Abstract: Redundancy requirements for UAV (Unmanned Aerial
Vehicle) are hardly faced due to the generally restricted amount
of available space and allowable weight for the aircraft systems,
limiting their exploitation. Essential equipment as the Air Data,
Attitude and Heading Reference Systems (ADAHRS) require several
external probes to measure significant data as the Angle of Attack
or the Sideslip Angle. Previous research focused on the analysis
of a patented technology named Smart-ADAHRS (Smart Air Data,
Attitude and Heading Reference System) as an alternative method to
obtain reliable and accurate estimates of the aerodynamic angles.
This solution is based on an innovative sensor fusion algorithm
implementing soft computing techniques and it allows to obtain a
simplified inertial and air data system reducing external devices.
In fact, only one external source of dynamic and static pressures
is needed. This paper focuses on the benefits which would be
gained by the implementation of this system in UAV applications.
A simplification of the entire ADAHRS architecture will bring to
reduce the overall cost together with improved safety performance.
Smart-ADAHRS has currently reached Technology Readiness Level
(TRL) 6. Real flight tests took place on ultralight aircraft equipped
with a suitable Flight Test Instrumentation (FTI). The output of
the algorithm using the flight test measurements demonstrates the
capability for this fusion algorithm to embed in a single device
multiple physical and virtual sensors. Any source of dynamic and
static pressure can be integrated with this system gaining a significant
improvement in terms of versatility.
Abstract: The purpose of this paper is to provide a practical
example to the Linear Quadratic Gaussian (LQG) controller. This
method includes a description and some discussion of the discrete
Kalman state estimator. One aspect of this optimality is that the
estimator incorporates all information that can be provided to it. It
processes all available measurements, regardless of their precision, to
estimate the current value of the variables of interest, with use of
knowledge of the system and measurement device dynamics, the
statistical description of the system noises, measurement errors, and
uncertainty in the dynamics models.
Since the time of its introduction, the Kalman filter has been the
subject of extensive research and application, particularly in the area
of autonomous or assisted navigation. For example, to determine the
velocity of an aircraft or sideslip angle, one could use a Doppler
radar, the velocity indications of an inertial navigation system, or the
relative wind information in the air data system. Rather than ignore
any of these outputs, a Kalman filter could be built to combine all of
this data and knowledge of the various systems- dynamics to
generate an overall best estimate of velocity and sideslip angle.