Robust fault diagnosis method of flight control system based on disturbance decoupling assignment

2010 ◽  
Author(s):  
Song yuqin ◽  
Zhang weiguo
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Flight Control ◽  
Flight Control System ◽  
Disturbance Decoupling ◽  
Diagnosis Method

Study on Intelligent Fault Diagnosis of a Type of Ammunition’s Flight Control System Based on Fuzzy Theory

2010 ◽  
Vol 40-41 ◽  
pp. 903-908
Author(s):  
Jing Qing Xu ◽  
Xing Lin Qi ◽  
Liang Liang Ren ◽  
Liang Cui
Keyword(s):  
Control System ◽  
Fault Diagnosis ◽  
Flight Control ◽  
Fuzzy Theory ◽  
Function Parameter ◽  
Flight Control System ◽  
Intelligent Fault Diagnosis ◽  
Practical Situation ◽  
Diagnosis Method ◽  
Fuzzy Diagnosis

Fault symptom of a type of ammunition’s flight control system shows fuzzy, and there is no intelligent fault diagnosis method for this control system fault diagnosis. This paper studies on intelligent fault diagnosis of terminally guide projectile based on fuzzy theory. The procedure of fuzzy fault diagnosis is shown. Combining with the practical situation of a type of ammunition’s flight control system and a large quantity of expert’ experience knowledge, the fuzzy diagnosis model is established. After given fuzzy diagnosis matrix, fault diagnosis of a type of ammunition’s flight control system was done as example. It practicability and credibility is shown by fault diagnosis result, and application of fuzzy theory in diagnosing the fault of a type of ammunition’s flight control system is proved valid. Two piece of proposal is proposed after analyzing the function parameter changing of this control system stored in storehouse longtime.

scholarly journals Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1350 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Wu ◽  
Xiong ◽  
Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Early Warning ◽  
Flight Control ◽  
Data Driven ◽  
Diagnostic Model ◽  
Flight Control System ◽  
Model Based ◽  
On Line

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

Sensor Fault Diagnosis for an UAV Control System Based on a Strong Tracking Kalman Filter

2014 ◽  
Vol 687-691 ◽  
pp. 270-274 ◽  
Author(s):  
Feng Tian ◽  
Jian Yang Zheng ◽  
Tong Zhang
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Fault Diagnosis ◽  
Real Time ◽  
Flight Control ◽  
Joint Estimation ◽  
Filter Method ◽  
State Variables ◽  
Flight Control System ◽  
Sensor Fault

The fault diagnosis of unmanned aerial vehicle (UAV) flight control system is an important research of UAV in health management. The sensor is the link which easiest to have problems of the flight control system. Making timely and accurate prediction of its faults is particularly important. A strong tracking Kalman Filter method for the sensor fault diagnosis of UAV flight control system was presented in this paper. The parameters of the system were extended to the state variables, the sensor fault observer was constructed, and the joint estimation of states and parameters of flight control system were gotten. The method can be used to real-time estimate the unmeasured states and time-varying parameters. The results of simulation experiments show that the method has a good real-time and accuracy in the sensor fault diagnosis of flight control system.

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