Robustness of neuro-PD hybrid control under uncertainties and disturbances
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13645Keywords:
Robustness, Robotics, Control, Hybrid neuro-PD control, Neural networksAbstract
This work presents the development and validation of a hybrid control system for a two-degree-of-freedom (2-DOF) robotic manipulator. The proposed scheme integrates a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), designed to approximate the inverse dynamics for system linearization, with a Proportional-Derivative (PD) regulator to close the feedback loop. The robustness of the architecture is evaluated through stress tests involving parametric uncertainty in the distal link mass, Gaussian sensor noise, and external torque disturbances. Experimental simulation results demonstrate that the hybrid controller effectively mitigates the drift characteristic of purely machine learning-based models, achieving precise trajectory tracking even under 900% variations in the robot’s load.
References
Craig, J. J., 2009. Introduction to robotics: mechanics and control, 3/E. Pearson Education India.
H., J. A. M., de Lope, J., Santos, M., 2009. A method to learn the inverse kinematics of multi-link robots by evolving neuro-controllers. Neurocomputing 72 (13), 2806–2814, hybrid Learning Machines (HAIS 2007) / Recent Developments in Natural Computation (ICNC 2007). URL: https://www.sciencedirect.com/science/article/ii/S0925231209001209 DOI: https://doi.org/10.1016/j.neucom.2008.08.021
Haykin, S., 1994. Neural networks: a comprehensive foundation. Prentice hall PTR. DOI: 10.1017/S0269888998214044
Kingma, D. P., Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. DOI: 10.48550/arXiv.1412.6980
Lewis, F., Jagannathan, S., Yesildirak, A., 2020. Neural network control of robot manipulators and non-linear systems. CRC press. DOI: 10.1201/9781003062714
Lewis, F. L., Dawson, D. M., Abdallah, C. T., 2003. Robot manipulator control: theory and practice. CRC Press. DOI: 10.1201/9780203026953
Lussón-Cervantes, A., Beltrán-Aguedo, R., Bernardez-Rubio, N., 2025. Control de un robot articulado de 2 gdl. Revista UIS Ingenierı́as 24 (3), 1–14. DOI: 10.18273/revuin.v24n3-2025001
Machado, J. T., de Carvalho, J. M., Galhano, A. M., 1993. Analysis of robot dynamics and compensation using classical and computed torque techniques. IEEE Transactions on Education 36 (4), 372–379. DOI: 10.1109/13.241614
Markiewicz, B., 1973. Analysis of the computed torque drive method and comparison with conventional position servo for a computer-controlled manipulator. Tech. rep.
Nair, V., Hinton, G. E., 2010. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10). pp. 807–814.
Nguyen-Tuong, D., Seeger, M., Peters, J., 2008. Computed torque control with nonparametric regression models. In: 2008 American Control Conference. IEEE, pp. 212–217. DOI: 10.1109/ACC.2008.4586493
Okubanjo, A., Oyetola, O., Osifeko, M., Olaluwoye, O., Alao, P., 2017. Modeling of 2-dof robot arm and control. Futo J Series (FUTOJNLS) 3 (2), 80–92.
Popescu, M.-C., Balas, V., Perescu-Popescu, L., Mastorakis, N., 07 2009. Multilayer perceptron and neural networks. WSEAS Transactions on Circuits and Systems 8. DOI: 10.5555/1639537.1639542
Receanu, D., 2013. Modeling and simulation of the nonlinear computed torque control in simulink/matlab for an industrial robot. vol 10, 95–106. DOI: 10.3970/SL.2013.010.095
Sharkawy, A.-N., Koustoumpardis, P., 2019. Dynamics and computed-torque control of a 2-dof manipulator: Mathematical analysis. International Journal of Advanced Science and Technology 28 (12), 201–212.
Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., 2009. Robotics: modelling, planning and control. Springer. DOI: 10.1007/978-1-84628-642-1
Sierra-Garcı́a, J. E., Santos, M., 2021. Switched learning adaptive neurocontrol strategy. Neurocomputing 452, 450–464. URL: https://www.sciencedirect.com/science/article/pii/S0925231220316507 DOI: https://doi.org/10.1016/j.neucom.2019.12.139
Spong, M. W., Hutchinson, S., Vidyasagar, M., 2020. Robot modeling and control. Vol. 2. Wiley New York. DOI: 10.1109/MCS.2006.252815
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