Enhancing Trajectory Tracking in Humanoid Robots Using Neural Network-Based Dynamic Gain Control

Darwin Trujillo; Luis A. Morales; Danilo Chávez; Marí­a Trujillo; David F. Pozo-Espí­n

doi:10.28991/ESJ-2025-09-02-02

Authors

Darwin Trujillo Departamento de Automatización Y Control Industrial, Escuela Politécnica Nacional, Quito,, Ecuador
Luis A. Morales Departamento de Automatización Y Control Industrial, Escuela Politécnica Nacional, Quito,, Ecuador
Danilo Chávez Departamento de Automatización Y Control Industrial, Escuela Politécnica Nacional, Quito,, Ecuador
María Trujillo Departamento de Automatización Y Control Industrial, Escuela Politécnica Nacional, Quito,, Ecuador
David F. Pozo-Espín
david.pozo@udla.edu.ec
Facultad de Ingeniería y Ciencias Aplicadas, Ingeniería en Electrónica y Automatización, Universidad de Las Américas, Quito,, Ecuador https://orcid.org/0000-0002-7436-3838

Vol. 9 No. 2 (2025): April

Research Articles

Downloads

PDF

Abstract
How to Cite
Metrics
References
License

This paper presents the development and evaluation of a dynamic gain controller utilizing neural networks to enhance trajectory tracking performance in the NAO humanoid robot. The proposed controller employs a differential kinematic model and dynamically adjusts its gains using a backpropagation algorithm, eliminating the need for manual gain tuning and simplifying the robot's setup process. Experimental validation was conducted in a simulated environment using CoppeliaSim, with the NAOqi library facilitating integration. The analysis results demonstrate that the dynamic controller using a neural network provides better trajectory tracking accuracy than the traditional kinematic controller. Adaptability of the dynamic controller, which adjusts gain parameters in real-time, contributes to improved robustness and precision across various trajectory types. These findings demonstrate the potential of dynamic, self-tuning controllers in enhancing the performance, efficiency, and versatility of humanoid robots in complex navigation tasks.

Doi: 10.28991/ESJ-2025-09-02-02

Full Text: PDF

Trujillo, D., Morales, L. A., Chávez, D., Trujillo, M., & Pozo-Espín, D. F. (2025). Enhancing Trajectory Tracking in Humanoid Robots Using Neural Network-Based Dynamic Gain Control. Emerging Science Journal, 9(2), 539–556. https://doi.org/10.28991/ESJ-2025-09-02-02

Download Citation

Kahraman, C., Deveci, M., Boltürk, E., & Türk, S. (2020). Fuzzy controlled humanoid robots: A literature review. Robotics and Autonomous Systems, 134. doi:10.1016/j.robot.2020.103643.

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

Parsianmehr, S., Moosavian, S. A. A., & Fakharian, A. (2017). An experimental system identification modeling and robust control for NAO humanoid robot. 4th RSI International Conference on Robotics and Mechatronics, ICRoM 2016, 506–511. doi:10.1109/ICRoM.2016.7886793.

Almeida, L., Santos, V., & Ferreira, J. (2024). Enhancement of humanoid robot locomotion on slippery floors using an adaptive controller. Robotica, 42(4), 1055–1073. doi:10.1017/S0263574724000080.

Iffath Unnisa Begum. (2024). Role of Artificial Intelligence in Higher Education- An Empirical Investigation. International Research Journal on Advanced Engineering and Management, 2(03), 49–53. doi:10.47392/irjaem.2024.0009.

Obrenovic, B., Gu, X., Wang, G., Godinic, D., & Jakhongirov, I. (2024). Generative AI and human–robot interaction: implications and future agenda for business, society and ethics. AI & Society, 40(2), 677–690. doi:10.1007/s00146-024-01889-0.

Mahamood, S. F., Fikry, A., Hamzah, M. I., Khalid, M. M., & Bhari, A. (2023). Fiqh Robotic For Artificial Intelligent In Humanoids Used For Therapy, Services and Other Social Activities: An Integration of Artificial Intelligence (AI) and Maqasid Shariah. Journal of Fatwa Management and Research, 28(2), 1–13. doi:10.33102/jfatwa.vol28no2.527.

Podpečan, V. (2023). Can You Dance? A Study of Child–Robot Interaction and Emotional Response Using the NAO Robot. Multimodal Technologies and Interaction, 7(9), 85. doi:10.3390/mti7090085.

Venkataswamy, R., Janamala, V., & Cherukuri, R. C. (2023). Realization of Humanoid Doctor and Real-Time Diagnostics of Disease Using Internet of Things, Edge Impulse Platform, and ChatGPT. Annals of Biomedical Engineering, 52(4), 738–740. doi:10.1007/s10439-023-03316-9.

