TY - CONF

T1 - Flight Control Research Laboratory Unmanned Aerial Systemflying in turbulent air:an algorithm for parameter identification from flight data

AU - Grillo, Caterina

AU - Montano, Fernando

PY - 2013

Y1 - 2013

N2 - This work addresses the identification of the dynamics of the research aircraft FCRL (FlightControl Research Laboratory) used for the Italian National Research Project PRIN2008 accountingfor atmospheric turbulence.The subject vehicle is an unpressurized 2 seats, 427 kg maximum take of weight aircraft. Itfeatures a non retractable, tailwheel, landing gear and a powerplant made up of reciprocatingengine capable of developing 60 HP, with a 60 inches diameter, two bladed, fixed pitch., tractorpropeller. 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 aresearch avionic system composed by sensors and computers and their relative power supplysubsystem. 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 andsideslip) GPS Receiver and Antenna Linear Potentiometers (Aileron, Elevator, Rudder and Throttle Command) RPM (Hall Effect Gear Tooth Sensor) Outside air temperature SensorA nonlinear mathematical model of the subject aircraft longitudinal dynamics, has been tuned upthrough semi empirical methods, numerical simulations and ground tests.To taking into account the atmospheric turbulence the identification problem addressed in thiswork is solved by using the Filter error method approach .In this case, the mathematical model isgiven by the stochastic equations: 0 0, , ,, ,x t f x t u t w ty t h x t u tz k y k v kx t x (1)where x is the state vector, u is the control input vector, f and h are dimensional general nonlinearvector functions, contains the unknown system parameters, z is the measurement vector ,w is theprocess noise and v(k) is the measurement noise. The presence of nonmeasurable process noiserequires a suitable state estimator to propagate the states. To take into account model nonlinearitiesin the present paper an Extended Kalman Filter has been implemented as the estimation algorithm.

AB - This work addresses the identification of the dynamics of the research aircraft FCRL (FlightControl Research Laboratory) used for the Italian National Research Project PRIN2008 accountingfor atmospheric turbulence.The subject vehicle is an unpressurized 2 seats, 427 kg maximum take of weight aircraft. Itfeatures a non retractable, tailwheel, landing gear and a powerplant made up of reciprocatingengine capable of developing 60 HP, with a 60 inches diameter, two bladed, fixed pitch., tractorpropeller. 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 aresearch avionic system composed by sensors and computers and their relative power supplysubsystem. 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 andsideslip) GPS Receiver and Antenna Linear Potentiometers (Aileron, Elevator, Rudder and Throttle Command) RPM (Hall Effect Gear Tooth Sensor) Outside air temperature SensorA nonlinear mathematical model of the subject aircraft longitudinal dynamics, has been tuned upthrough semi empirical methods, numerical simulations and ground tests.To taking into account the atmospheric turbulence the identification problem addressed in thiswork is solved by using the Filter error method approach .In this case, the mathematical model isgiven by the stochastic equations: 0 0, , ,, ,x t f x t u t w ty t h x t u tz k y k v kx t x (1)where x is the state vector, u is the control input vector, f and h are dimensional general nonlinearvector functions, contains the unknown system parameters, z is the measurement vector ,w is theprocess noise and v(k) is the measurement noise. The presence of nonmeasurable process noiserequires a suitable state estimator to propagate the states. To take into account model nonlinearitiesin the present paper an Extended Kalman Filter has been implemented as the estimation algorithm.

UR - http://hdl.handle.net/10447/95168

M3 - Other

SP - 13

EP - 13

ER -