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.
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.
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.
Keywords
Unmanned aerial vehicle (UAV); faulty sensors; fault detection and isolation; abrupt fault; feedback linearization control
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
