A Self-Tuning Hybrid Control Architecture Integrating Fuzzy Logic and Evolutionary Computation for Uncertain Robotic Dynamics

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 3

Year of Publication : 2026

Authors : Thi-Mai-Phuong Dao, Minh-Tuan Phan, Trung-Kien Le, Anh-Nam Tran Nguyen, Tien-Loc Le, Tien-Dung Nguyen, Duy-Thuan Vu, Ngoc-Khoat Nguyen

10.14445/23488549/IJECE-V13I3P103

How to Cite?

Thi-Mai-Phuong Dao, Minh-Tuan Phan, Trung-Kien Le, Anh-Nam Tran Nguyen, Tien-Loc Le, Tien-Dung Nguyen, Duy-Thuan Vu, Ngoc-Khoat Nguyen, "A Self-Tuning Hybrid Control Architecture Integrating Fuzzy Logic and Evolutionary Computation for Uncertain Robotic Dynamics," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 3, pp. 28-40, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I3P103

Abstract:

A hybrid control approach applying fuzzy logic and evolutionary computation techniques, which can be considered an efficient methodology in an industrial robot manipulation system, is studied in this paper. The proposed approach combines the flexibility of fuzzy logic techniques with the optimization capabilities of evolutionary computation to improve motion accuracy, sensor-based decision-making, and operational robustness in nonlinear and uncertain environments. A hybrid control model is proposed, where key parameters of the fuzzy controller, such as membership functions and fuzzy rules from a swarm based optimization algorithm with an auto-tuning organization for biological systems to work on input-output mappings, are automatically adjusted. This adaptive approach accelerates the convergence rate, minimizes steady state errors, and enhances the ability of a robot to handle external loads and environmental disturbances. Through numerical simulations, the results show that this newly developed method exceeds in trajectory accuracy and is more stable as well as adaptable to different loads than classic PID and independent fuzzy logic controllers. These results also validate the efficacy of the hybrid fuzzy-evolutionary control for the realization of intelligent, autonomous, and highly adaptive industrial robotic systems.

Keywords:

Industrial robot, Fuzzy Logic Control, Evolutionary algorithm, Parameter optimization, Hybrid control system.

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