AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA025180091
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RESEARCH ARTICLE

Model reference adaptive control based time delay estimation with RBF neural network for robot manipulators

Saim Ahmed1,2* ,  Ahmad Taher Azar1,2 ,  Ibraheem Kasim Ibraheem3,4
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1 College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
2 Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
3 Department of Electrical Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq
4 Department of Electronics and Communication Engineering, College of Engineering, Uruk University, Baghdad, Iraq
IJOCTA, 025180091 https://doi.org/10.36922/IJOCTA025180091
Received: 1 May 2025 | Revised: 4 July 2025 | Accepted: 18 July 2025 | Published online: 4 September 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

In this paper, model-reference adaptive control (MRAC) with neural network (NN) and time delay estimation (TDE) is proposed for controlling a robotic manipulator. With more than two degrees of freedom (DoF) of the robot, the formulation of a known regression matrix is tedious and also difficult to compute for the different robotic systems. Therefore, this work introduces MRAC based on TDE with NN (MRAC-NNTDE) to achieve high-control performance without prior knowledge of the regression matrix and offers a model free scheme. Firstly, MRAC is applied to adjust the control gains, then TDE is implemented to estimate the unknown dynamical robotic system, and NN is employed to deal with the TDE estimation error. The overall stability of the robotic dynamics is investigated using the Lyapunov theorem. In the end, computer simulations are compared to validate the effectiveness of the proposed scheme.

Keywords
Model reference adaptive control
Time delay estimation
Neural network
Robotic manipulator
Funding
This work is funded by Prince Sultan University, Riyadh, Saudi Arabia.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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