AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA025080032
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RESEARCH ARTICLE

African vultures optimization-based hybrid neural network–proportional-integral-derivative controller for improved robot manipulator tracking

Bashra Kadhim Oleiwi1* ,  Mohamed Jasim Mohamed1 ,  Ahmad Taher Azar2,3 ,  Saim Ahmed2,3* ,  Ahmed Redha Mahlous2,3 ,  Walid El-Shafai2,3,4
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1 Mechatronics and Robotics Engineering Department, College of Control and Systems Engineering, University of Technology, Baghdad, Iraq
2 College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
3 Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
4 Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, Monufia, Egypt
IJOCTA, 025080032 https://doi.org/10.36922/IJOCTA025080032
Received: 20 February 2025 | Revised: 29 June 2025 | Accepted: 2 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

Rigid robotic manipulators encounter several challenges in trajectory tracking control, including low accuracy and poor stability, resulting from uncertainties, external disturbances, and parameter variations. To address these issues, this study proposes two hybrid controllers that integrate the strengths of proportional-integral-derivative (PID) control with neural network (NN) methods for a three-link rigid robotic manipulator. These hybrid structures are the NN–PID controller and the self-tuning NN with PID (STNN–PID) controller. Their performance is compared against that of a conventional PID controller. To optimize control performance metrics, such as the integral time square error (ITSE), the parameters of the proposed controllers were tuned using the African vultures optimization algorithm. MATLAB was used to evaluate the effectiveness. Robustness tests were performed by varying the initial conditions, introducing external disturbances, and modifying system parameters. The NN–PID controller achieved ITSE values of 0.28919 × 10−4, 0.064321, and 0.001164, respectively, while the STNN–PID controller yielded values of 3.54549×10−4, 3.526199, and 0.883710, respectively. Moreover, when all these conditions were applied simultaneously, the NN–PID controller achieved an ITSE of 0.073968, compared to 2.672754 for the STNN–PID controller. These results demonstrate that the NN–PID controller outperforms the other controllers across all testing conditions. These findings confirm that the NN–PID controller is the most effective controller in terms of tracking accuracy, stability, and robustness across all test scenarios.

Keywords
3-Link rigid robotic manipulator
African vultures optimization algorithm
Neural network
Proportional-integral-derivative controller
Self-tuning proportional-integral-derivative controller
Trajectory tracking
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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