Pot, E., Monceaux, J., Gelin, R., & Maisonnier, B. (2009). Choregraphe: a graphical tool for humanoid robot programming. RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication, 46-51. doi:10.1109/roman.2009.5326209.

Ramkumar, A., Akhil Krishna, U., Madhan, M. S., & Prajit, K. K. (2019). Control of Nao robot arm using Myo Armband. International Journal of Innovative Technology and Exploring Engineering, 8(9S2), 409–413. doi:10.35940/ijitee.I1087.0789S219.

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.

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.

Asai, M., Chen, G., & Takami, I. (2019). Neural network trajectory tracking of tracked mobile robot. 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), 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, 1–6. 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.

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.

Farhat, M., Kali, Y., Saad, M., Rahman, M. H., & Lopez-Herrejon, R. E. (2024). Walking position commanded NAO robot using nonlinear disturbance observer-based fixed-time terminal sliding mode. ISA Transactions, 146, 592–602. doi:10.1016/j.isatra.2023.12.026.

Bai, K., Jiang, G., Jiang, G., & Liu, Z. (2019). Based on fuzzy-approximation adaptive backstepping control method for dual-arm of humanoid robot with trajectory tracking. International Journal of Advanced Robotic Systems, 16(3), 1-14. doi:10.1177/1729881419831904.

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.

Alcaraz-Jiménez, J. J., Herrero-Pérez, D., & Martínez-Barberá, H. (2013). Robust feedback control of ZMP-based gait for the humanoid robot Nao. International Journal of Robotics Research, 32(9–10), 1074–1088. doi:10.1177/0278364913487566.

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.

Eusebio, B.-C., & Ana Yaveni, A.-B. (2014). Visual control for training unicycle-type mobile robots under the leader-follower scheme. Engineering, Research, and Technology, 15(4), 593–602. doi:10.1016/s1405-7743(14)70657-2.

Kofinas, N., Orfanoudakis, E., & Lagoudakis, M. G. (2015). Complete Analytical Forward and Inverse Kinematics for the NAO Humanoid Robot. Journal of Intelligent and Robotic Systems: Theory and Applications, 77(2), 251–264. doi:10.1007/s10846-013-0015-4.

Li, S., & Shen, L. (2024). Seismic Optimization Design and Application of Civil Engineering Structures Integrated with Building Robot System Technology. HighTech and Innovation Journal, 5(4), 1118-1134. doi:10.28991/HIJ-2024-05-04-017.

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.

SoftBank Robotics. (2025). NAO6 the versatile humanoid robot. Available online: https://developer.softbankrobotics.com/nao6 (accessed March 2025).

Shamsuddin, S., Ismail, L. I., Yussof, H., Ismarrubie Zahari, N., Bahari, S., Hashim, H., & Jaffar, A. (2011). Humanoid robot NAO: Review of control and motion exploration. IEEE International Conference on Control System, Computing and Engineering, 511-516. doi:10.1109/iccsce.2011.6190579.

Hwang, J. H., Tsay, S. Y., & Hwang, C. (2000). Tuning PID Controllers for Minimizing ISE and Satisfying Specified Gain and Phase Margins. IFAC Proceedings Volumes, 33(4), 601-606. doi:10.1016/S1474-6670(17)38309-X.

Acceptance Rate:	21%
Review Speed:	74 days
Issue Per Year:	6
Number of Volumes:	7
Number of Issues:	44
Number of Articles:	493
Number of Reviewers:	1187
Number of Contributors:	1394
Contributing Countries:	83
No. of WoS Citations:	2609
No. of Scopus Citations:	2936
No. of Google Citations:	4161
Google h-index:	29
Google i10-index:	126
Abstract Views:	681,807
PDF Download:	492,524

Enhancing Trajectory Tracking in Humanoid Robots Using Neural Network-Based Dynamic Gain Control

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Most Cited Articles

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Impediments of Green Finance Adoption System: Linking Economy and Environment

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic

Address

Contact Info:

Enhancing Trajectory Tracking in Humanoid Robots Using Neural Network-Based Dynamic Gain Control

Authors

Downloads

Downloads

Login

submission

Publisher & Affiliated Societies

Indexing & Abstracting

SidebarMenu

social

Journal Imprint

Journal Metrics

IndexedBy

Indexing and Abstracting

twitter

Social Media

Analytics

Analytics

Information

Most Cited Articles

Thermal Regeneration and Reuse of Carbon and Glass Fibers from Waste Composites

Impediments of Green Finance Adoption System: Linking Economy and Environment

Optical and Structural Characterization of Bi2FexNbO7 Nanoparticles for Environmental Applications

Digital Transformation: Opportunities and Challenges for Leaders in the Emerging Countries in Response to Covid-19 Pandemic