Article: GA-optimised fuzzy control for trajectory tracking of a 3-DOF robotic manipulator Journal: International Journal of Automation and Control (IJAAC) 2026 Vol.20 No.8 pp.1 - 27 Abstract: This paper investigates the use of three control techniques to improve trajectory tracking of a 3-DOF robotic arm. Specifically, Fuzzy Logic Control, Adaptive Neuro Fuzzy Inference System-based control, and a Genetic Algorithm (GA)-enhanced fuzzy controller are examined, with the objective of improving tracking accuracy and handling disturbances and parameter variations. Models were derived, and controllers' performance was evaluated using MATLAB/Simulink. Results showed that the GA-optimised fuzzy controller outperformed the others, achieving the highest accuracy and stability. Under external disturbances, the GA-optimised fuzzy controller maintained strong performance, achieving an average root mean square error (RMSE) reduction of 89% compared to feedback linearisation controller (FBLC), and reductions of 95% and 91% compared to conventional fuzzy logic control (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Moreover, under severe parameter variations, it demonstrated robust resilience, maintaining stable tracking with consistently low RMSE, whereas the FBLC failed. Findings suggest that the GA-optimised fuzzy controller provides an effective solution for applications where precision and resilience to external changes are critical. Inderscience Publishers - linking academia, business and industry through research

Title: GA-optimised fuzzy control for trajectory tracking of a 3-DOF robotic manipulator

Authors: Abdullah Abushokor; Asem Abdalhadi; Muhammad Faizan Mysorewala

Addresses: Department of Control and Instrumentation Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia ' Department of Control and Instrumentation Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia ' Department of Control and Instrumentation Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia; Department of Computer Engineering, University of Sharjah, Sharjah, UAE

Abstract: This paper investigates the use of three control techniques to improve trajectory tracking of a 3-DOF robotic arm. Specifically, Fuzzy Logic Control, Adaptive Neuro Fuzzy Inference System-based control, and a Genetic Algorithm (GA)-enhanced fuzzy controller are examined, with the objective of improving tracking accuracy and handling disturbances and parameter variations. Models were derived, and controllers' performance was evaluated using MATLAB/Simulink. Results showed that the GA-optimised fuzzy controller outperformed the others, achieving the highest accuracy and stability. Under external disturbances, the GA-optimised fuzzy controller maintained strong performance, achieving an average root mean square error (RMSE) reduction of 89% compared to feedback linearisation controller (FBLC), and reductions of 95% and 91% compared to conventional fuzzy logic control (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Moreover, under severe parameter variations, it demonstrated robust resilience, maintaining stable tracking with consistently low RMSE, whereas the FBLC failed. Findings suggest that the GA-optimised fuzzy controller provides an effective solution for applications where precision and resilience to external changes are critical.

Keywords: intelligent control; trajectory tracking; robotic arm; FLC; fuzzy logic control; Genetic Algorithm.

DOI: 10.1504/IJAAC.2026.157259

International Journal of Automation and Control, 2026 Vol.20 No.8, pp.1 - 27

Received: 17 Mar 2025
Accepted: 10 Oct 2025
Published online: 26 Oct 2026 *

Title: GA-optimised fuzzy control for trajectory tracking of a 3-DOF robotic manipulator

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