Project Report

Electronics & Communication - Final Report

Shailendra Pratap Yadav

Electronics and Communication Department,

JSS Academy of Technical Education,
Noida
Contents
1 Introduction .............................................................................................................................................. 1

1.1 Objective .................................................................................................................................................. 1

1.2 Applications ............................................................................................................................................. 2

2 Design ........................................................................................................................................................ 2

2.1 Overview .................................................................................................................................................. 2

2.2 Battery Control Circuit .............................................................................................................................. 3

2.3 Motors & Electronic Speed Controllers ...................................................................................................... 4

2.4 Transmitter & Receiver ............................................................................................................................. 5

2.5 Internal Measurement Unit ....................................................................................................................... 7

2.6 Flight Controller ....................................................................................................................................... 8

3 Project Evaluation Metric ....................................................................................................................... 10

4 Conclusion ............................................................................................................................................... 10

5 Acknowledgements ................................................................................................................................. 11

1 Introduction
1.1 Objective
The project objective was to build a quadcopter drone that uses an Arduino as its main flight controller. This
build was largely inspired by electronic hobbyist Joop Brokking, whose YouTube videos we often referred to
[1].
We set both primary and secondary objectives, with the secondary objectives existing simply to serve as an
interesting bonus outgrowth of our original project plan. Our primary objectives were as follows:

1. Build a remote-controlled quadcopter

2. Stabilize the flight of the quadcopter using a feedback control loop

Although the primary objectives appear concise, there are many intricate aspects to the building such an
“Auto-leveling” quadcopter. Firstly, the remote-control aspect of the quadcopter requires seamless, lagfree and
stable communication between the transmitter and quadcopter motors for the quadcopter to fly smoothly.
Secondly, due to the inherent unstable nature of quadcopter flight, there is a necessity for some stabilization
mechanism in-built to the quadcopter flight controller. For this, we plan on using a proportionalintegral-

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derivative (PID) feedback control that uses input for an onboard internal measurement unit (IMU) to
communicate with the motors in such a way the quadcopter flight is less unstable, if not completely stable.
If these primary objectives were completed in time, we had modular secondary objectives as add-on features
for the quadcopter, which included:

1. Sonar-based collision avoidance system

2. Voice control system

These objectives are currently based upon ongoing developments in industry standard quadcopters, and
were to be bonus features we could add onto our main quadcopter build to enhance the barebone drone.

1.2 Applications
Autonomous drones technology has an extremely promising future in the evolving transportation, retail and
entertainment markets. The rapid development of deep learning and the relative affordability of
microcontrollers and other single board computing technology has made the development of the consumer
facing drone industry economically feasible. Hardware companies like DJI have been steadily growing their own
consumer/hobbyist product line. However, more importantly is the growth of the business facing drone
industry for both hardware and software development. The promising advancements in neural net processing
and open source AI projects has pushed the boundaries on the concept of possible. For example, companies like
Skydio R1 are pushing forward drones with powerful facial recognition software for video tracking and
photography. While facial recognition and autonomy is a game changer to the entertainment industry with
smart cinema cameras and selfie drones, the true utility for this technology lies in the agriculture and shipping
industries. Autonomous drones present an extremely convenient method for mapping and irrigation for large
crop fields. Any sizeable farm will employ more than a dozen people for the sole purpose of watering and
mapping the crop fields. This work could be given to a drone (or fleet of drones) and save time/energy. The
commercial shipping industry is also investing in drone technology for quicker package delivery (Amazon being
the prominent company). Large tech conglomerates have introduced large capital investments , for example
Intel recently invested 60 million dollars into Yuneec an electrical aviation company specializing in drones.

2 Design
2.1 Overview
The overall project can be split up into 5 main components: developing a battery control circuit that powers our
entire quadcopter, controlling the motors and electronic speed controllers (ESCs), retrieving pulse width
signals from a radio frequency transmitter and receiver, obtaining data from an internal measurement unit
(IMU), and, finally, building a flight controller on an MCU, specifically an ATmega328p (which we work using an
Arduino Uno development board).
The design of the primary objective part of the project is very modular. The secondary objective features can
easily be added to this ‘barebone’ quadcopter; for example, the voice control kit or sonar-based collision system
can be set up with an external MCU, such as a Raspberry Pi or another Arduino, and be synced with the main
Arduino flight controller via the I2C communication bus (corresponding to the SDA and SCL pins), which the
MPU-6050 is currently connected to.

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 Figure 1: Full schematic of the electronics for the quadcopter.

2.2 Battery Control Circuit

Figure 2: Schematic of battery control circuit

The battery control circuit is the heart of the quadcopter. You can reference figure 2 with the Vin, A0 and A2
pins representing Arduino connections pins. The four resistors used are identical precision resistors which are
all within two ohms of each other. This was done to provide the relative voltage outputs to the Arduino’s analog
input pins, A0/A2. The battery circuit code is quite simple. The raw voltage output of the LiPo battery is roughly
13.1V (much higher than its rated 11.2V). This is too high for the on board voltage regulator on the Arduino
UNO (the voltage regulator on the Arduino can handle 12.0V max). To accommodate for this we use the 3 pin
MOLEX header that is used to charge each cell of the battery to draw only 7.4V from 2 cells of the 3 cell battery
pack. The voltage divider is read the Arduino using analog pins A0/A2 and below a given threshold the Arduino

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will flash an on board LED to indicate that the battery is low. This same effect could be achieved with just one
pair of resistors, however, the second pair was added as a precaution in case there is a hardware malfunction
mid flight. This second pair also allows for a more accurate measurement from the Arduino, because the values
read at the analog inputs can be averaged to obtain a more consistent value. Finally, the diode is connected
directly to the battery so as to prevent backflow current from entering the battery.

