constraint satisfaction, and robust asymptotic stability in expectation under standard assumptions. A numerical example demonstrates the efficiency of the proposed control scheme. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00681 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty - Extended Version Teutsch, Johannes Kerz, Sebastian Wollherr, Dirk Leibold, Marion Systems and Control We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundamental lemma and requires only a single persistently exciting input-output data trajectory. Compared to current state-of-the-art approaches, we do not rely on availability of exact disturbance data. Instead, we leverage a novel parameterization of the unknown disturbance data considering consistency with the measured data and the system class. This allows for deterministic approximation of the chance constraints in a sampling-based fashion. A robust constraint on the first predicted step enables recursive feasibility, closed-loop constraint satisfaction, and robust asymptotic stability in expectation under standard assumptions. A numerical example demonstrates the efficiency of the proposed control scheme. |
| title | Sampling-based Stochastic Data-driven Predictive Control under Data Uncertainty - Extended Version |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2402.00681 |