Control de seguimiento de trayectorias repetitivas para un quadrotor
ilustraciones, gráficas, tablas
- Autores:
-
Mozuca Tamayo, Paula Andrea
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82956
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Control de posición
Micro UAV
Seguimiento de trayectorias
Rechazo activo de perturbaciones
Observador de estados extendidos
Control de aprendizaje iterativo
Position Controller
Trajectory Tracking
Extended State Observer
Disturbance Rejection
Iterative Learning Control
Control automático
Automatic control
Mecanización
Mechanization
Aplicación informática
Computer applications
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Control de seguimiento de trayectorias repetitivas para un quadrotor |
| dc.title.translated.eng.fl_str_mv |
Repetitive trajectory tracking control for a quadrotor |
| title |
Control de seguimiento de trayectorias repetitivas para un quadrotor |
| spellingShingle |
Control de seguimiento de trayectorias repetitivas para un quadrotor 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Control de posición Micro UAV Seguimiento de trayectorias Rechazo activo de perturbaciones Observador de estados extendidos Control de aprendizaje iterativo Position Controller Trajectory Tracking Extended State Observer Disturbance Rejection Iterative Learning Control Control automático Automatic control Mecanización Mechanization Aplicación informática Computer applications |
| title_short |
Control de seguimiento de trayectorias repetitivas para un quadrotor |
| title_full |
Control de seguimiento de trayectorias repetitivas para un quadrotor |
| title_fullStr |
Control de seguimiento de trayectorias repetitivas para un quadrotor |
| title_full_unstemmed |
Control de seguimiento de trayectorias repetitivas para un quadrotor |
| title_sort |
Control de seguimiento de trayectorias repetitivas para un quadrotor |
| dc.creator.fl_str_mv |
Mozuca Tamayo, Paula Andrea |
| dc.contributor.advisor.spa.fl_str_mv |
Ramos Fuentes, Germán Andrés |
| dc.contributor.author.spa.fl_str_mv |
Mozuca Tamayo, Paula Andrea |
| dc.contributor.researchgroup.spa.fl_str_mv |
Electrical Machines & Drives, Em&D |
| dc.contributor.orcid.spa.fl_str_mv |
Mozuca Tamayo, Paula Andrea [0000-0002-5659-7956] |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Control de posición Micro UAV Seguimiento de trayectorias Rechazo activo de perturbaciones Observador de estados extendidos Control de aprendizaje iterativo Position Controller Trajectory Tracking Extended State Observer Disturbance Rejection Iterative Learning Control Control automático Automatic control Mecanización Mechanization Aplicación informática Computer applications |
| dc.subject.proposal.spa.fl_str_mv |
Control de posición Micro UAV Seguimiento de trayectorias Rechazo activo de perturbaciones Observador de estados extendidos Control de aprendizaje iterativo |
| dc.subject.proposal.eng.fl_str_mv |
Position Controller Trajectory Tracking Extended State Observer Disturbance Rejection Iterative Learning Control |
| dc.subject.unesco.spa.fl_str_mv |
Control automático Automatic control Mecanización Mechanization Aplicación informática |
| dc.subject.unesco.eng.fl_str_mv |
Computer applications |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-09 |
| dc.date.accessioned.none.fl_str_mv |
2023-01-16T21:01:29Z |
| dc.date.available.none.fl_str_mv |
2023-01-16T21:01:29Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/82956 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/82956 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
| language |
spa |
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(2020). Datasheet Crazyflie 2.1. Bitcraze. Rev. 3. Abdelmaksoud, S. I., Mailah, M., and Abdallah, A. M. (2020). Robust intelligent self-tuning active force control of a quadrotor with improved body jerk performance. IEEE Access, 8:150037–150050. Ai, W., Wang, H., and Li, X. (2019). Research and application of active disturbance rejection based iterative learning control for the brushless dc motor. In 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS), pages 1049–1054. Alaimo, A., Artale, V., Milazzo, C., and Ricciardello, A. (2013). Comparison between euler and quaternion parametrization in uav dynamics. In AIP Conference Proceedings, volume 1558, pages 1228–1231. American Institute of Physics. Albers, A., Trautmann, S., Howard, T., Trong Anh Nguyen, Frietsch, M., and Sauter, C. (2010). Semi-autonomous flying robot for physical interaction with environment. In 2010 IEEE Conference on Robotics, Automation and Mechatronics, pages 441–446. Altan, A. and Hacıoğlu, R. (2020). Model predictive control of three-axis gimbal system mounted on uav for real-time target tracking under external disturbances. Mechanical Systems and Signal Processing, 138:106548. Alvarez-Valle, R. S. and Rivadeneira, P. S. (2019). Design of controllers to track trajectories for multi-rotor unmanned aerial vehicles. In 2019 IEEE 4th Colombian Conference on Automatic Control (CCAC), pages 1–6. IEEE. Amezquita-Brooks L., Liceaga-Castro E., G.