| Autor: |
Le, Tien-Loc, Hung, Nguyen Huu |
| Předmět: |
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| Zdroj: |
Neural Computing & Applications; Aug2024, Vol. 36 Issue 22, p13617-13627, 11p |
| Abstrakt: |
This article presents a novel approach to achieve precise trajectory tracking control for a line-following quadcopter by employing a multilayer type-2 fuzzy Petri nets controller (MT2PNC). The MT2PNC dynamically adapts its parameters based on tracking errors, allowing for real-time adjustments to the quadcopter's tilt angles and flight direction. The effectiveness of the controller is thoroughly evaluated through both simulations and experimental studies. In the experimental study, a camera is integrated into the quadcopter to capture line images, which are then processed using sophisticated image processing algorithms to extract essential line information. This extracted data is subsequently fed into the MT2PNC, enabling the quadcopter to precisely follow the reference line. The simulation and experimental results conclusively demonstrate the superior control efficacy of the MT2PNC, showcasing its remarkable ability to accurately track the quadcopter's trajectory. The proposed control method exhibits great promise for line-following and trajectory-tracking applications, and its practical implementation holds substantial potential. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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