Trajectory Tracking Control of a Mobile Robot using Neural Networks

Darwin Trujillo, Luis A. Morales, Danilo Chávez, David F. Pozo


This paper presents a novel soft computing-based machine learning technique designed to enhance the trajectory tracking capabilities of mobile robots through the application of neural networks. The goal of this approach is to enhance the accuracy and overall performance of trajectory tracking without the need for manual gain recalibration, which is a tedious and time-consuming task for the designer when setting up the robot. This improvement is achieved by creating a kinematic controller based on neural networks, which are constructed using the kinematic model of the robot. In the initial phase, the controller requires gains defined by the designer. Subsequently, during the application phase, the backpropagation algorithm is used to dynamically adjust the gains of the neural network, aiming to minimize the closed-loop error. One of the key innovations introduced by this controller is the potential for automatic online gain tuning, thereby eliminating the need for a pre-learning phase, typically required by traditional neural controllers. To validate the effectiveness of this approach, the results are systematically analyzed and compared against those obtained using a conventional kinematic controller. Performance metrics reveal the improved precision in trajectory tracking achieved by the controller, with reduced effort, highlighting the performance enhancements in different trajectories.


Doi: 10.28991/ESJ-2023-07-06-01

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Backpropagation; Kinematics; Robot; Neural Network; Trajectory Tracking.


Isasi Vinuela, P., & Galván León, I. M. (2004). Artificial neural networks: A practical approach. Pearson Educación, Madreid, Spain. (In Spanish).

Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264–75278. doi:10.1109/ACCESS.2020.2988510.

Sarker, S., Jamal, L., Ahmed, S. F., & Irtisam, N. (2021). Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Robotics and Autonomous Systems, 146. doi:10.1016/j.robot.2021.103902.

Calderón Velasco, R. (2023). Inteligencia artificial en medicina. Diagnóstico, 62(1), e431. doi:10.33734/diagnostico.v62i1.431.

Matas, C. R. (2018). Artificial intelligence, robotics and public administration models. Revista del CLAD Reforma y Democracia, 72, 5-42. (In Spanish).

Tzafestas, S. G. (2013). Introduction to Mobile Robot Control. Elsevier, Amsterdam, Netherlands. doi:10.1016/C2013-0-01365-5.

Byambasuren, B. E., Kim, D., Oyun-Erdene, M., Bold, C., & Yura, J. (2016). Inspection robot based mobile sensing and power line tracking for smart grid. Sensors (Switzerland), 16(2), 250. doi:10.3390/s16020250.

Bengochea-Guevara, J. M., Conesa-Muñoz, J., Andújar, D., & Ribeiro, A. (2016). Merge fuzzy visual servoing and GPS-based planning to obtain a proper navigation behavior for a small crop-inspection robot. Sensors (Switzerland), 16(3), 276. doi:10.3390/s16030276.

Klamt, T., Kamedula, M., Karaoguz, H., Kashiri, N., Laurenzi, A., Lenz, C., Leonardis, D., Mingo Hoffman, E., Muratore, L., Pavlichenko, D., Porcini, F., Rodriguez, D., Ren, Z., Schilling, F., Schwarz, M., Solazzi, M., Felsberg, M., Frisoli, A., Gustmann, M., … Holmquist, K. (2019). Flexible Disaster Response of Tomorrow: Final Presentation and Evaluation of the Centauro System. IEEE Robotics & Automation Magazine, 26(4), 59–72. doi:10.1109/MRA.2019.2941248.

Hassan, N., & Saleem, A. (2022). Neural Network-Based Adaptive Controller for Trajectory Tracking of Wheeled Mobile Robots. IEEE Access, 10, 13582–13597. doi:10.1109/ACCESS.2022.3146970.

Parsianmehr, S., Moosavian, S. A. A., & Fakharian, A. (2016). An experimental system identification modeling and robust control for NAO humanoid robot. 2016 4th International Conference on Robotics and Mechatronics (ICROM), Tehran, Iran. doi:10.1109/icrom.2016.7886793.

Jang, J. S. R., Sun, C. T., & Mizutani, E. (2005). Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence. IEEE Transactions on Automatic Control, 42(10), 1482–1484. doi:10.1109/tac.1997.633847.

