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Volume 13
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Article

Optimization of Fuzzy Adaptive Logic Controller for Robot Manipulators Using Modified Greater Cane Rat Algorithm

by
Jian Sun
1,2,
Shuyi Wu
3,
Jinfu Chen
1,
Xingjia Li
4,*,
Ziyan Wu
1,
Ruiting Xia
1,
Wei Pan
2 and
Yan Zhang
1
1
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China
2
School of Information Engineering, Jiangsu Vocational College of Agriculture and Forestry, Jurong 212400, China
3
School of Software Technology, Dalian University of Technology, Dalian 116086, China
4
School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China
*
Author to whom correspondence should be addressed.
Mathematics 2025, 13(10), 1631; https://doi.org/10.3390/math13101631
Submission received: 29 March 2025 / Revised: 1 May 2025 / Accepted: 12 May 2025 / Published: 15 May 2025
(This article belongs to the Special Issue Computational Methods, Algorithms and Models for IoT and Information Security)
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Abstract

In the control of robot manipulators, input torque constraints and system nonlinearities present significant challenges for precise trajectory tracking. However, fuzzy adaptive logic control (FALC) often fails to generate the optimal membership functions or function intervals. This paper proposes a modified greater cane rat algorithm (MGCRA) to optimize a fuzzy adaptive logic controller (FALC) for minimizing input torques during trajectory tracking tasks. The main innovation lies in integrating the improved MGCRA with FALC, which enhances the controller’s adaptability and performance. For benchmarking, several state-of-the-art swarm intelligence algorithms—including particle swarm optimization (PSO), artificial bee colony (ABC), ant colony optimization (ACO), gray wolf optimization (GWO), covariance matrix adaptation evolution strategy (CMA-ES), adaptive guided differential evolution (AGDE), the basic greater cane rat algorithm (GCRA), and a trial-and-error method—are compared under identical conditions. Experimental results show that the MGCRA-tuned FALC achieves lower input torques and improved trajectory tracking accuracy compared to other methods. The findings demonstrate the effectiveness and potential of the proposed MGCRA-FALC framework for advanced robotic manipulator control.
Keywords: fuzzy adaptive logic control (FALC); swarm intelligence; modified greater cane rat algorithm (MGCRA); friction compensation; robot manipulators fuzzy adaptive logic control (FALC); swarm intelligence; modified greater cane rat algorithm (MGCRA); friction compensation; robot manipulators

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MDPI and ACS Style

Sun, J.; Wu, S.; Chen, J.; Li, X.; Wu, Z.; Xia, R.; Pan, W.; Zhang, Y. Optimization of Fuzzy Adaptive Logic Controller for Robot Manipulators Using Modified Greater Cane Rat Algorithm. Mathematics 2025, 13, 1631. https://doi.org/10.3390/math13101631

AMA Style

Sun J, Wu S, Chen J, Li X, Wu Z, Xia R, Pan W, Zhang Y. Optimization of Fuzzy Adaptive Logic Controller for Robot Manipulators Using Modified Greater Cane Rat Algorithm. Mathematics. 2025; 13(10):1631. https://doi.org/10.3390/math13101631

Chicago/Turabian Style

Sun, Jian, Shuyi Wu, Jinfu Chen, Xingjia Li, Ziyan Wu, Ruiting Xia, Wei Pan, and Yan Zhang. 2025. "Optimization of Fuzzy Adaptive Logic Controller for Robot Manipulators Using Modified Greater Cane Rat Algorithm" Mathematics 13, no. 10: 1631. https://doi.org/10.3390/math13101631

APA Style

Sun, J., Wu, S., Chen, J., Li, X., Wu, Z., Xia, R., Pan, W., & Zhang, Y. (2025). Optimization of Fuzzy Adaptive Logic Controller for Robot Manipulators Using Modified Greater Cane Rat Algorithm. Mathematics, 13(10), 1631. https://doi.org/10.3390/math13101631

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