Diseño de una estrategia de control para robots móviles utilizando técnicas de álgebra lineal (LABC) y estimación neuronal en el seguimiento de trayectorias

Carlos A. Vacca

https://orcid.org/0000-0002-3776-4085

Argentina

National University of San Juan image/svg+xml

Facultad Regional La Rioja

Eduardo G. Scaglia

Argentina

National University of San Juan image/svg+xml

Instituto de Investigaciones Químicas. Facultad de Ingeníera (UNSJ)

Fernando C. Ulloa-Vasquez

https://orcid.org/0000-0003-2897-2867

Chile

Universidad Tecnológica Metropolitana image/svg+xml

Programa Investigación Radio Digital (PIRD). Facultad de Ingeniería

Francisco G. Rossomando

https://orcid.org/0000-0002-7792-8101

Argentina

National University of San Juan image/svg+xml

Instituto de Automática. Facultad de Ingeníera (UNSJ-CONICET)

|

Aceptado: 13-11-2024

|

Publicado: 04-12-2024

DOI: https://doi.org/10.4995/riai.2024.21484
Datos de financiación

Descargas

Palabras clave:

Álgebra Lineal, Control Neuronal, Control No Lineal, Identificación,, Robots Móviles

Agencias de apoyo:

Esta investigación no contó con financiación

Resumen:

Los incovenientes planteados por el seguimiento de trayectorias usando robots móviles es un tema vigente en la teoría de control, en esta propuesta se presenta el diseño de un controlador de álgebra lineal en combinación con un estimador neuronal. Donde además el robot móvil cuenta con incertidumbres aditívas. Los valores de incertidumbre en cada momento de muestreo se obtienen mediante estimación basada en Redes Neuronales, donde se incluye el diseño de un estimador neuronal del error de modelado junto con la demostración de la convergencia a cero del error de seguimiento. La técnica de control propuesta se valida mediante simulación y resultados experimentales. El controlador de Ágebra Lineal y el estimador neuronal demuestran que se puede utilizar para reducir el efecto de las incertidumbres aditivas en el error de control de seguimiento.

Ver más Ver menos

Citas:

Acosta, F. U. V.-S., 2004. Diseño de un sistema telemando y telemétrico experimental para una aerostación de baja altura. Researchgate.

Andaluz Ortiz, G. M., 2011. Modelación, identificación y control de robots móviles. B.S. thesis, QUITO/EPN/2011.

Blazic, S., 2011. A novel trajectory tracking control law for wheeled mobile robots. Robotics and Autonomous Systems 59 (11), 1001-1007. https://doi.org/10.1016/j.robot.2011.06.005

Chwa, D., 2004. Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates. IEEE Transactions on Control Systems Technology 12 (4), 637-644. https://doi.org/10.1109/TCST.2004.824953

Damodaran, S., Kumar, T. S., Sudheer, A. P., 2017. Design and implementation of ga tuned pid controller for desired interaction and trajectory tracking of wheeled mobile robot. En: Proceedings of the Advances in Robotics. pp.1-6. https://doi.org/10.1145/3132446.3134898

Das, T., Kar, I. N., 2006. Design and implementation of an adaptive fuzzy logic based controller for wheeled mobile robots. IEEE Transactions on Control systems technology 14 (3), 501-510. https://doi.org/10.1109/TCST.2006.872536

De La Cruz, C., Bastos, T. F., Carelli, R., 2011. Adaptive motion control law of a robotic wheelchair. Control Engineering Practice 19 (2), 113-125. https://doi.org/10.1016/j.conengprac.2010.10.004

Deng, R., Zhang, Q., Gao, R., Li, M., Liang, P., Gao, X., 2021. A trajectory tracking control algorithm of nonholonomic wheeled mobile robot. En: 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics(ICARM). pp. 823-828. https://doi.org/10.1109/ICARM52023.2021.9536154

Fareh, R., Khadraoui, S., Abdallah, M. Y., Baziyad, M., Bettayeb, M., 2021. Active disturbance rejection control for robotic systems: A review. Mechatronics 80, 102671. https://doi.org/10.1016/j.mechatronics.2021.102671

Farooq, U., Hasan, K., Hanif, A., Amar, M., Asad, M. U., 2014. Fuzzy logic based path tracking controller for wheeled mobile robots. International Journal of Computer and Electrical Engineering 6 (2), 77. https://doi.org/10.7763/IJCEE.2014.V6.811

Gandolfo, D., Rosales, C., Patiño, D., Scaglia, G., Jordan, M., 2014. Trajectory tracking control of a pvtol aircraft based on linear algebra theory. Asian Journal of Control 16 (6), 1849-1858. https://doi.org/10.1002/asjc.819

