Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems
This work presents the design and implementation of a data-driven Nonlinear Model Predictive Control (NMPC) framework for an Uncrewed Aerial Vehicle (UAV) equipped with a 3-DOF robotic arm. Real-world data was collected using the Matrice 100 platform and Dynamixel MX-28AR actuators to identify a hig...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11020657/ |
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| author | Bryan S. Guevara Jose Varela-Aldas Viviana Moya Manuel Cardona Daniel C. Gandolfo Juan M. Toibero |
| author_facet | Bryan S. Guevara Jose Varela-Aldas Viviana Moya Manuel Cardona Daniel C. Gandolfo Juan M. Toibero |
| author_sort | Bryan S. Guevara |
| collection | DOAJ |
| description | This work presents the design and implementation of a data-driven Nonlinear Model Predictive Control (NMPC) framework for an Uncrewed Aerial Vehicle (UAV) equipped with a 3-DOF robotic arm. Real-world data was collected using the Matrice 100 platform and Dynamixel MX-28AR actuators to identify a high-dimensional linear model via Dynamic Mode Decomposition with Control (DMDc), capturing the interactions between the aerial vehicle and the manipulator across 21 state variables. This DMDc-based model is embedded within the NMPC formulation to predict system behavior over finite horizons. The UAV’s orientation is represented using quaternions, enabling continuous and singularity-free attitude control. Additionally, the redundancy of the UAV-manipulator system allows for the integration of secondary objectives into the cost function, supporting flexible task execution. To meet real-time requirements, the control problem is solved using the Acados solver. The resulting controller achieves high-precision tracking while managing internal constraints, demonstrating the potential of data-driven NMPC in aerial manipulation tasks. |
| format | Article |
| id | doaj-art-b631940b72b942cf830f68c4932f1d6f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b631940b72b942cf830f68c4932f1d6f2025-08-20T03:09:45ZengIEEEIEEE Access2169-35362025-01-0113968349684310.1109/ACCESS.2025.357572411020657Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator SystemsBryan S. Guevara0https://orcid.org/0000-0002-7830-0498Jose Varela-Aldas1https://orcid.org/0000-0002-4084-1424Viviana Moya2https://orcid.org/0000-0002-6064-6925Manuel Cardona3https://orcid.org/0000-0002-4211-3498Daniel C. Gandolfo4https://orcid.org/0000-0002-4938-2105Juan M. Toibero5https://orcid.org/0000-0001-9347-566XInstituto de Automática, Universidad Nacional de San Juan-CONICET, San Juan, ArgentinaCentro de Investigación MIST, Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato, EcuadorFacultad de Ciencias Técnicas, Universidad Internacional del Ecuador (UIDE), Quito, EcuadorResearch Department, Universidad Don Bosco, Soyapango, El SalvadorInstituto de Automática, Universidad Nacional de San Juan-CONICET, San Juan, ArgentinaInstituto de Automática, Universidad Nacional de San Juan-CONICET, San Juan, ArgentinaThis work presents the design and implementation of a data-driven Nonlinear Model Predictive Control (NMPC) framework for an Uncrewed Aerial Vehicle (UAV) equipped with a 3-DOF robotic arm. Real-world data was collected using the Matrice 100 platform and Dynamixel MX-28AR actuators to identify a high-dimensional linear model via Dynamic Mode Decomposition with Control (DMDc), capturing the interactions between the aerial vehicle and the manipulator across 21 state variables. This DMDc-based model is embedded within the NMPC formulation to predict system behavior over finite horizons. The UAV’s orientation is represented using quaternions, enabling continuous and singularity-free attitude control. Additionally, the redundancy of the UAV-manipulator system allows for the integration of secondary objectives into the cost function, supporting flexible task execution. To meet real-time requirements, the control problem is solved using the Acados solver. The resulting controller achieves high-precision tracking while managing internal constraints, demonstrating the potential of data-driven NMPC in aerial manipulation tasks.https://ieeexplore.ieee.org/document/11020657/Aerial manipulatorNMPCCasADiAcadosreal-time optimizationUAV dynamics |
| spellingShingle | Bryan S. Guevara Jose Varela-Aldas Viviana Moya Manuel Cardona Daniel C. Gandolfo Juan M. Toibero Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems IEEE Access Aerial manipulator NMPC CasADi Acados real-time optimization UAV dynamics |
| title | Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems |
| title_full | Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems |
| title_fullStr | Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems |
| title_full_unstemmed | Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems |
| title_short | Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems |
| title_sort | data driven model predictive control for trajectory tracking in uav manipulator systems |
| topic | Aerial manipulator NMPC CasADi Acados real-time optimization UAV dynamics |
| url | https://ieeexplore.ieee.org/document/11020657/ |
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