Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Neural Network-based Active Fault-Tolerant Control Design for Unmanned Helicopter with Sensor Faults

Version 1 : Received: 13 June 2021 / Approved: 14 June 2021 / Online: 14 June 2021 (13:25:52 CEST)

A peer-reviewed article of this Preprint also exists.

Mokhtari, S.; Abbaspour, A.; Yen, K.K.; Sargolzaei, A. Neural Network-based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults. Remote Sens. 2021, 13, 2396. Mokhtari, S.; Abbaspour, A.; Yen, K.K.; Sargolzaei, A. Neural Network-based Active Fault-Tolerant Control Design for Unmanned Helicopter with Additive Faults. Remote Sens. 2021, 13, 2396.

Journal reference: Remote Sens. 2021, 13, 2396
DOI: 10.3390/rs13122396

Abstract

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate 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 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 navigation sensors, and the helicopter tracks the desired trajectory without any interruption.

Subject Areas

Unmanned aerial vehicle (UAV); faulty sensors; fault detection and isolation; abrupt fault; feedback linearization control

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