A Flight Control System for
Autonomous Helicopter
People
Group Members:
Jacky, SHEN Jie
Frank, WANG Tao
Marl, MA Mo
Supervisors:
Prof. QIU Li
Prof. LI Zexiang
Presentation Flow
Introduction
Controlling a Model Helicopter
Attitude Estimation
Hardware and Software
Results and Further Work
Introduction
What is an
Autonomous
Helicopter??
Introduction
What is Autonomous Helicopter?
An Autonomous Helicopter is a
helicopter who is fully or semi

controlled by on

board intelligence and
computing power.
Pictures are from CMU: http://www

2.cs.cmu.edu/afs/cs/project/chopper
What could an
Autonomous Helicopter do?
Fly to a designated area on a
prescribed path while avoiding
obstacles.
Search and locate object of interest in
the designated area.
Visually lock on to and track or, if
necessary, pursue the objects.
Send back images to a ground station
while tracking the objects.
What could an
Autonomous Helicopter do?
Pictures are from CMU: http://www

2.cs.cmu.edu/afs/cs/project/chopper/www/goals.html
Objectives and Goal
Step 1: On

board electronic system
development
Step 2: Data collection from
human controlled flights
Step 3: Algorithm simulations in PC
Step 4: On

board real

time algorithm
implementation and testing
Goal: Achieving a hover flight
Controlling a Helicopter

Dynamic Model
From MIT: http://gewurtz.lids.mit.edu/index.htm
Controlling a Helicopter

Dynamic Model
u, v, w, the velocity in x, y, z axis
p, q, r, the angle velocity in 3 axis
Θ
, pitch,
Φ
, yaw
From MIT: http://gewurtz.lids.mit.edu/index.htm
Controller Result
Successfully achieved a hover flight
Flied forward, backward and sideward
Flied on a prefixed path
Flight Controller Demonstration
A small demonstration
of autonomous
helicopter controller
Attitude Estimation
Information needed by flight
controller
The parameter we need to estimate:
Body orientation: pitch, roll, yaw
Body linear velocity vector: Vb (u, v, w)
Body angular velocity vector: Wb (p, q, r)
NED position : x, y, z
The complementary property of attitude
estimation by gyro and gravity vector
Angular
velocity
propagation
Gravity vector
tracking
Short term
accuracy
High
Low
Long term
accuracy
Diverging
Stable
The complementary property of body
velocity estimation by GPS and body
acceleration integration
GPS velocity
Acceleration
integration
Advantage
Good accuracy
Error not
accumulating
Low delay(<0.1s)
High bandwidth
(up to 100 Hz)
Disadvantage
High delay(>0.4s)
Low bandwidth
(5 Hz)
Long term
diverging
What is Kalman fitler?
The Kalman filter is a multiple

input,
multiple

output digital filter that can
optimally estimate, in real time, the states
of a system based on its noisy outputs.
The Kalman filter estimates a process by
using a form of feedback control: the filter
estimates the process state at some time
and then obtains feedback in the form of
(noisy) measurements.
The Kalman filter
x: actual state vector
z: measurement vector
w: process variance
v: measurement variance
u: control input
The Kalman filter
Gyro
noise
variance
Can be
calculated
from gyro
reading
orientation state:
Pitch, roll, yaw
1
1
1
k
k
k
k
w
Bu
Ax
x
Pitch, Roll, Yaw
Calculated from
Accelerometer
ASSUMMING the helicopter do
NOT has any body linear
acceleration.
Variance introduced
by the resulting
error of the
“acceleration free”
assumption
k
k
k
v
Hx
z
The helicopter has several strong
vibration sources
Main rotor at
29 Hz
Structural vibration at 10 Hz
Low

pass filtering
Fortunately vibration noises and
helicopter dynamics are not in the
same frequency, we can low

pass the
data to eliminate the noise.
Hardware dumper :
cutoff frequency 7

9Hz
FIR filter:
cutoff frequency 5Hz
Filter result
The result of our filter is satisfying
For example, The remaining noise is
+

0.1 m/s^2 in x axis acc sensor and
around +

1 degree/s in y axis gyro
reading. The noise will be further
eliminated in the Kalman filter and
integration operation.
Sensor Offset Effect
GPS Antenna
IMU Sensor
C.G.
IMU Offset Compensation
The IMU offset vector is
The accelerometer reading follows:
The largest error is introduced by the
term
T
z
y
x
IMU
R
R
R
R
]
,
,
[
IMU
R
IMU
IMU
cg
m
R
R
a
a
IMU Offset Compensation
The IMU offset compensation is
g
R
R
a
a
t
w
t
v
t
u
body
local
IMU
measure
linear
0
0
GPS antenna offset
compensation
The GPS offset vector is
The GPS offset compensation equation is
T
GZ
GY
GX
GPS
r
r
r
R
GPS
local
body
antenna
rotorhead
R
R
z
y
x
z
y
x
GPS
offset
with
body
local
g
c
R
r
q
p
w
v
u
R
w
v
u
_
.
Sensor offset compensation
effects
Kalman filter result
(attitude and velocity estimation)
Measure attitude from acc gps
and compass
Background:
In strap down inertia navigation filter, the
attitude information should be continuously
measured from the accelerometer, GPS, and
magnetic sensor.
In static situation the only acceleration
accelerometer sensed is the gravitational
force, the pitch and roll in the Euler angles
can be measured by the following method:
cos
cos
sin
cos
cos
sin
sin
sin
sin
cos
sin
cos
sin
cos
cos
cos
sin
sin
sin
cos
sin
sin
sin
cos
sin
cos
sin
cos
cos
z
y
x
R
R
R
R
'
0
0
g
g
n
gravitatio
'
'
cos
cos
sin
cos
sin
z
y
x
n
gravitatio
ter
accelerome
a
a
a
g
g
g
g
R
a
)
/
(
tan
)
/
(
sin
1
1
z
y
x
a
a
g
a
However, when in dynamic environment the
accelerometer sensed not only the static
gravitational force but also linear
acceleration which can be obtained from
derivative of GPS ground velocity reading.
Because the first and the second part of are
no longer zero so the first two column of will
make the and no longer easy to solve, thus
a good method should be developed to solve
this problem.
Introduction
of the
Hardware System
Hardware System
Electrical System

GPS

IMU

Compass
Mechanical Damper
Overall Electrical System
GPS
Main Feature of GPS
5 Hz Position Velocity and
Time
(PVT) output
Robust Signal Tracking
Satellite Based
Augmentation System
IMU
Main Feature of IMU
96 Hz Sampling Rate
MEMS Technology
Digital Outputs
+/

2g Acceleration
Measurement Range
User

configurable FIR Filters
Compass
Main Feature of Compass
1
°
Heading Accuracy,
0.1
°
Resolution
15Hz Response Time
UART/SPI Interface
Mechanical Dampers
Main Feature of Dampers
7

9 Hz Cutoff Frequency
11 Hz in horizontal plane
13 Hz in the vertical
direction
Communication
Between Devices
Overall Block Diagram
SPI Communication
ARM & Microprocessors
SPI Communication
SD Card & Microprocessor
UART
Microprocessor & Servo Motor
Result
Achievements
&
Further
Development
Achievements
PD Controller successfully
implemented
Attitude estimation
Hover Flight
Estimation Result
Further Development
Maneuver Flight Possibility
Vision Tracking
Q & A
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