Tube-Based Model Predictive Control with Random Fourier Features for Nonlinear Systems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908667239989248 |
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| author | Bokor, Ákos M. Dózsa, Tamás Biertümpfel, Felix Szabó, Ádám |
| author_facet | Bokor, Ákos M. Dózsa, Tamás Biertümpfel, Felix Szabó, Ádám |
| contents | This paper presents a computationally efficient approach for robust Model Predictive Control of nonlinear systems by combining Random Fourier Features with tube-based MPC. Tube-based Model Predictive Control provides robust constraint satisfaction under bounded model uncertainties arising from approximation errors and external disturbances. The Random Fourier Features method approximates nonlinear system dynamics by solving a numerically tractable least-squares problem, thereby reducing the approximation error. We develop the integration of RFF-based residual learning with tube MPC and demonstrate its application to an autonomous vehicle path-tracking problem using a nonlinear bicycle model. Compared to the linear baseline, the proposed method reduces the tube size by approximately 50%, leading to less conservative behavior and resulting in around 70% smaller errors in the test scenario. Furthermore, the proposed method achieves real-time performance while maintaining provable robustness guarantees. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16425 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tube-Based Model Predictive Control with Random Fourier Features for Nonlinear Systems Bokor, Ákos M. Dózsa, Tamás Biertümpfel, Felix Szabó, Ádám Systems and Control This paper presents a computationally efficient approach for robust Model Predictive Control of nonlinear systems by combining Random Fourier Features with tube-based MPC. Tube-based Model Predictive Control provides robust constraint satisfaction under bounded model uncertainties arising from approximation errors and external disturbances. The Random Fourier Features method approximates nonlinear system dynamics by solving a numerically tractable least-squares problem, thereby reducing the approximation error. We develop the integration of RFF-based residual learning with tube MPC and demonstrate its application to an autonomous vehicle path-tracking problem using a nonlinear bicycle model. Compared to the linear baseline, the proposed method reduces the tube size by approximately 50%, leading to less conservative behavior and resulting in around 70% smaller errors in the test scenario. Furthermore, the proposed method achieves real-time performance while maintaining provable robustness guarantees. |
| title | Tube-Based Model Predictive Control with Random Fourier Features for Nonlinear Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.16425 |