89,589 research outputs found
Continuous positive airway pressure treatment: Good for obstructive sleep apnea syndrome, maybe not for hypertension?
No full text[No abstract available
Wind component estimation for UAS flying in turbulent air
No full textOne of the most important problem of autonomous flight for UAS is the wind identification, especially for small scale vehicles. This research focusses on an identification methodology based on the Extended Kalman Filter (EKF). In particular authors focus their attention on.the filter tuning problem. The proposed procedure requires low computational power, so it is very useful for UAS. Besides it allows a robust wind component identification even when, as it is usually, the measurement data set is affected by noticeable noises. (C) 2019 Elsevier Masson SAS. All rights reserved
Automatic Wind Identification for UAS: a Case Study
No full textUAS applications are nowadays experiencing a tremendous development both in the civil and in military sector. One of the main issues for this kind of autonomous vehicles is induced by atmospheric turbulence, which may pose a severe problem especially for small size UAV. The present research extends previous investigations to a broader range of turbulence spectra and tests an innovative procedure based on Extended Kalman Filter (EKF) autotune for wind identificatio
AN EKF BASED PROCEDURE FOR AUTOMATIC PATH FOLLOWING IN TURBULENT AIR
No full textAim of the present paper is to propose a procedure to afford an accurate automatic path following in turbulent air. The technique is based on the simultaneous employment of two different EKF. The first estimates disturbances, the second one estimates deflection that are necessary to reject the estimated disturbances. The first EKF uses measurements gathered in turbulent air. The second EKF obtains command laws able to follow the desired flight path rejecting disturbances. To purchase the objective, aerodynamic coefficients have been modified by adding entirely new derivatives or synthetic increments to basic ones. The modified aircraft parameters are determined by augmenting the aircraft’s state. The filter determines the “new” set of derivatives that contains functions of control displacements. The desired flight path is injected into the second EKF as artificial instrumental measurements and the filter is tuned up to follow these. The obtained control laws are adaptive since they depend by either the characteristics of the disturbance or the desired flight path. Proposed procedure requires low computational power, therefore it is particularly suited for UAS. Besides it’s simple to implement on board, so it may be successfully employed on low cost platforms
Optimal Flight Path Determination in Turbulent Air: A Modified EKF Approach
No full textBy using the Extended Kalman Filter an accurate path following in turbulent air is performed. The procedure employs
simultaneously two dierent EKFs: the rst one estimates disturbances, the second one aords to determine the necessary
controls displacements for rejecting those ones. To tune the EKFs an optimization algorithm has been designed to
automatically determine Process Noise Covariance and Measurement Noise Covariance matrices. The rst lter, by using
instrumental measurements gathered in turbulent air, estimates wind components. The second one obtains command
laws able to follow the desired
ight path. To perform this task aerodynamic coecients have been modied. Such
a procedure leads to a set of unknown stability and control parameters containing the required displacements of the
controls. The lter estimates the new set of aircraft stability derivatives by using measurements made by the desired flight path parameters. Once the unknown stability and control derivatives have been determined, the obtained control
displacements are used to perform an accurate path following in turbulent air. The obtained control laws are adaptive
and they depend by either the characteristics of the disturbance or the desired
ight path. The proposed algorithm,
using appropriate imposed constrains, permits to tune the lter without trial and error procedure
Architettura generale degli aeromobili ad ala fissa
No full textSi definisce aeromobile ogni tipo di mezzo capace di muoversi nell'aria. A seconda del tipo di sostentazione si hanno gli aeromobili a sostentazione statica e gli aeromobili a sostentazione dinamica
Flight Control Research Laboratory Unmanned Aerial System flying in turbulent air: an algorithm for parameter identification from flight data
No full textThis work addresses the identification of the dynamics of the research aircraft FCRL (Flight
Control Research Laboratory) used for the Italian National Research Project PRIN2008 accounting
for atmospheric turbulence.