Zalama, E., Paul, M., & Perán, J. R. (1998). Neural Network for the Behavioral Navigation of a Mobile Robot. IFAC Proceedings Volumes, 31(3), 93–98. doi:10.1016/s1474-6670(17)44067-5.

Marichal, G. N., Toledo, J., Acosta, L., González, E. J., & Coll, G. (2007). A neuro-fuzzy method applied to the motors of a stereovision system. Engineering Applications of Artificial Intelligence, 20(7), 951–958. doi:10.1016/j.engappai.2006.12.010.

Liu, Q., & Cong, Q. (2022). Kinematic and dynamic control model of wheeled mobile robot under internet of things and neural network. Journal of Supercomputing, 78(6), 8678–8707. doi:10.1007/s11227-021-04160-1.

Asai, M., Chen, G., & Takami, I. (2019). Neural network trajectory tracking of tracked mobile robot. 16th International Multi-Conference on Systems, Signals and Devices, SSD 2019, 225–230. doi:10.1109/SSD.2019.8893152.

Chen, Z., Liu, Y., He, W., Qiao, H., & Ji, H. (2021). Adaptive-Neural-Network-Based Trajectory Tracking Control for a Nonholonomic Wheeled Mobile Robot with Velocity Constraints. IEEE Transactions on Industrial Electronics, 68(6), 5057–5067. doi:10.1109/TIE.2020.2989711.

Mohareri, O., Dhaouadi, R., & Rad, A. B. (2012). Indirect adaptive tracking control of a nonholonomic mobile robot via neural networks. Neurocomputing, 88, 54–66. doi:10.1016/j.neucom.2011.06.035.

Yildirim, S., Savas, S., & Andruskiene, J. (2021). Controller Gain Tuning of a Nonholonomic Mobile Robot via Neural Network Predictor. 2021 25th International Conference Electronics. doi:10.1109/ieeeconf52705.2021.9467455.

Mohamed, M., & Hamza, M. (2019). Design PID Neural Network Controller for Trajectory Tracking of Differential Drive Mobile Robot Based on PSO. Engineering and Technology Journal, 37(12A), 574–583. doi:10.30684/etj.37.12a.12.

Gou, W., & Liu, Y. (2022). Trajectory tracking control of wheeled mobile robot based on improved LSTM-DDPG algorithm. Journal of Physics: Conference Series, 2303(1). doi:10.1088/1742-6596/2303/1/012069.

Rossomando, F. G., Soria, C., & Carelli, R. (2011). Autonomous mobile robots navigation using RBF neural compensator. Control Engineering Practice, 19(3), 215–222. doi:10.1016/j.conengprac.2010.11.011.

Nath, K., Bera, M. K., & Jagannathan, S. (2022). Concurrent Learning-Based Neuro-Adaptive Robust Tracking Control of Wheeled Mobile Robot: An Event-Triggered Design. IEEE Transactions on Artificial Intelligence, 4(6), 1514–1525. doi:10.1109/TAI.2022.3207133.

Morales, L., Aguilar, J., Rosales, A., Chávez, D., & Leica, P. (2020). Modeling and control of nonlinear systems using an Adaptive LAMDA approach. Applied Soft Computing, 95, 106571. doi:10.1016/j.asoc.2020.106571.

Botía Valderrama, J. F., & Botía Valderrama, D. J. L. (2018). On LAMDA clustering method based on typicality degree and intuitionistic fuzzy sets. Expert Systems with Applications, 107, 196–221. doi:10.1016/j.eswa.2018.04.022.

Morales, L., Lozada, H., Aguilar, J., & Camargo, E. (2019). Applicability of LAMDA as classification model in the oil production. Artificial Intelligence Review, 53(3), 2207–2236. doi:10.1007/s10462-019-09731-6.

Polit, M. (2006). An optimization method for the data space partition obtained by classification techniques for the monitoring of dynamic processes. Artificial Intelligence Research and Development, IOS Press, Amsterdam, Netherlands.

Naveed, K., Khan, Z. H., & Hussain, A. (2014). Adaptive trajectory tracking of wheeled mobile robot with uncertain parameters. Studies in Computational Intelligence, 540, 237–262. doi:10.1007/978-981-4585-36-1_8.

