Diseño de un controlador de dos etapas para el balanceo y la estabilización el péndulo de Furuta

dc.contributor.advisorMontoya Girlado, Oscar Danilo
dc.contributor.authorPeréz Espíndola, Camilo Esteban
dc.contributor.authorPintor Ahumada, Sebastian David
dc.contributor.orcidMontoya Girlado, Oscar Danilo [0000-0001-6051-4925]
dc.contributor.otherGarcía Barreto, Germán Alberto (Catalogador)
dc.date.accessioned2025-11-19T20:49:56Z
dc.date.available2025-11-19T20:49:56Z
dc.date.created2025-11-06
dc.descriptionLos sistemas de control desempeñan un papel fundamental en la regulación de variables en los sistemas dinámicos, permitiendo así la obtención de la estabilidad de los mismos, acorde a requerimientos particulares de tiempo, seguimiento de referencia y rechazo a las perturbaciones. Esta investigación tuvo como propósito Diseñar un controlador de dos etapas que estabilice el Péndulo de Furuta en la posición vertical superior mediante la teoría de control predictivo basado en el modelo en conjunto con una estrategia de balanceo y levantamiento, empleando funciones de energía que finalmente son implementadas en el sistema físico péndulo invertido de QUANSER. A lo largo de este artículo se detalla la obtención del modelo matemático y su relación con el diseño del controlador predictivo, donde a manera de evaluar su desempeño, se emplea como punto comparativo un control de retroalimentación de estados, implementado en el mismo sistema físico. Ambos controladores son puestos bajo referencias y perturbaciones estandarizadas que permiten identificar los puntos fuertes de cada uno de ellos a través del análisis grafico e índice de desempeño ITAE. Sobresaliendo en criterios como la rapidez, error y seguimientos el control predictivo basado en el modelo pese a limitaciones en la conmutación de controladores con la que no cuenta el control por retroalimentación de estados
dc.description.abstractControl systems are essential for managing variables within dynamic systems, ensuring stability and performance according to criteria such as response time, reference accuracy, and disturbance rejection. This study focuses on designing a two-stage control strategy to stabilize the Furuta Pendulum in its upright position. The proposed approach integrates a Model Predictive Control (MPC) scheme with a swing-up and energy-based lifting technique, and is applied to a physical QUANSER inverted pendulum setup. The paper outlines the mathematical modeling process of the system and details how this model is incorporated into the MPC framework. To assess the effectiveness of the proposed controller, a comparative analysis is conducted against a traditional state feedback controller, both implemented on the same experimental platform. The controllers are tested under standardized reference inputs and disturbance scenarios, with performance evaluated through graphical analysis and the Integral Time Absolute Error (ITAE) index. Results demonstrate that the MPC-based control strategy outperforms the state feedback controller in terms of response speed, tracking precision, and steady-state accuracy. Notably, the MPC exhibits significant advantages during controller switching scenarios, highlighting its robustness and adaptability—features that are not achievable with the conventional state feedback approach.
dc.format.mimetypepdf
dc.identifier.urihttp://hdl.handle.net/11349/99869
dc.language.isospa
dc.publisherUniversidad Distrital Francisco José de Caldas
dc.relation.referencesLuisa F. Escobar-Dávila, Oscar D. Montoya-Giraldo, and Didier Giraldo-Buitrago. Control global del péndulo de furuta empleando redes neuronales artificiales y realimentación de variables de estado. TecnoLógicas, (30):71, June 2013.
