FUZZY LOGIC CONTROL IN ROBOT MANIPULATORS: A COMPARATIVE ANALYSIS WITH BOOLEAN LOGIC
Article Sidebar
Main Article Content
Abstract
Robot manipulators encounter significant control challenges stemming from nonlinear dynamics, parametric uncertainties, and external disturbances. While Boolean logic-based controllers operate through precise binary thresholds, fuzzy logic control utilizes linguistic rules and graded membership to emulate human decision-making. This paper examines both control paradigms, detailing theoretical foundations and practical implementation for a 2-DOF manipulator. Quantitative comparisons reveal fuzzy logic’s superior adaptability in trajectory tracking and disturbance rejection, while Boolean methods maintain advantages in computational efficiency. The study concludes with controlled selection guidelines based on application-specific requirements.
Downloads
Article Details
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
Public License Terms
(For Open Journal Systems (OJS))
-
Copyright:
The copyright of the published article remains with the author(s). However, after publication, the article is distributed on the OJS platform under the Creative Commons (CC BY) license. -
License Type:
This article is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means users can utilize the article under the following conditions:- Copy and distribute: The text of the article or its parts can be freely distributed.
- Quote and analyze: Parts of the article can be used for quoting and analysis.
- Free use: The article can be freely used for research and educational purposes.
- Attribution: Users must provide proper attribution and reference to the original source.
-
Commercial use:
The article can be used for commercial purposes, provided that authorship and source are properly cited. -
Document modification:
The text or content of the article can be modified or adapted, as long as it does not harm the authorship. -
Liability disclaimer:
The author(s) are responsible for the accuracy of the information contained in the article. The editorial team of the platform is not liable for any damages resulting from the use of this information. -
Public usage obligations:
The content of the article must be used only in accordance with legal and ethical standards. Unauthorized use is strictly prohibited.
Note:
These license terms are designed to ensure transparency and openness in material usage. By accepting these terms, you agree to the adaptation and distribution of the article content under the terms of the Creative Commons license.
Link: Creative Commons Attribution 4.0 International (CC BY 4.0)
How to Cite
References
[1] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
[2] Lee, C. C. (1990). Fuzzy logic in control systems: Fuzzy logic controller. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 404–435. https://doi.org/10.1109/21.52551 DOI: https://doi.org/10.1109/21.52551
[3] Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2006). Robot modeling and control. Wiley. https://www.wiley.com/en-us/Robot+Modeling+and+Control-p-9780471649908
[4] Passino, K. M., & Yurkovich, S. (1998). Fuzzy control. Addison-Wesley. https://www.pearson.com/us/higher-education/program/Passino-Fuzzy-Control/PGM228476.html
[5] Li, T.-H. S., et al. (2020). Adaptive fuzzy hierarchical sliding-mode control for robot manipulators. IEEE Transactions on Industrial Electronics, 67(9), 7329–7340. https://doi.org/10.1109/TIE.2019.2938470 DOI: https://doi.org/10.1109/TIE.2019.2938470
[6] Craig, J. J. (2018). Introduction to robotics: Mechanics and control (4th ed.). Pearson.
[7] Marquez, H. J. (2003). Nonlinear control systems: Analysis and design. Wiley.