-S. M. G.-S. O. M.-V. D. (2017). Towards a standard design model for quad-rotors: A review of current models, their accuracy and a novel simplified model. Progress in Aerospace Sciences, 95:1–23. Antonio-Toledo, M. E., Sanchez, E. N., Alanis, A. Y., Flórez, J., and Perez-Cisneros, M. A. (2018). Real-time integral backstepping with sliding mode control for a quadrotor uav. IFAC-PapersOnLine, 51(13):549–554. Ardakani, M. M. G., Khong, S. Z., and Bernhardsson, B. (2017). On the convergence of iterative learning control. Automatica, 78:266–273. Arimoto, S., Kawamura, S., and Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic systems, 1(2):123–140. Bhattacharjee, D. and Subbarao, K. (2020). Robust control strategy for quadcopters using sliding mode control and model predictive control. In AIAA Scitech 2020 Forum, page 2071. Bitcraze, Crazyflie 2.1 (2022). www.bitcraze.io/products/crazyflie-2-1/, 14 de mayo de 2022. Bristow, D. A., Tharayil, M., and Alleyne, A. G. (2006). A survey of iterative learning control. IEEE control systems magazine, 26(3):96–114. C. Teuliére, E. M. and Eck, L. (2015). 3-d model-based tracking for uav indoor localization. IEEE Transactions on Cybernetics, 45(5):869–879. Chen, Y. and Wen, C. (1999). Iterative learning control: convergence, robustness and applications. Springer. Chovancová, A., Fico, T., Chovanec, L., and Hubinsk, P. (2014). Mathematical modelling and parameter identification of quadrotor (a survey). Procedia Engineering, 96:172–181. Christopher D. McKinnon, A. P. S. (2020). Estimating and reacting to forces and torques resulting from common aerodynamic disturbances acting on quadrotors. Robotics and Autonomous Systems, 123. Cortés, J. A. and Ramos, G. A. (2015). Control gpi-repetitivo para sistemas lineales con incertidumbre/variación en los parámetros. TecnoLógicas, 18(34):13–24. Cortés-Romero, J., Ramos, G. A., and Coral-Enriquez, H. (2014). Generalized proportional integral control for periodic signals under active disturbance rejection approach. ISA transactions, 53(6):1901–1909. Criado, R. M. and Rubio, F. R. (2015). Autonomous path tracking control design for a comercial quadcopter. IFAC-PapersOnLine, 48(9):73–78. Degen, N. and Schoellig, A. P. (2014). Design of norm-optimal iterative learning controllers: The effect of an iteration-domain kalman filter for disturbance estimation. In 53rd IEEE Conference on Decision and Control, pages 3590–3596. IEEE. Dong, J. and He, B. (2018). Novel fuzzy pid-type iterative learning control for quadrotor uav. Sensors, 19(1):24. Efe, M. ff. (2011). Neural network assisted computationally simple piλd µ control of a qua drotor uav. IEEE Transactions on Industrial Informatics, 7(2):354–361 Fabrice, P. M. (2018). Control of the quadcopter crazyflie. Technical report, The University of manchester. Fernández, R. A. S., Dominguez, S., and Campoy, P. (2017). L 1 adaptive control for wind gust rejection in quad-rotor uav wind turbine inspection. In 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pages 1840–1849. IEEE. Fernández-Caballero, A., Maria Belmonte, L., Morales, R., and Andres Somolinos, J. (2015). Generalized proportional integral control for an unmanned quadrotor system. International Journal of Advanced Robotic Systems, 12(7):85. Freeman*, C., Lewin, P., and Rogers, E. (2005). Experimental evaluation of iterative learning control algorithms for non-minimum phase plants. International Journal of Control, 78(11):826–846. Gao, Z. (2006). Active disturbance rejection control: a paradigm shift in feedback control system design. In 2006 American control conference, pages 7–pp. IEEE. Han, J. (2009). From pid to active disturbance rejection control. IEEE transactions on Industrial Electronics, 56(3):900–906. Hassanalian, M. and Abdelkefi, A. (2017). Classifications, applications, and design challenges of drones: A review. Progress in Aerospace Sciences, 91:99–131. Huang, D., Min, D., Jian, Y., and Li, Y. (2020). Current-cycle iterative learning control for high-precision position tracking of piezoelectric actuator system via active disturbance rejection control for hysteresis compensation. IEEE Transactions on Industrial Electronics, 67(10):8680–8690. Huang, Y. and Xue, W. (2014). Active disturbance rejection control: methodology and theoretical analysis. ISA transactions, 53(4):963–976. Hunt, K. J., Sbarbaro, D., Żbikowski, R., and Gawthrop, P. J. (1992). Neural networks for control systems—a survey. Automatica, 28(6):1083–1112. Islam, M., Okasha, M., and