Benbouabdallah, K., & Qi-dan, Z. (2013). Genetic Fuzzy Logic Control Technique for a Mobile Robot Tracking a Moving Target. International Journal of Computer Science Issues, 10(1), 607–613.

Mendez, E., Baltazar-Reyes, G., MacIas, I., Vargas-Martinez, A., De Jesus Lozoya-Santos, J., Ramirez-Mendoza, R., Morales-Menendez, R., & Molina, A. (2020). ANN based MRAC-PID controller implementation for a furuta pendulum system stabilization. Advances in Science, Technology and Engineering Systems, 5(3), 324–333. doi:10.25046/aj050342.

Duong, H. Q., Nguyen, Q. H., Nguyen, D. T., & Van Nguyen, L. (2022). PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor. Emerging Science Journal, 6(2), 201-216. doi:10.28991/ESJ-2022-06-02-01.

Siregar, S. P., & Wanto, A. (2017). Analysis of artificial neural network accuracy using backpropagation algorithm in predicting process (Forecasting). International Journal of Information System & Technology, 1(1), 34. doi:10.30645/ijistech.v1i1.4.

Jepkoech, J., Mugo, D. M., Kenduiywo, B. K., & Too, E. C. (2021). The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks. International Journal of Advanced Computer Science and Applications, 12(8), 736–751. doi:10.14569/IJACSA.2021.0120885.

Shirong Liu, Huidi Zhang, Yang, S. X., & Jinshou Yu. (2004). Dynamic control of a mobile robot using an adaptive neurodynamics and sliding mode strategy. Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788). doi:10.1109/wcica.2004.1343669.

Gracia, L., & Tornero, J. (2008). Kinematic control of wheeled mobile robots. Latin American Applied Research, 38(1), 7–16.

Rossomando, F. G., Soria, C., & Carelli, R. (2010). Control of Mobile Robots with Dynamic Uncertainties using Radial Base Networks. Revista Iberoamericana de Automática e Informática Industrial RIAI, 7(4), 28–35. doi:10.1016/s1697-7912(10)70057-1.

Morales, L., Herrera, M., Camacho, O., Leica, P., & Aguilar, J. (2021). LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots. IEEE Access, 9, 37179–37195. doi:10.1109/access.2021.3062202.

Eusebio, B.-C., & Ana Yaveni, A.-B. (2014). Visual control for the formation of unicycle-type mobile robots under the leader-follower scheme. Ingeniería, Investigación y Tecnología, 15(4), 593–602. doi:10.1016/s1405-7743(14)70657-2.

Valencia, J. A., Montoya, A., & Rios, L. H. (2009). Kinematic model of a differential type mobile robot and navigation from odometric estimation. Scientia et Technica, 1(41). (In Spanish).

Rohmer, E., Singh, S. P. N., & Freese, M. (2013). V-REP: A versatile and scalable robot simulation framework. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan. doi:10.1109/iros.2013.6696520.

Rodríguez-Mariano, A., Reynoso-Meza, G., Páramo-Calderón, D. E., Chávez-Conde, E., García-Alvarado, M. A., & Carrillo-Ahumada, J. (2015). Analysis of the Performance of Tuned Linear Controllers in Different Steady States of the Cholette Bioreactor using Multi-Criteria Decision Techniques. Revista mexicana de Ingeniería Química, 14(1), 167-204. (In Spanish).

Proudfoot, C. G. (1987). Principles and practice of automatic process control. Carlos A. Smith and Armando B. Corripio (Book Review). Automatica, 23(3), 414. doi:10.1016/0005-1098(87)90018-5.

Duarte-Mermoud, M. A., & Prieto, R. A. (2004). Performance index for quality response of dynamical systems. ISA Transactions, 43(1), 133–151. doi:10.1016/s0019-0578(07)60026-3.

Salgado, M. E., Oyarzún, D. A., & Silva, E. I. (2007). H2 optimal ripple-free deadbeat controller design. Automatica, 43(11), 1961–1967. doi:10.1016/j.automatica.2007.03.014.

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DOI: 10.28991/ESJ-2023-07-06-01


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