The Development of Neural Network Based System Identification ...

92 CHAPTER 4 NEURAL NETWORK BASED SYSTEM IDENTIFICATION
signals s = [s ail s aux s ele s rud ] T , can be translated to pilot’s stick positions (Input
δ range = ±1), δ = [δ lon δ lat δ col δ ped ] T , by means of a linear transformation:
δ = G −1 (s − s trim ) (4.11)
where s trim are the servo signals at trim values which indicate the necessary pulse
width values to level the swash plate position and tail pitch cyclic. Matrix G (mixing
gains) has to be determined through the measurement of servo signals for different
stick positions to get the exact relationship between pulse width commands sent to the
servos and the requested control inputs. During the system identification experiments, a
separate micro-controller was used to continuously measure the servo signals. It passed
the values through to the MBS270 over a Universal Asynchronous Receiver/Transmitter
(UART) serial interface. The overall system architecture used in system identification
experiment can be referred in Section 3.5. The resulting values for matrix G and s trim
was found to be:
⎡
⎤
⎡ ⎤
0.049 0.126 −0.239 0
1.387
−0.047 0.127 0.248 0
1.692
G =
; s trim =
⎢ 0.103 0.003 0.242 0 ⎥
⎢1.387⎥
⎣
⎦
⎣ ⎦
0 0 0 −0.256
1.503
(4.12)
The common frequency range for the excitation signal used in rotorcraft system
identification and control is between 0.3 rad/s to 20 rad/s. It is also recommended in
Tischler and Remple [2006] that an identical filter to be used for all output and input
signals with a cut-off frequency 5 times higher than the maximum excitation signal
frequency. Hence to reduce the noise in sensors data, the cut-off frequency of the low
pass filter used in this study was selected at 15 Hz. The sampling rate of the sensors
was selected at 100 Hz which was at least 25 times higher than the maximum excitation
frequency.
The IMU (3DM-GX1) filters the raw sensor outputs on-board, combining the data
from the accelerometers, gyroscopes, and magnetometers. However since the position