Dependence of the amplitude of transverse oscillations of the axisymmetric UAV rotating around the longitudinal axis on the residual imbalance

The analysis of the relationship between residual imbalance of an axisymmetric cylindrical UAV, and the amplitude of its prevailing transverse oscillations during the flight. The residual imbalance of an UAV that rotates along the roll and its effect on the amplitude of the vehicle`s transverse oscillations in real flight are discussed and investigated. The total imbalance and the average imbalance of UAV were calculated on the basis of dynamic imbalance measurements in two planes. Flight data from twelve UAV launches was obtained for this dependence analysis. During angular velocity sensors signals processing an orientation algorithm with one quaternion equation was applied. Eiler-Krylov angles were obtained from the resulting orientation quaternion and an average and total imbalance influence on transverse oscillations amplitude was investigated. During the analysis of the tests held, it is possible to make a conclusion that transverse oscillations amplitude directly depends on the magnitude of residual imbalance. Transverse oscillations amplitude mostly depends on average imbalance (which is arithmetic average between imbalance magnitudes in every plane of measurement) and it depends less on total imbalance (which is geometric sum of imbalance vectors in every plane of measurement): average imbalance change from 100 g∙mm to 700 g∙mm caused transverse oscillations amplitude change from 0,5° to 2°. In some cases we will see spread of oscillations amplitude values up to ±1° relatively approximation line, this is due to other factors influence, besides imbalance, such as aerodynamic asymmetry. However, the tendency of oscillations amplitude increase with the increase of imbalance is preserved. The obtained results give us an explicit dependence of transverse oscillations amplitude of UAV from its dynamic imbalance in specific conditions of flight (velocity, type of trajectory and rotation velocity) and with specific UAV parameters (aerodynamic, mass-inertial and other parameters). Change of these parameters may cause change of specific quantitative parameters of obtained dependence, but its nature remains the same. The mass of the investigated UAV was approximately 15 kg, trajectory was ballistic, flight speed was transonic and subsonic, rotation velocity around longitudinal axis was 1..7 rounds per second.

A New Method of Integrating the Equations of Autonomous Strapdown INS

We propose the new version of separating the process of integrating the differential equations, which describe the functioning of the strapdown inertial navigation system (SINS) in the normal geographic coordinate system (NGCS), into rapid and slow cycles. In this version, the vector of the relative velocity of an object is represented as a sum of a rapidly changing component and a slowly changing component. The equation for the rapidly changing component of the relative velocity includes the vectors of angular velocities of the Earth’s rotation, NGCS rotation, and, at the same time, the vectors of the apparent acceleration and gravity acceleration, because these accelerations partially balance each other, and at rest relative to the Earth are balanced completely. The equation of the slowly changing component of the relative velocity includes only the vector of angular velocity of the Earth’s rotation and the vector of NGCS rotation. The quaternion orientation of an object relative to the NGCS is represented as a product of two quaternions: a rapidly changing one, which is determined by the absolute angular velocity of an object, and slowly changing one, which is determined by the angular velocity of the NGCS. The right parts of the equations for each group of variables depend on the rapidly changing and slowly changing variables. In order to enable the independent integration of the slow and rapid cycle equations, the algorithm have been developed for integrating the equations using the predictor and corrector for the cases of instantaneous and integral information generated by SINS sensors. At each predictor step the Euler method is used to estimate the longitude, latitude and altitude of an object, slowly changing component of the relative velocity, and slowly changing multiplier of the orientation quaternion at the rightmost point of the slow cycle. Then the Euler-Cauchy method is used to integrate the equations for the rapidly changing components on the rapid cycle intervals, which are present in the slow cycle. The necessary values of the slowly changing components in the intermediate points are calculated using the formulas of linear interpolation. After the rapidly changing components are estimated at the rightmost point of the slow cycle, at the corrector step the Euler-Cauchy method is used to refine the values of the slowly changing components at the rightmost point of the slow cycle. Note that at the beginning of each slow cycle step the slowly changing component of velocity is equal to the value of the relative velocity of an object, and the rapidly changing component is zero. Similarly, at the beginning of each slow cycle step the slowly changing multiplier of object’s orientation quaternion equals to the quaternion of orientation of an object relative to the NGCS, and the rapidly changing multiplier of the orientation of an object has its scalar part equal to one, and its vector part equal to zero (this formula is derived from the quaternion formula for adding the finite rotations). SINS on a stationary base had been simulated in the presence of perturbations for a large time interval for a diving object, which drastically changes its height over short time periods.

Orientation Estimation for Motion Capture Unit with Significant Motion Interference

The orientation estimation is a critical technique in inertial sensor based motion capture systems. One challenge of the orientation estimation is that it suffers from the acceleration interference due to body segment motion, especially when the acceleration interference is significant. In this paper, we propose a quaternion based orientation estimation algorithm using unscented Kalman filter. In the algorithm, the acceleration interference is taken as an element of the state vector and estimated in the algorithm together with the orientation quaternion, knowing that the acceleration interference can be predicted based on the rotational angular velocity. The experiments were conducted using both computer simulation and in real-world motion scenarios. Both experimental results have shown the effectiveness of the proposed orientation estimation algorithm.