Sulaeman, E. (2019). A model predictive control (mpc) approach on unit quaternion orientation based quadrotor for trajectory tracking. International Journal of Control, Automation and Systems, 17(11):2819–2832. J. Tisdale, Z. K. and Hedrick, J. (2009). Autonomous uav path planning and estimation. IEEE Robotics Automation Magazine, 16(2):35–42. J.D.C. Tsouros, A. Triantafyllou, S. B. and Sarigannidis, P. G. (2019). Data acquisition and analysis methods in uav- based applications for precision agriculture. pages 377–384, Santorini Island Greece. Jiang, F., Pourpanah, F., and Hao, Q. (2020). Design, implementation, and evaluation of a neural-network-based quadcopter uav system. IEEE Transactions on Industrial Electronics, 67(3):2076–2085. Kamel, M., Burri, M., and Siegwart, R. (2017). Linear vs nonlinear mpc for trajectory tracking applied to rotary wing micro aerial vehicles. IFAC-PapersOnLine, 50(1):3463– 3469. Ke, C., Ren, J., and Quan, Q. (2018). Saturated d-type ilc for multicopter trajectory tracking based on additive state decomposition. In 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), pages 1146–1151. IEEE. Kwon, W., Park, J. H., Lee, M., Her, J., Kim, S.-H., and Seo, J.-W. (2019). Robust autonomous navigation of unmanned aerial vehicles (uavs) for warehouses’ inventory application. IEEE Robotics and Automation Letters, 5(1):243–249. Labbadi, M., Boukal, Y., and Cherkaoui, M. (2020). Path following control of quadrotor uav with continuous fractional-order super twisting sliding mode. Journal of Intelligent & Robotic Systems, pages 1–23. Lei, W., Li, C., and Chen, M. Z. (2018). Robust adaptive tracking control for quadrotors by combining pi and self-tuning regulator. IEEE Transactions on Control Systems Technology, 27(6):2663–2671. Lei, Y. and Wang, H. (2020). Aerodynamic performance of a quadrotor mav considering the horizontal wind. Ieee Access, 8:109421–109428. Li, Q., Qian, J., Zhu, Z., Bao, X., Helwa, M. K., and Schoellig, A. P. (2017). Deep neural networks for improved, impromptu trajectory tracking of quadrotors. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 5183–5189. Liang, X., Zheng, M., and Zhang, F. (2018). A scalable model-based learning algorithm with application to uavs. IEEE Control Systems Letters, 2(4):839–844. Liu, C., Pan, J., and Chang, Y. (2016). Pid and lqr trajectory tracking control for an unmanned quadrotor helicopter: Experimental studies. In 2016 35th Chinese Control Conference (CCC), pages 10845–10850. López-Gutiérrez, R., Rodriguez-Mata, A. E., Salazar, S., González-Hernández, I., and Lozano, R. (2017). Robust quadrotor control: attitude and altitude real-time results. 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Wang, J., Zhu, S., Luo, X., Chen, G., Xu, Z., Liu, X., and Li, Y. (2020). Refined microscale geological disaster susceptibility evaluation based on uav tilt photography data and weighted certainty factor method in mountainous area. Ecotoxicology and Environmental Safety, 189:110005. Xu, J., Li, D., Ma, X., Zhang, B., and Jin, Q. (2020). Study of an adaptive iterative learning control based on extended state observer in path tracking of double joint robot. In 2020 39th Chinese Control Conference (CCC), pages 2037–2042. Yang, S., Li, K., and Shi, J. (2009). Design and simulation of the longitudinal autopilot of uav based on self-adaptive fuzzy pid control. In 2009 International Conference on Computational Intelligence and Security, volume 1, pages 634–638. Ye, J., Wang, J., Song, T., Wu, Z., and Tang, P. (2021). Nonlinear modeling the quadcopter considering the aerodynamic interaction. IEEE Access, 9:134716–134732. Zhang, J., R. Z. D. C. e. a. (2019). Adaptive fuzzy global sliding mode control for trajectory tracking of quadrotor uavs. Nonlinear Dyn, 97:609–627. Zhang, X., Li, X., Wang, K., and Lu, Y. (2014). A survey of modelling and identification ofquadrotor robot. In Abstract and Applied Analysis, volume 2014. Hindawi. Zhao, B., Xian, B., Zhang, Y., and Zhang, X. (2015). Nonlinear robust adaptive tracking control of a quadrotor uav via immersion and invariance methodology. IEEE Transactions on Industrial Electronics, 62(5):2891–2902. Zhaowei, M., Tianjiang, H., Lincheng, S., Weiwei, K., Boxin, Z., and Kaidi, Y. (2015). An iterative learning controller for quadrotor uav path following at a constant altitude. In 2015 34th Chinese control conference (CCC), pages 4406–4411. IEEE. |
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Bogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrial |
