Data-fused MPC with Guarantees: Application to Flying Humanoid Robots
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| author | Gorbani, Davide Elobaid, Mohamed L'Erario, Giuseppe Mohamed, Hosameldin Awadalla Omer Pucci, Daniele |
| author_facet | Gorbani, Davide Elobaid, Mohamed L'Erario, Giuseppe Mohamed, Hosameldin Awadalla Omer Pucci, Daniele |
| contents | This paper introduces a Data-Fused Model Predictive Control (DFMPC) framework that combines physics-based models with data-driven representations of unknown dynamics. Leveraging Willems' Fundamental Lemma and an artificial equilibrium formulation, the method enables tracking of changing, potentially unreachable setpoints while explicitly handling measurement noise through slack variables and regularization. We provide guarantees of recursive feasibility and practical stability under input-output constraints for a specific class of reference signals. The approach is validated on the iRonCub flying humanoid robot, integrating analytical momentum models with data-driven turbine dynamics. Simulations show improved tracking and robustness compared to a purely model-based MPC, while maintaining real-time feasibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10353 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-fused MPC with Guarantees: Application to Flying Humanoid Robots Gorbani, Davide Elobaid, Mohamed L'Erario, Giuseppe Mohamed, Hosameldin Awadalla Omer Pucci, Daniele Systems and Control Robotics This paper introduces a Data-Fused Model Predictive Control (DFMPC) framework that combines physics-based models with data-driven representations of unknown dynamics. Leveraging Willems' Fundamental Lemma and an artificial equilibrium formulation, the method enables tracking of changing, potentially unreachable setpoints while explicitly handling measurement noise through slack variables and regularization. We provide guarantees of recursive feasibility and practical stability under input-output constraints for a specific class of reference signals. The approach is validated on the iRonCub flying humanoid robot, integrating analytical momentum models with data-driven turbine dynamics. Simulations show improved tracking and robustness compared to a purely model-based MPC, while maintaining real-time feasibility. |
| title | Data-fused MPC with Guarantees: Application to Flying Humanoid Robots |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2509.10353 |