Real-Time Adaptive Intelligent Control System for Quadcopter Unmanned Aerial Vehicles With Payload Uncertainties

Autor:	Rajkumar Muthusamy, Hemanshu R. Pota, Praveen Kumar Muthusamy, Matthew Garratt
Rok vydání:	2022
Předmět:	Quadcopter Artificial neural network Control and Systems Engineering Control theory Payload Computer science Control system Trajectory Electrical and Electronic Engineering Fuzzy logic Drone
Zdroj:	IEEE Transactions on Industrial Electronics. 69:1641-1653
ISSN:	1557-9948 0278-0046
DOI:	10.1109/tie.2021.3055170
Popis:	A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4a6bb678d2c4f33037939b5e6619e18d https://doi.org/10.1109/tie.2021.3055170 Zobrazit plný text záznamu