DIY flight controller from scratch

Pictures: First hardware version using Arduino Uno board and second hardware version using Arduino Nano board

---------------------------Table of contents----------------------------

1. Introduction

1.1. Purpose

1.2. Progress state

1.3. Videos

2. Required sensors

3. Accro mode explained (aka manual)

3.1. Angle feedback

3.2. Speed stabilization

4. Angle mode explained (aka automatic leveling mode)

4.1. Angles and angular speeds feedback

4.2. Stabilization

5. Hardware configuration

5.1. Components list

5.2. Connexions

6. Software setup

6.1 Using Arduino IDE

6.2 Using PlatformIO

6.3. Using Docker

7. Software detailed design

7.1 Source Code overview

7.2 The state machine

7.3 Radio reception

7.4 Motors speed control

7.5 Failsafe

8. Appendix

8.1 My BenchTest

8.2 Exemple of First Person View configuration(FPV)

10. Glossary

11. Bibliography

Warning

Before starting this project, you must know that spinning propellers are very dangerous for your eyes and people around. When using the benchtest indoor, you must wear large protective glasses, and set up a power limit. For outside tests, choose a very large area, with nobody around.

1. Introduction

1.1 Purpose

The aim of this project is to develop a very simple quadrirotor flight controller from scratch, using an Arduino and inertial sensors. There is two benefits:

• to understand UAV flight stabilization
• to have our own system, with no limits for customization: you can add all the sensors you want.

In this project, the two main flight modes are addressed:

• Accrobatic mode (or manual mode): the simplest to code, but it requires flight skills
• Angle mode: more complex to implement, but easier to fly: UAV automatically goes back to horizontal

I strongly advice to start by implementing the accrobatic mode, since a good accro mode will be a solid fundation for angle mode, and it is easy and fast to implement.

1.2 Progress state

Releases have been successfully tested during flights tests: I have some nice flights in FPV on a 450mm frame, both in accro and angle modes. BUT, this project is sometimes updated, mainly for code format, and unfortunately, I cannot realize flight tests for each submit. So, I cannot garantee that the last commit will allow to flight without some corrections.

Note that you may also have to tune PID according to your configuration.

I advice you to use a large frame (450mm for exemple), because it is more stable, and I did not test the software on smaller frames.

1.3 Videos

Loop control indoor test

Outdoor flight test

2. Required sensors

An IMU (aka MPU) is the only sensor needed for flight.

An « Inertial Measurement Unit » is a MEMS, "Microelectromechanical system", composed by a 3 axis gyroscope sensor, and 3 axis accelerometer sensor.

3. Accro mode (aka manual)

Accrobatic mode implements minimal stabilization algorithms to make flight possible. UAV is not able to auto-leveling, and pilot skills are required.

This mode is the required base for the angle mode, and is easier to realize: do not try to code angle mode if accro mode is not working fine.

3.1 Compute angle speed feedback

Accrobatic mode algorithm only needs UAV angular speeds and angular speed command as entry data. Angular speeds are computed from raw gyroscope data integration.

Exemple of pitch angle speed computation using gyroscopes:

// currentPitch = previousPitch + rawSensorPitch*loopTime
_pos[0] = _pos[0] + (accGyroRaw[0+3]/GyroSensitivity)*_loop_time;

3.2 Speed stabilization

A closed-loop system is needed to control angular speed.

This system compares angular speed command to angular speed feedback, and computes a new motor power to apply.

4. Angle mode (aka automatic leveling mode)

It is a more sophisticated flight mode than the accrobatic mode: now, UAV is able to auto-level.

Auto-leveling mode entry data is UAV attitude: the angles from the horizontal (roll, pitch, yaw) and the angular speeds.

4.1 Compute angles and angular speeds feedback

Both angles speed and angles are required to compute reliable attitude angle feedback along time.

Both gyroscopes and accelerometers are involved.

4.1.1 IMU gyroscopes

As seen previously, Angular speeds are computed from raw gyroscope data integration.

4.1.1 IMU accelerometers

Attitude angles are computed from accelerations:

When UAV is not moving, or if it is moving at constant speed, accelerometers measure gravity, ie vertical acceleration. Angle between UAV and the gravity vector is computed by trigonometry. Be carefull, this measure is faulty when UAV accelerates.

Exemple of pitch attitude angle computation using accelerometers:

_pos[0] = atan(accGyroRaw[1]/accGyroRaw[2]))*(180/PI);

4.1.2 Data merging: the complementary filter

Complementary filter merges gyroscopes and accelerometers data, to get both sensors benefits, and to reduce theirs drawbacks:

• The gyroscope if fast but it drifts along time.

• Accelerometers are not fast enougth, they are noizy, and they are usable only when the UAV is not moving, and when UAV receive only earth acceleration.

The complementary filter mask their respective errors:

• A low-pass filter is applied on accelerometer data to filter the noize and the unwanted accelerations: these data are usefull on a long time period, fast changes have to be eliminated.
• A high-pass filter is applied on the gyrosope data: these data are usefull on short time period, but they drift and accumulate errors on long time periods.

Coding exemple

 _pos[0] = HighPassFilterCoeff*(_pos[0] + (accGyroRaw[0+3]/GyroSensitivity)*_loop_time) +
 (LowPassFilterCoeff)*((atan(accGyroRaw[1]/accGyroRaw[2]))*(180/PI));

Note: LowPassFilterCoeff = 1 - HighPassFilterCoeff

Coefficients computation

HighPassFilterCoeff is computed from the Time constant.

