Fault tolerant navigation method for satellite based on information fusion and unscented Kalman filter

Since the permeant magnet synchronous generator (PMSG) has many applications in particular safety-critical applications, enhancing PMSG availability has become essential. An effective tool for enhancing PMSG availability and reliability is continuous monitoring and diagnosis of the machine. Therefore, designing a robust fault diagnosis (FD) and fault tolerant system (FTS) of PMSG is essential for such applications. This paper describes an FD method that monitors online stator winding partial inter-turn faults in PMSGs. The fault appears in the direct and quadrature (dq)-frame equations of the machine. The extended Kalman filter (EKF) and unscented Kalman filter (UKF) were used to detect the percentage and the place of the fault. The proposed techniques have been simulated for different fault scenarios using Matlab®/Simulink®. The results of the EKF estimation responses simulation were validated with the practical implementation results of tests that were performed with a prototype PMSG used in the Arab Academy For Science and Technology (AAST) machine lab. The results showed impressive responses with different operating conditions when exposed to different fault states to prevent the development of complete failure.

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A new fault tolerant control system based on adaptive unscented Kalman filter and fuzzy decision making approach

2010 Conference on Control and Fault-Tolerant Systems (SysTol) ◽

10.1109/systol.2010.5676064 ◽

2010 ◽

Cited By ~ 1

Author(s):

Karim Salahshoo ◽

Amin Mirzaee

Keyword(s):

Decision Making ◽

Kalman Filter ◽

Control System ◽

Unscented Kalman Filter ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Fuzzy Decision Making ◽

Fuzzy Decision ◽

Adaptive Unscented Kalman Filter

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Application of Improved Unscented Kalman Filter to AUV Based on Terrain-Aided Strapdown Inertial Navigation System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.389.758 ◽

2013 ◽

Vol 389 ◽

pp. 758-764 ◽

Cited By ~ 1

Author(s):

Qi Wang ◽

Dong Li ◽

Zi Jia Zhang ◽

Chang Song Yang

Keyword(s):

Kalman Filter ◽

Information Fusion ◽

Navigation System ◽

Inertial Navigation System ◽

Unscented Kalman Filter ◽

Autonomous Underwater Vehicles ◽

Inertial Navigation ◽

Strapdown Inertial Navigation System ◽

Integrated Navigation ◽

Strapdown Inertial Navigation

To improve the navigation precision of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Improved Unscented Kalman Filter (IUKF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and improved UKF method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the IUKF method is able to greatly improve the long-time navigation precision, relative to the traditional information fusion method.

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Unscented Kalman Filter based Autonomous Optical Navigation Method

2006 Chinese Control Conference ◽

10.1109/chicc.2006.280582 ◽

2006 ◽

Author(s):

Shu-lin Sui ◽

Jian Yuan ◽

Wen-xia Zhang

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Optical Navigation ◽

Navigation Method

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Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.303-306.975 ◽

2013 ◽

Vol 303-306 ◽

pp. 975-978

Author(s):

Hong Yu Zheng ◽

Chang Fu Zong

Keyword(s):

Kalman Filter ◽

Electric Vehicle ◽

Electric Vehicles ◽

Information Fusion ◽

Unscented Kalman Filter ◽

Accurate Estimate ◽

Fusion Algorithm ◽

Power Battery ◽

Battery State Of Charge ◽

New Generation

The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effective and accuracy, so it is may apply it the electric vehicle control and observation.

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