A practical fuzzy logic controller for the path tracking of wheeled mobile robots

2003 ◽  
Vol 23 (2) ◽  
pp. 60-65 ◽  
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robots ◽  
Fuzzy Logic Controller ◽  
Path Tracking ◽  
Wheeled Mobile Robots

scholarly journals Fuzzy Logic Based Path Tracking Controller for Wheeled Mobile Robots

2014 ◽  
Vol 6 (2) ◽  
pp. 145-150 ◽  
Author(s):  
Umar Farooq ◽  
K. M. Hasan ◽  
Athar Hanif ◽  
Muhammad Amar ◽  
Muhammad Usman Asad
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robots ◽  
Path Tracking ◽  
Wheeled Mobile Robots ◽  
Tracking Controller

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

scholarly journals Anti‐sideslip path tracking of wheeled mobile robots based on fuzzy model predictive control

2020 ◽  
Vol 56 (10) ◽  
pp. 490-493
Author(s):  
Guoxing Bai ◽  
Yu Meng ◽  
Li Liu ◽  
Weidong Luo ◽  
Qing Gu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Mobile Robots ◽  
Predictive Control ◽  
Fuzzy Model ◽  
Path Tracking ◽  
Wheeled Mobile Robots

scholarly journals Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 18426-18441
Author(s):  
Mohamed Abdelwahab ◽  
Victor Parque ◽  
Ahmed M. R. Fath Elbab ◽  
A. A. Abouelsoud ◽  
Shigeki Sugano
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robots ◽  
Trajectory Tracking ◽  
Wheeled Mobile Robots
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