Neural Network-Based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults

Autor:	Sohrab Mokhtari, Alireza Abbaspour, Kang K. Yen, Arman Sargolzaei
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	unmanned aerial vehicle (UAV) faulty sensors fault detection and isolation abrupt fault feedback linearization control Science
Zdroj:	Remote Sensing, Vol 13, Iss 12, p 2396 (2021)
Druh dokumentu:	article
ISSN:	2072-4292
DOI:	10.3390/rs13122396
Popis:	A novel adaptive neural network-based fault-tolerant control scheme is proposed for six degree-of-freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate actuators and sensors’ faults in real time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter’s dynamic model to detect faults in the actuators and navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter actuators, and the helicopter tracks the desired trajectory without any interruption.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/89e914cff368469ca2e9f13e9e39b45b Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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