scholarly journals An Aeromagnetic Compensation Algorithm for Aircraft Based on Fuzzy Adaptive Kalman Filter

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yao Zhang ◽  
Yu-Xin Zhao ◽  
Shuai Chang
Keyword(s):  
Kalman Filter ◽  
Geomagnetic Field ◽  
Precise Measurement ◽  
Adaptive Kalman Filter ◽  
New Approach ◽  
Compensation Algorithm ◽  
Simulation Results ◽  
Fuzzy Adaptive

In order to ensure the effectiveness of geomagnetic navigation, as the foundation, the precise measurement of geomagnetic field must be guaranteed; namely, aircraft aeromagnetic compensation is worthy of being further studied. In this paper, the classical aircraft aeromagnetic compensation algorithm based on Leliak Model is analyzed and an aircraft aeromagnetic compensation algorithm based on fuzzy adaptive Kalman filter is proposed, which is a new approach for aircraft to achieve aeromagnetic compensation. Simulation results show that it has better compensation performance without relying on the aircraft attitude.

Fuzzy adaptive Kalman filter for the drive system with an elastic coupling

2013 ◽  
Vol 62 (2) ◽  
pp. 251-265 ◽  
Author(s):  
Piotr J. Serkies ◽  
Krzysztof Szabat
Keyword(s):  
Kalman Filter ◽  
Control Structure ◽  
Experimental Tests ◽  
Drive System ◽  
Adaptive Kalman Filter ◽  
State Matrix ◽  
State Controller ◽  
Kalman Gain ◽  
Robust Adaptive ◽  
Fuzzy Adaptive

Abstract In the paper issues related to the design of a robust adaptive fuzzy estimator for a drive system with a flexible joint is presented. The proposed estimator ensures variable Kalman gain (based on the Mahalanobis distance) as well as the estimation of the system parameters (based on the fuzzy system). The obtained value of the time constant of the load machine is used to change the values in the system state matrix and to retune the parameters of the state controller. The proposed control structure (fuzzy Kalman filter and adaptive state controller) is investigated in simulation and experimental tests.

Low cost automation using INS/GPS data fusion for accurate positioning

Robotica ◽  
2003 ◽  
Vol 21 (3) ◽  
pp. 255-260 ◽  
Author(s):  
J. Z. Sasiadek ◽  
Q. Wang
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Autonomous Vehicles ◽  
Low Cost ◽  
Real Time Control ◽  
Adaptive Kalman Filter ◽  
Time Control ◽  
Global Positioning ◽  
And Control ◽  
Fuzzy Adaptive

Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS.

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