کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5004255 | 1461188 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV](/preview/png/5004255.png)
چکیده انگلیسی
A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISA Transactions - Volume 67, March 2017, Pages 317-329
Journal: ISA Transactions - Volume 67, March 2017, Pages 317-329
نویسندگان
Alireza Abbaspour, Payam Aboutalebi, Kang K. Yen, Arman Sargolzaei,