# A Flight Control System for

Electronics - Devices

Nov 15, 2013 (4 years and 7 months ago)

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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

Good accuracy

Error not
accumulating

Low delay(<0.1s)

High bandwidth

(up to 100 Hz)

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

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 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
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
°

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