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001			vtls000105296
003			KUKTEM
005			20251117113402.0
008			180925t20182018my a f a m 001 0 eng d
020			_aTHE0008060(Local)
039		9	_a201906190843 _bamy2 _y201809251523 _zfateeha
040			_aUMP _beng _cUMP _erda
090			_aFKEE .C43 2018 r Thesis
100	1		_aChan, Shi Jing, _eauthor.
245	1	0	_aHybrid fault detection using kalman filter and neural network for quadrotor micro aerial vehicle / _cChan Shi Jing
264		1	_aKuantan, Pahang : _bUMP, _c2018
264		4	_c© 2018
300			_axiii, 110 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Electrical and Electronics Engineering
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2018
504			_aIncludes bibliographical references
520	3		_aThis thesis introduces the application of time-domain Hybrid Fault Detection (HFD) methods for application in a quadrotor Micro Aerial Vehicle (MAV). The application aims to solve one of the main problems of the quadrotor, which is its inability to reach the exact target location that the user intended. The problem may be due to a faulty signal happened in the sensor or the actuator side or both, causing the quadrotor unable to complete the task given. Among the reasons of the faulty signal are the occurrences of signal in quadrotor, in the sensor or actuator side, as well as possible communication problem. When actuator fault, sensor fault or both faults occur, the controllers cannot function well and hence its performance reduce. At the initial control design stage of quadrotor, it is usually designed under the assumption that no faults would occur in quadrotors. The Faulty Detection (FD) method is therefore crucial to ensure quadrotor system can work properly and efficiently. The proposed method for the fault detection in this study uses hybrid technique which combines the extended kalman filter and artificial neural network (ANN). Two classes of approaches are analysed: the fault system identification approach ANN and the observer-based approach using the extended kalman filter. The extended kalman filter recognizes data from the sensors of the system and indicates the residuals of the system in the sensor reading. Residuals prediction is based on the fault magnitude and the time occurrence of fault. The information will then be fed to ANN, which consists of a bank of parameter estimation that generates the failure state. ANN is an algorithm that is used to determine the fault condition and determine its severity in the quadrotor system. ANN is designed based on nonlinear autoregressive network with exogenous inputs (NARX) scheme so that it can be trained to generate output based on the simulation behaviours of the quadrotor. The different fault locations are used as input vectors for training an artificial neural network (ANN). The result of the residual signal before filtration and after filtration showed that Kalman-ANN is able to identify single fault as well as multiple faults. For all individual faults including the multiple fault detection, the accuracy of the detection is 78.89 percent. It can be conclude that the newly proposed hybrid FD method in this thesis is able to accurately detect the location fault, for both the sensor and actuator faults simultaneous in the quadrotor.
610	2	0	_aFaculty of Electrical and Electronics Engineering _xDissertations
650		0	_aUniversities and colleges _xDisertations
650		0	_aTheses
999			_aVIRTUA40 _c7772 _d7778
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