Time constant is a compromise between UAV acceleration filtering, and gyroscopes drift:

• Too low, accelerometer noize are not eliminated
• Too high, gyroscopes drift is not compensated

timeConstant = (HighPassFilterCoeff*dt) / (1-HighPassFilterCoeff)
=> HighPassFilterCoeff = timeConstant / (dt + timeConstant)

Time constant in this project is set to 5 milliseconds. It implies a coefficient "HighPassFilterCoeff" of 0.9995 for a loop time of 2.49 ms.

Note: More efficient filters like the Kalman one are used in UAV, but they are more complicated to understand, and we want a very simple DIY software!

4.2 Stabilization

A double closed-loop system is needed to control attitude angles. It consists of a speed closed-loop [like the one in the accro mode], inside a position closed-loop.

This system compares angle command to angle feedback, and computes a new motor power to apply. Angular feedback is computed using a complementary filter to merge gyroscope dans accelerometer data.

The pilot controls each attitude angle, and if transmitter sticks are centered, command is 0°, and UAV automatically goes back to horizontal.

5. Hardware configuration

5.1 Components list

As exemple, my hardware configuration for a 450mm quad:

5.2 Connexions

TODO: add receiver in schematic

Full view:

Zoomed view:

6. Software setup

6.1 Using Arduino IDE

With minor modifications, project can be build using Arduino IDE:

• rename "main.cpp" to "CodeDroneDIY.ino"
• copy all source files from "CodeDroneDIY/src" to "CodeDroneDIY"
• launch and compile "CodeDroneDIY.ino" using Arduino IDE

6.2. Using Visual Studio Code IDE and PlatformIO

• Install Visual Studio code

• Start VSCode and click on "Extensions" icon

• Install PlatformIO IDE

6.3. Using Linux terminal and PlatformIO

PlatformIO is an open source ecosystem for IoT development.

6.2.1. PlatformIO installation

sudo apt-get install python-pip
sudo pip install --upgrade pip && sudo pip install -U platformio==3.5.2
platformio platform install atmelavr --with-package=framework-arduinoavr
platformio lib install MPU6050
pio lib install "I2Cdevlib-MPU6050"

Optional, for code format:

sudo apt-get install -y clang-format

6.2.2. Build project

platformio run

6.2.3. Flash target

platformio upload --upload-port/ttyACM0

6.2.4. Run unit tests

platformio test -e uno --verbose

6.4. Using Docker and PlatformIO

The development tool "Docker" is a container platoform: it is a stand-alone, executable package of a piece of software that includes everything needed to run it: code, runtime, system tools, system libraries, settings. It isolates software from its surroundings.

• Install Docker
• Move inside docker's folder: cd docker
• Build docker image: make image
• Format code: make format-all
• build project: make build-codedronediy

7. Software detailed design

7.1 Source code overview

StateMachine folder:

Stabilization folder:

customLibs folder:

7.2 The state machine

Due to security reasons, the UAV cannot start running with a flight mode enabled. Five states are defined to enable or disable flight mode safely. These states are ruled by a statemachine.

The five StateMachine states:

7.3. Radio reception

A CPPM receiver is used to fly the UAV using a remote control.

CPPM (Pulse Position Modulation) reception allows to receive all channels using only one entry pin. Each rising edge correpond to the end of the previous channel impulsion, and at the beginning of the next channel impulsion. Elapsed time between two rising edge correspond to the pulse width of a given channel.

In this projet, each pulse width is measured using INT0, and then stored in the correponding channel of an array.

7.4 Motors speed control

Each motor is controlled by an ESC. The microcontroller drives the 4 ESC.

Motor speed is tuned by modifying the pulse width on the ESC signal wire. A pulse must be send at least every 20ms.

• Max motor speed is set by a pulse of 1860ms

• Motor is stopped by a pulse of 1060ms

All speeds between this two values are possible.

Each timer1 interrupt call the function "SetMotorsSpeed", which generate a falling edge on the previous motor and a rising edge on the current motor:

7.5 Failsafe

For security, you must set the failsafe to cut motors power when radio link is lost.

To set the failsafe:

1. Put transmitter sticks in the configuration wanted when reception is lost
2. Bind the receiver with the transmitter

Transmitter configuration used during the « bind » operation defines the « failsafe. »

8. Appendix

8.1 The benchtest

8.2 FPV - First Person View

My cheap configuration:

10. Glossary

ESC Electronic Speed Control

PID It is a Proportionnal Integral Derivative control loop

IMU Inertial Measurement Unit

MPU Motion Processor Unit

FPV First Person View

11. Bibliography

• Arduino

https://www.arduino.cc/en/Reference/Libraries.html

https://www.arduino.cc/en/Reference/HomePage

• PWM

https://librepilot.atlassian.net/wiki/display/LPDOC/PWM,+PWMSync,+Oneshot+Output

http://forum.arduino.cc/index.php?topic=46487.0

• PPM

http://frskytaranis.forumactif.org/t4426-tuto-pwm-cppm-ccpm-ppm-s-bus-s-port-kesako

• PID

http://www.fpv-passion.fr/docteur-pid/

• Quadricopter

https://www.mondrone.net/fabriquer-quadricoptere-la-propulsion/

• Data processing

https://ericjformanteaching.wordpress.com/2013/10/08/smoothing-sensor-input/

• Data merging

http://www.mouser.fr/applications/sensor_solutions_mems/

http://www.pieter-jan.com/node/11

• Programme python

Andy BAKER

http://pythonfiddle.com/andy-baker-quadcopter/

• Vibrations

http://ardupilot.org/plane/docs/common-vibration-damping.html