Gomez, J., Rossomando, F., Capraro, F., Soria, C., 2023. Real-time neuroadaptive pi control of soil moisture by using a hybrid model. Revista Iberoamericana de Automática e Informática industrial 20 (1), 93-103. https://doi.org/10.4995/riai.2022.17106

Gu, D., Hu, H., 2002. Neural predictive control for a car-like mobile robot. Robotics and Autonomous Systems 39 (2), 73-86. https://doi.org/10.1016/S0921-8890(02)00172-0

Hassan, N., Saleem, A., 2021. Analysis of trajectory tracking control algorithms for wheeled mobile robots. En: 2021 IEEE Industrial Electronics and Applications Conference (IEACon). pp.236-241. https://doi.org/10.1109/IEACon51066.2021.9654675

Jung, S., 2012. Experiences in developing an experimental robotics course program for undergraduate education. IEEE Transactions on Education 56 (1), 129-136. https://doi.org/10.1109/TE.2012.2213601

Lee, J. H., Lin, C., Lim, H., Lee, J. M., 2009. Sliding mode control for trajectory tracking of mobile robot in the rfid sensor space. International Journal of Control, Automation and Systems 7, 429-435. https://doi.org/10.1007/s12555-009-0312-7

López-Cortés, L. F., Lozano-Hernandez, Y., Torres, L., Guerrero-Castellanos, J. F., Aguirre-Anaya, J. A., 2021. Comparison of dynamic model-based control algorithms for trajectory tracking in an omnidirectional robot. En: 2021 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE). pp. 60-65. https://doi.org/10.1109/ICMEAE55138.2021.00017

Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., Bastos-Filho, T. F., 2008. An adaptive dynamic controller for autonomous mobile robot trajectory tracking. Control Engineering Practice 16 (11), 1354-1363. https://doi.org/10.1016/j.conengprac.2008.03.004

Normey-Rico, J. E., Alcalá, I., Gómez-Ortega, J., Camacho, E. F., 2001.Mobile robot path tracking using a robust pid controller. Control Engineering Practice 9 (11), 1209-1214. https://doi.org/10.1016/S0967-0661(01)00066-1

Pantano, M. N., Serrano, M. E., Fernández, M. C., Rossomando, F. G., Ortiz, O. A., Scaglia, G. J., 2017. Multivariable control for tracking optimal profiles in a nonlinear fed-batch bioprocess integrated with state estimation. Industrial and Engineering Chemistry Research 56 (20), 6043-6056. https://doi.org/10.1021/acs.iecr.7b00831

Park, J.-W., Kwak, H.-J., Kang, Y.-C., Kim, D.W., et al., 2016. Advanced fuzzy potential field method for mobile robot obstacle avoidance. Computational intelligence and neuroscience 2016. https://doi.org/10.1155/2016/6047906

Poggio, T., Girosi, F., 1990. Networks for approximation and learning. Proceedings of the IEEE 78 (9), 1481-1497. https://doi.org/10.1109/5.58326

Rosales, A., Scaglia, G., Mut, V., di Sciascio, F., 2011. Formation control and trajectory tracking of mobile robotic systems-a linear algebra approach. Robotica 29 (3), 335-349. https://doi.org/10.1017/S0263574710000068

Rosales, C., Soria, C. M., Rossomando, F. G., 2019. Identification and adaptive pid control of a hexacopter uav based on neural networks. International journal of Adaptive control and signal processing 33 (1), 74-91. https://doi.org/10.1002/acs.2955

Rossomando, F. G., Soria, C., Carelli, R., 2012. Neural network-based compensation control of mobile robots with partially known structure. IET Control Theory and Applications 6 (12), 1851-1860. https://doi.org/10.1049/iet-cta.2011.0581

Rossomando, F. G., Soria, C., Carelli, R., 2014. Sliding mode neuro adaptive control in trajectory tracking for mobile robots. Journal of Intelligent and Robotic Systems 74, 931-944. https://doi.org/10.1007/s10846-013-9843-5

Scaglia, G., Aballay, P. M., Serrano, M. E., Ortiz, O. A., Jordan, M., Vallejo, M. D., 2014. Linear algebra based controller design applied to a bench-scale oenological alcoholic fermentation. Control Engineering Practice 25, 66-74. https://doi.org/10.1016/j.conengprac.2014.01.002

Scaglia, G., Montoya, L. Q., Mut, V., di Sciascio, F., 2009. Numerical methods based controller design for mobile robots. Robotica 27 (2), 269-279. https://doi.org/10.1017/S0263574708004669

Scaglia, G. J. E., Serrano, M. E., Godoy, S. A., Rossomando, F., 2020. Linear algebra-based controller for trajectory tracking in mobile robots with additive uncertainties estimation. IMA Journal of Mathematical Control and Information 37 (2), 607-624. https://doi.org/10.1093/imamci/dnz016