dc.relation.referencesSHOZO MORI, HIROYOSHI NISHIHARA, and KATSUHISA FURUTA. Control of unstable mechanical system control of pendulum. International Journal of Control, 23(5):673–692, May 1976.
dc.relation.referencesNicholas J. Jensen and Takayuki Ishizaki. Furuta Pendulum Design Update for Accessible Control Demonstrations. IFAC- PapersOnLine, 56(2):7573–7578, 2023.
dc.relation.referencesM. Yamakita, K. Furuta, K. Konohara, J. Hamada, and H. Kusano. Vss adaptive control based on nonlinear model for titech pendulum. In Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation, pages 1488–1493. IEEE, 2003.
dc.relation.referencesMayra Antonio-Cruz, Victor Manuel Hernandez-Guzman, Carlos Alejandro Merlo-Zapata, and Celso Marquez-Sanchez. Nonlinear control with friction compensation to swing-up a furuta pendulum. ISA Transactions, 139:713–723, August 2023.
dc.relation.referencesFuzzy Swing Up Control and Optimal State Feedback Stabilization for Self-Erecting Inverted Pendulum. IEEE Access, 8:6496–6504, 2020.
dc.relation.referencesXinrong Zhang, Jie Ma, Lian Lin, and Lele Wang. Study on swing-up control of rotary inverted pendulum based on energy feedback. In 2018 5th International Conference on Information Science and Control Engineering (ICISCE), pages 994–998. IEEE, July 2018.
dc.relation.referencesEwelina Cholodowicz and Przemyslaw Orlowski. Optimization of a fractional order controller for the furuta pendulum with an output disturbance using a genetic algorithm. In 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), pages 373–379. IEEE, December 2022.
dc.relation.referencesNgo Phong Nguyen, Hyondong Oh, Yoonsoo Kim, and Jun Moon. A nonlinear hybrid controller for swinging-up and stabilizing the rotary inverted pendulum. Nonlinear Dynamics, 104(2):1117–1137, 2021.
dc.relation.referencesRicardo Binz and Stanislav Aranovskiy. Iterative learning control strategy for a furuta pendulum system with variable-order linearization. IFAC-PapersOnLine, 54(20):14–19, 2021.
dc.relation.referencesDavid Acosta Villamil, Jovanny Pacheco Bolivar, Jose Noguera Polania, and Marco Sanjuan Mejia. Neural network armax model for a furuta pendulum. Ingeniare. Revista chilena de ingeniería, 29(4):668–682, December 2021.
dc.relation.referencesWilber Acuña-Bravo, Andrés Guillermo Molano-Jiménez, and Enrico Canuto. Embedded model control for underactuated systems: An application to Furuta pendulum. Control Engineering Practice, 113(May):12, 2021.
dc.relation.referencesMate B. Vizi and Gabor Stepan. Stability of the furuta pendulum with delayed digital controller. IFAC-PapersOnLine, 54(18):204– 208, 2021.
dc.relation.referencesG. Rigatos, P. Siano, M. Abbaszadeh, S. Ademi, and A. Melkikh. Nonlinear h-infinity control for underactuated systems: the furuta pendulum example. International Journal of Dynamics and Control, 6(2):835–847, August 2017.
dc.relation.referencesMax Schwenzer, Muzaffer Ay, Thomas Bergs, and Dirk Abel. Review on model predictive control: an engineering perspective. The International Journal of Advanced Manufacturing Technology, 117(5–6):1327–1349, August 2021.
dc.relation.referencesL. R. C. Moura, M. A. F. Montezuma, M. Mendonça, R. H. C. Palácios, C. R. A. Oliveira, A. N. Vargas, M. A. Diop, and R. Breganon. Controlling the furuta pendulum: Proof of concept through virtual prototyping. Journal of Applied Research and Technology, 22(3):327–335, June 2024.
dc.relation.referencesVictor Manuel Hernández-Guzmán and Ramón Silva-Ortigoza. Control of a Furuta Pendulum, pages 869–919. Springer International Publishing, September 2018.
dc.relation.referencesAnjana Govind and S. Selva Kumar. A Comparative Study of Controllers for QUANSER QUBE Servo 2 Rotary Inverted Pendulum System, pages 1401–1414. Springer Singapore, 2020.