The subject vehicle is an unpressurized 2 seats, 427 kg maximum take of weight aircraft. It
features a non retractable, tailwheel, landing gear and a powerplant made up of reciprocating
engine capable of developing 60 HP, with a 60 inches diameter, two bladed, fixed pitch., tractor
propeller. The aircraft stall speed is 41.6 kts, therefore it is capable of speeds up to about 115 kts
(Sea level) and it will be cleared for altitudes up to 10.000 ft. The studied aircraft is equipped with a
research avionic system composed by sensors and computers and their relative power supply
subsystem. In particular the Sensors subsystem consists of :
Inertial Measurement Unit (three axis accelerometers and gyros)
Magnetometer (three axis)
Air Data Boom (static and total pressure port, vane sense for angle of attack and
sideslip)
GPS Receiver and Antenna
Linear Potentiometers (Aileron, Elevator, Rudder and Throttle Command)
RPM (Hall Effect Gear Tooth Sensor)
Outside air temperature Sensor
A nonlinear mathematical model of the subject aircraft longitudinal dynamics, has been tuned up
through semi empirical methods, numerical simulations and ground tests.
To taking into account the atmospheric turbulence the identification problem addressed in this
work is solved by using the Filter error method approach .In this case, the mathematical model is
given by the stochastic equations:
0 0
, , ,
, ,
x t f x t u t w t
y t h x t u t
z k y k v k
x t x
(1)
where x is the state vector, u is the control input vector, f and h are dimensional general nonlinear
vector functions, contains the unknown system parameters, z is the measurement vector ,w is the
process noise and v(k) is the measurement noise. The presence of nonmeasurable process noise
requires a suitable state estimator to propagate the states. To take into account model nonlinearities
in the present paper an Extended Kalman Filter has been implemented as the estimation algorithm
An EKF based method for path following in turbulent air
No full textAn innovative use of the Extended Kalman Filter (EKF) is proposed to perform both accurate path following and adequate disturbance rejection in turbulent air. The tuned up procedure employs simultaneously two different EKF: the first one estimates gust disturbances, the second one estimates modified aircraft parameters. The first filter, by using measurements gathered in turbulent air, estimates aircraft states and wind components. The second, by using the estimated disturbances, obtains command laws able to reject disturbances. Therefore, the estimated wind components are inserted into the predictor of the second EKF, which contains the motion equations in turbulent air. Since aerodynamic coefficients are modified by adding entirely new derivatives or synthetic increments to basic ones. So a set of unknown stability and control parameters (containing displacements of the controls) is introduced into the predictor. Therefore the aircraft’s state is augmented by the above aforementioned unknown values of aircraft parameters. The filter estimates the latters by using a set of measurements formed by the desired flight path variables. Due to the postulated unknown stability and control derivatives containing control displacements, the command laws are obtained. The obtained control laws are adaptive since they are related to either the characteristics of the disturbance or the desired flight path
Automatic Take Off and Landing for UAS Flying in Turbulent Air - An EKF Based Procedure
No full textAn innovative use of the Extended Kalman Filter (EKF) is proposed to perform automatic take off and landing by the rejection of disturbances due to turbulence. By using two simultaneously working Extended Kalman Filters, a procedure is implemented: the first filter, by using measurements gathered in turbulent air, estimates wind components; the second one, by using the estimated disturbances, obtains command laws that are able to reject disturbances. The fundamental innovation of such a procedure consists in the fact that the covariance matrices of process (Q) and measurement (R) noise are not treated as filter design parameters. In this way determined optimal values of the aforementioned matrices lead to robust control laws. At any moment, during the estimation process, the EKF employs the optimal values of Q and R. To determine these ones, adequate constrains, related to flight path characteristics, are inserted into the algorithm. In particular, to determine wind components, the constrains are imposed to elevation, altitude and longitudinal position; whereas, to determine control actions, the constrains are imposed to an adequate performance index obtained by using measurements gathered by a small set of sensors (IMU, air data boom and a low rate GPS) in turbulent air
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