Serrano, M. E., Godoy, S. A., Mut, V. A., Ortiz, O. A., Scaglia, G. J., 2016. A nonlinear trajectory tracking controller for mobile robots with velocity limitation via parameters regulation. Robotica 34 (11), 2546-2565. https://doi.org/10.1017/S026357471500020X

Serrano M. E., Godoy, S. A., Romoli, S., Scaglia, G. J., 2017. A numerical approximation-based controller for mobile robots with velocity limitation Asian Journal of Control 19 (6), 2165-2177. https://doi.org/10.1002/asjc.1522

Serrano, M. E., Scaglia, G. J., Godoy, S. A., Mut, V., Ortiz, O. A., 2013. Trajectorytracking of underactuated surface vessels: A linear algebra approach. IEEE Transactions on Control Systems Technology 22 (3), 1103-1111. https://doi.org/10.1109/TCST.2013.2271505

Sira-Ramírez, H., López-Uribe, C., Velasco-Villa, M., 2013. Linear observerbased active disturbance rejection control of the omnidirectional mobile robot. Asian Journal of Control 15 (1), 51-63. https://doi.org/10.1002/asjc.523

Sun, Z., Xia, Y., Dai, L., Liu, K., Ma, D., 2017. Disturbance rejection mpc for tracking of wheeled mobile robot. IEEE/ASME Transactions On Mechatronics 22 (6), 2576-2587. https://doi.org/10.1109/TMECH.2017.2758603

Tian, Y., Sarkar, N., 2014. Control of a mobile robot subject to wheel slip. Journal of Intelligent and Robotic Systems 74, 915-929. https://doi.org/10.1007/s10846-013-9871-1

Tzafestas, S. G., 2013. Introduction to mobile robot control. Elsevier. Wang, K., 2012. Near-optimal tracking control of a nonholonomic mobile robot with uncertainties. International Journal of Advanced Robotic Systems 9 (3), 66. https://doi.org/10.5772/51189

Wit, C. C. d., Khennouf, H., Samson, C., Sordalen, O. J., 1993. Nonlinear control design for mobile robots. En: Recent trends in mobile robots. World Scientific, pp. 121-156. https://doi.org/10.1142/9789814354301_0005

Wu, Q., Qi, J., Wu, C., Wang, M., 2020. Design of ugv trajectory tracking controller in ugv-uav cooperation. En: 2020 39th Chinese Control Conference (CCC). pp. 3689-3694. https://doi.org/10.23919/CCC50068.2020.9188688

Yang, C., Ma, H., Fu, M., Yang, C., Ma, H., Fu, M., 2016. Robot kinematics and dynamics modeling. Advanced technologies in modern robotic applications, 27-48. https://doi.org/10.1007/978-981-10-0830-6_2

Yang, Y., Yan, X., Sirlantzis, K., Howells, G., 2019. Application of sliding mode trajectory tracking control design for two-wheeled mobile robots. En: 2019 NASA/ESA Conference on Adaptive Hardware and Systems (AHS).pp. 109-114. https://doi.org/10.1109/AHS.2019.00012

Ye, J., 2008a. Adaptive control of nonlinear pid-based analog neural networks for a nonholonomic mobile robot. Neurocomputing 71 (7-9), 1561-1565. https://doi.org/10.1016/j.neucom.2007.04.014

Ye, J., 2008b. Tracking control for nonholonomic mobile robots: Integrating the analog neural network into the backstepping technique. Neurocomputing 71 (16-18), 3373-3378. https://doi.org/10.1016/j.neucom.2007.11.005

Yu, H., Xie, T., Paszczynski, S., Wilamowski, B. M., 2011. Advantages of radial basis function networks for dynamic system design. IEEE Transactions on Industrial Electronics 58 (12), 5438-5450. https://doi.org/10.1109/TIE.2011.2164773

Yue, M., Tang, F., Liu, B., Yao, B., 2012. Trajectory-tracking control of a nonholonomic mobile robot: Backstepping kinematics into dynamics with uncertain disturbances. Applied Artificial Intelligence 26 (10), 952-966. https://doi.org/10.1080/08839514.2012.731347

Zhang, L., Wang, B., Guan, E., Liu, X., Saqib, M., Zhao, Y., 2024. Adaptive skid-steering control approach for robots on uncertain inclined planes with redundant load-bearing mobility. Biomimetics 9 (2), 64. https://doi.org/10.3390/biomimetics9020064

Zhang, M., Miao, Z., Li, N., 2022. Research on trajectory tracking control of wheeled mobile robot by ilc. En: 2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE).pp. 33-36. https://doi.org/10.1109/ICBAIE56435.2022.9985861

Ver más Ver menos