SINDy and PD-Based UAV Dynamics Identification for MPC
This study proposes a comprehensive framework for the identification of nonlinear dynamics in Unmanned Aerial Vehicles (UAVs), integrating data-driven methodologies with theoretical modeling approaches. Two principal techniques are employed: Proportional-Derivative (PD)-based control input approxima...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/71 |
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| author | Bryan S. Guevara José Varela-Aldás Daniel C. Gandolfo Juan M. Toibero |
| author_facet | Bryan S. Guevara José Varela-Aldás Daniel C. Gandolfo Juan M. Toibero |
| author_sort | Bryan S. Guevara |
| collection | DOAJ |
| description | This study proposes a comprehensive framework for the identification of nonlinear dynamics in Unmanned Aerial Vehicles (UAVs), integrating data-driven methodologies with theoretical modeling approaches. Two principal techniques are employed: Proportional-Derivative (PD)-based control input approximation and Sparse Identification of Nonlinear Dynamics (SINDy). Addressing the inherent platform constraints—where control inputs are restricted to specific attitude angles and z-axis velocities—thrust and torque are approximated via a PD controller, which serves as a practical intermediary for facilitating nonlinear system identification. Both methodologies leverage data-driven strategies to construct compact and interpretable models from experimental data, capturing significant nonlinearities with high fidelity. The resulting models are rigorously evaluated within a Model Predictive Control (MPC) framework, demonstrating their efficacy in precise trajectory tracking. Furthermore, the integration of data-driven insights enhances the accuracy of the identified models and improves control performance. This framework offers a robust and adaptable solution for analyzing UAV dynamics under realistic operational conditions, emphasizing the comparative strengths and applicability of each modeling approach. |
| format | Article |
| id | doaj-art-46ad672473c14e76a709e5cd3a380266 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-46ad672473c14e76a709e5cd3a3802662025-01-24T13:29:52ZengMDPI AGDrones2504-446X2025-01-01917110.3390/drones9010071SINDy and PD-Based UAV Dynamics Identification for MPCBryan S. Guevara0José Varela-Aldás1Daniel C. Gandolfo2Juan M. Toibero3Instituto de Automática, Universidad Nacional de San Juan—CONICET, Av. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaCentro de Investigación en Mecatrónica y Sistemas Interactivos (MIST), Carrera de Ingeniería Industrial, Universidad Tecnológica Indoamérica, Ambato 180103, EcuadorInstituto de Automática, Universidad Nacional de San Juan—CONICET, Av. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaInstituto de Automática, Universidad Nacional de San Juan—CONICET, Av. San Martín Oeste 1109, San Juan J5400ARL, ArgentinaThis study proposes a comprehensive framework for the identification of nonlinear dynamics in Unmanned Aerial Vehicles (UAVs), integrating data-driven methodologies with theoretical modeling approaches. Two principal techniques are employed: Proportional-Derivative (PD)-based control input approximation and Sparse Identification of Nonlinear Dynamics (SINDy). Addressing the inherent platform constraints—where control inputs are restricted to specific attitude angles and z-axis velocities—thrust and torque are approximated via a PD controller, which serves as a practical intermediary for facilitating nonlinear system identification. Both methodologies leverage data-driven strategies to construct compact and interpretable models from experimental data, capturing significant nonlinearities with high fidelity. The resulting models are rigorously evaluated within a Model Predictive Control (MPC) framework, demonstrating their efficacy in precise trajectory tracking. Furthermore, the integration of data-driven insights enhances the accuracy of the identified models and improves control performance. This framework offers a robust and adaptable solution for analyzing UAV dynamics under realistic operational conditions, emphasizing the comparative strengths and applicability of each modeling approach.https://www.mdpi.com/2504-446X/9/1/71SINDynonlinear identificationMPCUAVdata-driven modeling |
| spellingShingle | Bryan S. Guevara José Varela-Aldás Daniel C. Gandolfo Juan M. Toibero SINDy and PD-Based UAV Dynamics Identification for MPC Drones SINDy nonlinear identification MPC UAV data-driven modeling |
| title | SINDy and PD-Based UAV Dynamics Identification for MPC |
| title_full | SINDy and PD-Based UAV Dynamics Identification for MPC |
| title_fullStr | SINDy and PD-Based UAV Dynamics Identification for MPC |
| title_full_unstemmed | SINDy and PD-Based UAV Dynamics Identification for MPC |
| title_short | SINDy and PD-Based UAV Dynamics Identification for MPC |
| title_sort | sindy and pd based uav dynamics identification for mpc |
| topic | SINDy nonlinear identification MPC UAV data-driven modeling |
| url | https://www.mdpi.com/2504-446X/9/1/71 |
| work_keys_str_mv | AT bryansguevara sindyandpdbaseduavdynamicsidentificationformpc AT josevarelaaldas sindyandpdbaseduavdynamicsidentificationformpc AT danielcgandolfo sindyandpdbaseduavdynamicsidentificationformpc AT juanmtoibero sindyandpdbaseduavdynamicsidentificationformpc |