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Facultad de Ingeniería |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Ramos Fuentes, Germán Andrés6be566ba08b29743f6865afa1263f9ba600Mozuca Tamayo, Paula Andrea6470fa30638140687b2eafb696071d02600Electrical Machines & Drives, Em&DMozuca Tamayo, Paula Andrea [0000-0002-5659-7956]2023-01-16T21:01:29Z2023-01-16T21:01:29Z2022-09https://repositorio.unal.edu.co/handle/unal/82956Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasEl control de posición de un quadcopter permite seguir autónomamente diferentes trayectorias, sin necesidad de un piloto. Cuando los caminos a seguir son repetitivos es posible incluir un control de aprendizaje iterativo (ILC) que en cada vuelo modifica la referencia de acuerdo al error de intentos previos. El algoritmo ILC es de lazo abierto por lo que se requiere implementar junto con un control de realimentación. Este control de realimentación debe mantener el desempeño a pesar de que el modelo del dron es no lineal y con alta incerti dumbre, y que, además, en el ambiente de vuelo pueden aparecer diferentes perturbaciones. Una estrategia de control que tiene en cuenta estos problemas es la basada en rechazo activo de perturbaciones (ADRC). La estimación de la perturbación total, que incluye tanto las no linealidades como la incertidumbre, permite mejorar el rechazo de perturbaciones y la robustez del sistema. Este trabajo muestra el diseño y la implementación de un control ILC basado en ADRC, mostrando como mejora considerablemente el desempeño al compararse con un control tradicional. (Texto tomado de la fuente).To follow trajectories autonomously, without a pilot, the quadcoper needs a position controller. When the paths to follow are repetitive, an Iterative Learning Control (ILC) can be included, modifying the reference according to the error of previous attempts. As the ILC algorithm works in open loop, it must be implemented with a feedback controller. This feedback controller must maintain the performance even though the system is nonlinear, with high uncertainty and that random disturbances appear in the flight environment. A strategy that in its design includes each of these problems is the feedback control based on Active Disturbance Rejection (ADRC). An estimate of the total perturbation, which contains the nonlinearities and the uncertainties, is included in the control law to improve performance and robustness. This research work shows the design and implementation of an ILC position controller based on ADRC, comparing the high performance obtained with a clasic control.División de Investigación Sede Bogotá (DIB)MaestríaMagíster en Ingeniería - Automatización IndustrialControl-Robótica Móvilxix, 79 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaControl de posiciónMicro UAVSeguimiento de trayectoriasRechazo activo de perturbacionesObservador de estados extendidosControl de aprendizaje iterativoPosition ControllerTrajectory TrackingExtended State ObserverDisturbance RejectionIterative Learning ControlControl automáticoAutomatic controlMecanizaciónMechanizationAplicación informáticaComputer applicationsControl de seguimiento de trayectorias repetitivas para un quadrotorRepetitive trajectory tracking control for a quadrotorTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM(2020). Datasheet Crazyflie 2.1. Bitcraze. Rev. 3.Abdelmaksoud, S. I., Mailah, M., and Abdallah, A. M. (2020). Robust intelligent self-tuning active force control of a quadrotor with improved body jerk performance. IEEE Access, 8:150037–150050.Ai, W., Wang, H., and Li, X. (2019). Research and application of active disturbance rejection based iterative learning control for the brushless dc motor. In 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS), pages 1049–1054.Alaimo, A., Artale, V., Milazzo, C., and Ricciardello, A. (2013). Comparison between euler and quaternion parametrization in uav dynamics. In AIP Conference Proceedings, volume 1558, pages 1228–1231. American Institute of Physics.Albers, A., Trautmann, S., Howard, T., Trong Anh Nguyen, Frietsch, M., and Sauter, C. (2010). Semi-autonomous flying robot for physical interaction with environment. In 2010 IEEE Conference on Robotics, Automation and Mechatronics, pages 441–446.Altan, A. and Hacıoğlu, R. (2020). 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IEEE.Control de seguimiento de trayectorias repetitivas para un dron en un ambiente con perturbaciones e iluminación no controladaUniversidad Nacional de ColombiaEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/82956/3/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD53ORIGINAL1032486505.pdf1032486505.pdfTesis de Maestría en Ingeniería - Automatización Industrialapplication/pdf7436647https://repositorio.unal.edu.co/bitstream/unal/82956/4/1032486505.pdf6bf3f777483ef55cee9007ab0067760fMD54unal/82956oai:repositorio.unal.edu.co:unal/829562023-01-16 16:03:50.949Repositorio Institucional Universidad Nacional de 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