dc.relation.referencesAhmad Taher Azar and Quanmin Zhu, editors. Advances and Applications in Sliding Mode Control systems. Springer International Publishing, 2015
dc.relation.referencesMukhtar Fatihu Hamza, Hwa Jen Yap, and Imtiaz Ahmed Choudhury. Cuckoo search algorithm based design of interval type-2 fuzzy pid controller for furuta pendulum system. Engineering Applications of Artificial Intelligence, 62:134–151, June 2017.
dc.relation.referencesAlvaro Prado, Marco Herrera, and Oswaldo Menéndez. Intelligent swing-up and robust stabilization via tube-based nonlinear model predictive control for a rotational inverted-pendulum system. Revista Politécnica, 45(1):49–64, April 2020.
dc.relation.referencesLi Zhang and Roger Dixon. Robust nonminimal state feedback control for a furuta pendulum with parametric modeling errors. IEEE Transactions on Industrial Electronics, 68(8):7341–7349, August 2021.
dc.relation.referencesArturo Cruz Avilés, Martín Ortiz Domínguez, and Yira Muños-Sánchez. Ingeniería de control moderna. Ingenio y Conciencia Boletín Científico de la Escuela Superior Ciudad Sahagún, 5(10), July 2018.
dc.relation.referencesBasil Kouvaritakis and Mark Cannon. Model Predictive Control. Springer International Publishing, 2016.
dc.relation.referencesJuan Libardo Duarte Madrid, E.A. Gonzalez Querubin, and P.A. Ospina-Henao. Predictive control of a furata pendulum. In 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), pages 1–6. IEEE, October 2017.
dc.relation.referencesFreeman. Instructor Workbook, volume 53. 2013.
dc.relation.referencesChangrui Liu, Shengling Shi, and Bart De Schutter. Stability and performance analysis of model predictive control of uncertain linear systems. IEEE, pages 7356–7362, 2024.
dc.relation.referencesMohammed Alhajeri and Masoud Soroush. Tuning guidelines for model-predictive control. Industrial & Engineering Chemistry Research, 59(10):4177–4191, February 2020.
dc.relation.referencesJue He, Yongbo Li, Ziang Wei, and Zixin Huang. Gain-scheduled model predictive control for cart–inverted-pendulum with friction and disturbances. Applied Sciences, 13(24):13080, December 2023.
dc.relation.referencesJesus Dario Mina Antonio, Eduardo Miramón Juarez, Oscar Hernández Martínez, and Miguel Francisco Sabido Borges. Diseño optimizado del conjunto filtro-controlador de un inversor para mejorar su integración a red. Ingeniería Investigación y Tecnología, 23(4):1–13, July 2022
dc.rights.accesoAbierto (Texto Completo)
dc.rights.accessrightsOpenAccess
dc.subjectControl lineal
dc.subjectControl híbrido
dc.subjectBalanceo y estabilización
dc.subjectPéndulo de Furuta
dc.subjectControl predictivo basado en el modelo
dc.subject.keywordLinear control
dc.subject.keywordHybrid control
dc.subject.keywordSwing up and stabilization
dc.subject.keywordFuruta pendulum
dc.subject.keywordModel predictive control
dc.subject.lembIngeniería Eléctrica -- Tesis y disertaciones académicas
dc.subject.lembSistemas de Control
dc.subject.lembSistemas de control adaptables
dc.subject.lembControladores programables
dc.subject.lembModelos matemáticos
dc.titleDiseño de un controlador de dos etapas para el balanceo y la estabilización el péndulo de Furuta
dc.title.titleenglishDesign of a two-stage controller for balancing and stabilizing the Furuta pendulum
dc.typebachelorThesis
dc.type.degreeProducción Académica
dc.type.driverinfo:eu-repo/semantics/bachelorThesis

Archivos

Bloque original

Mostrando 1 - 2 de 2
No hay miniatura disponible
Nombre:
PintorAhumadaSebastianDavid2025.pdf
Tamaño:
1.74 MB
Formato:
Adobe Portable Document Format
Descripción:
Trabajo de Grado
No hay miniatura disponible
Nombre:
Licencia de uso y publicacion.pdf
Tamaño:
274.18 KB
Formato:
Adobe Portable Document Format

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
7 KB
Formato:
Item-specific license agreed upon to submission
Descripción: