Abstract
The most common filters used to determine the angular position of quadrotors are the Kalman filter and the complementary filter. The problem of angular position estimation consist is a result of the absence of direct data. The most common sensors on board UAVs are micro electro mechanical system (MEMS) type sensors. The data acquired from the sensors are processed using digital filters. In the literature, the results of research conducted on the effectiveness of Kalman and complementary filters are known. A significant problem in evaluating the performance of the studied filters was the lack of an arbitrarily determined UAV position. The authors of this paper undertook the task of determining the best filter for a real object. The main objective of this research was to improve the stability of the physical quadrotor. For this purpose, we developed a research method using a laboratory station for testing quadrotor drones. Moreover, using the MATLAB environment, they determined the optimal parameters for the real filter applied using the PX4 software, which is new and has not been considered before in the available scientific literature. It should be mentioned that the authors of this work focused on the analysis of filters most commonly used for flight stabilization, without modifying the structure of these filters. By not modifying the filter structure, it is possible to optimize the existing flight controllers. The main contribution of this study lies in finding the most optimal filter, among those available in flight controllers, for angular position estimation. The special emphasis of our work was to develop a procedure for selecting the filter coefficients for a real object. The algorithm was designed so that other researchers could use it, provided they collected arbitrary data for their objects. Selected results of the research are presented in graphical form. The proposed procedure for improving the embedded filter can be used by other researchers on their subjects.
Highlights
The Kalman filter and the complementary filter are the most popular filters for determining the angular position of unmanned aerial vehicles (UAVs)
The raw data were recorded for all degrees of freedom in which the sensors implemented in the flight controller operate
The paper presents a acomparison ofofavailable methods ofof filtering signals from the most popular sensors used in unmanned aerial vehicles
Summary
The Kalman filter and the complementary filter are the most popular filters for determining the angular position of unmanned aerial vehicles (UAVs). The problem of angle estimation is the absence of direct data. The most common sensors on board UAVs are micro electro mechanical system (MEMS) sensors. Basic sensors are used, such as accelerometer, gyroscope, magnetometer sensors. The data acquired from the sensors are processed by using digital filters. The Kalman filter has been studied in [1,2,3,4,5,6,7]. Publications are available in which the authors have undertaken modifications to the structure of the Kalman filter
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