Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

2020 ◽  
pp. 129-134
Keyword(s):  
Predictive Model ◽  
Unmanned Aerial Vehicle ◽  
Predictive Models ◽  
Navigation System ◽  
Error Compensation ◽  
Compensation Scheme ◽  
Radar Images ◽  
Aerial Vehicle ◽  
System Errors ◽  
Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

Development of the navigation complex structure for a high-precision unmanned aerial vehicle for flight in the atmosphere

2020 ◽  
Keyword(s):  
Mathematical Modeling ◽  
Unmanned Aerial Vehicle ◽  
High Precision ◽  
Kalman Filters ◽  
Inertial Navigation System ◽  
Complex Structure ◽  
Navigation Systems ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
System Errors

The navigation systems as part of the navigation complex of a high-precision unmanned aerial vehicle in conditions of different altitude flight are investigated. The working contours of the navigation complex with correction algorithms for an unmanned aerial vehicle during high-altitude and low-altitude flights are formed. Mathematical models of inertial navigation system errors used in non-linear and linear Kalman filters are presented. The results of mathematical modeling demonstrate the effectiveness of the working contours effectiveness of the navigation complex with correction algorithms. Keywords high-precision unmanned aerial vehicle; navigation complex; multi-altitude flight; work circuit; passive noises; Kalman filter; correction

The Design and Error Compensation Research of 3-Axis Electronic Compass Installed in Small Muti-Rotors Unmanned Aerial Vehicle

2012 ◽  
Vol 256-259 ◽  
pp. 2270-2273
Author(s):  
Song Wei Fan ◽  
Hong Wei Bian
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Error Compensation ◽  
Zero Bias ◽  
Unmanned Vehicle ◽  
Bias Adjustment ◽  
Self Calibration ◽  
Aerial Vehicle ◽  
Electronic Compass

A 3-axis electronic compass is designed for small multi-rotors unmanned vehicle. The STM32F103 is used as E-compass’ CPU, and ADXL345 and MAG3110 is used as the acceleration and geomagnetic sensor. The E-compass’ software is programmed by using IAR EWARM. For outdoor applications, the ellipsoid assumption theory is simply proved and used for E-compass’ self-calibration. By using the zero-bias adjustment for pre-calibration and the fitellipsoid compensation for precise calibration, the E-compass’ precision is nearly 1 degree.

scholarly journals Online Adaptive Error Compensation SVM-Based Sliding Mode Control of an Unmanned Aerial Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Kaijia Xue ◽  
Congqing Wang ◽  
Zhiyu Li ◽  
Hanxin Chen
Keyword(s):  
Sliding Mode Control ◽  
Unmanned Aerial Vehicle ◽  
Error Compensation ◽  
Sliding Mode ◽  
Control Process ◽  
Support Vector ◽  
Control Effect ◽  
Unknown Parameters ◽  
Mode Control ◽  
Aerial Vehicle

Unmanned Aerial Vehicle (UAV) is a nonlinear dynamic system with uncertainties and noises. Therefore, an appropriate control system has an obligation to ensure the stabilization and navigation of UAV. This paper mainly discusses the control problem of quad-rotor UAV system, which is influenced by unknown parameters and noises. Besides, a sliding mode control based on online adaptive error compensation support vector machine (SVM) is proposed for stabilizing quad-rotor UAV system. Sliding mode controller is established through analyzing quad-rotor dynamics model in which the unknown parameters are computed by offline SVM. During this process, the online adaptive error compensation SVM method is applied in this paper. As modeling errors and noises both exist in the process of flight, the offline SVM one-time mode cannot predict the uncertainties and noises accurately. The control law is adjusted in real-time by introducing new training sample data to online adaptive SVM in the control process, so that the stability and robustness of flight are ensured. It can be demonstrated through the simulation experiments that the UAV that joined online adaptive SVM can track the changing path faster according to its dynamic model. Consequently, the proposed method that is proved has the better control effect in the UAV system.

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