2.3 Motors & Electronic Speed Controllers

The electronic speed controllers (ESC) and motors are the muscles and limbs of the quadcopter respectively.
We used LHI’s a 2212 920KV Brushless DC Motor (BLDC) and SimonK’s 30A ESC [2, 3].

Figure 3: Right: ESC - middle top MOSFET chip has a visible short on the bottom three pins.
Left: Brushless Motor - Internal magnet rotated by the induced alternating magnetic field from wire loops. ESC
used to output A-B-C pulses at frequency which matches desired speed[4].

BLDC motors work in a primarily different manner to commutator (brushed) DC motors. Instead of having a
rotor shaft rotate by having an alternating current passing through a wire loop in between a permanent magnet,
BLDC motors rely on switching the direction of current passing through electromagnets that surround a central
permanent magnet which acts as the rotor. For the simple case of having 1 electromagnet, the current through
the coil windings of the electromagnet is only alternated with every 180 ◦. Typical BLDC motors nowadays,
however, have 3 or more electromagnets, and so the current is alternated between the different coil windings
(electromagnets) at different phases depending on the number of electromagnets and desired rotor RPM (refer
to Figure 3, left). Due to the lack of friction in them, BLDC motors are more efficient, longer lasting, and faster
in comparison to the older commutator DC motors.
The way we correctly output the necessary multiple phased pulses to the BLDC motor is with an ESC. The ESC
interprets pulse-width-modulation (PWM) signals and converts them into the correct pulse currents to be sent
to the electromagnets within the BLDC. This is done using multiple transistors, which are typically power
MOSFET modules. Therefore, we are able to control the motors of our quadcopter using an Arduino by sending
the calibrated PWM signal to the ESC, which corresponds to the RPM we would like the motor to spin at.
One issue that we have encountered during our project was a faulty ESC shipped from the manufacturer.
There was a short through one of the attached MOSFETs on the ESC’s circuit board. Unfortunately, when

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connecting this to a motor for testing, the motor shorted out and began smoking. We naively believed that the
issue occured with the motor and not the ESC and so we tested the ESC on another motor and the same issue
occurred. At this point we disassembled the ESC and the motors to troubleshoot the issues. The motors were
tested using inductance meters because of the large quantity of copper wire windings associated with brushless
motors. A working motor should read similar inductances around each of the leads, however if there has been
a short then the inductance will vary extremely. After doing this process on each of the motors, we found that
two of the motors had a short circuit. To test the remaining ESCs we checked the connection between the
MOSFET source and drain pins on the circuit board and found that two of our ESC’s contained shorts (refer to
Figure 3, right). We promptly ordered a new set of ESCs and brushless motors and checked the ESCs and motors
before testing.
Combined with knowledge of the specifications of our battery (11.1V, 2800mAh, 35C)[5], we can make an
estimate on the minimum flight time of our quadcopter by assuming it is running at full-speed for the entire
flight:
Battery Capacity × Battery Discharge
T=
Max. Total Amp Draw
T=

2.4 Transmitter & Receiver

2800mAh × 35C
= 49 minutes
30A × 4 motors

(a) Multiple pulses of constant (b) Pulse length for 0% throttle (c) Pulse length for 100% throttle time
and frequency being received corresponds to approximately corresponds to approximately from the radio
transmitter. 1000us. 2000us.

Figure 4: Example output from receiver channel 3, which corresponds to the transmitter’s throttle.

The radio transmitter and receiver are the nervous system of the quadcopter. We use a 6-channel 2.4Ghz radio
controller transmitter and receiver from Flysky [6], although we only need to utilize 4 sticks (i.e 4 channels)
which correspond to the throttle, pitch, yaw and roll movements. The Flysky FS-T6 controller communicates
with the FS-R6B receiver via radio frequency signals. Although the physics behind this communication is
interesting, we will not choose to explain how the transmitter sends these signals in more detail.
Instead, what the receiver outputs as we move the sticks on the transmitter is of utmost importance. As can
be seen in figure 4, the receiver channels output a constant frequency of pulses whose width is modulated

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according to the stick movements - this is exactly the pulse width modulation needed by the ESCs to control the
motors.
This raises the question of what PWM signal we should send to the ESCs - the PWM generated by the Arduino,
or the PWM generated by the receiver? The correct answer is the PWM from the receiver. This is for a few
reasons: firstly, the Arduino PWM function is very slow, and not very stable. This makes it hard to control a
quadcopter whose motor speeds need to be precisely controlled and be responsive to a change in user control,
otherwise it will crash very easily. Secondly, using the receiver PWM signals allows for a quicker interaction
between the pilot and motors.

Figure 5: Complete interrupt sequence code contained in Arduino flight controller sketch.

In order to store the receiver pulse lengths, we use an interrupt sequence, as seen in figure 5. This interrupt
sequence triggers on a state change. For example, let’s look at how the pulse time will be obtained for channel
1, which is connected to the digital input pin 8 on the Arduino. When the channel goes from low to high, the
interrupt sequence is triggered and we go through our control flow statements (note that for the first trigger,
we initialize the last channel variable to be 0). Since the last state of the channel was a low, we collect the current
time. After the interrupt is triggered again when the state falls from high to low, we go through the second
control flow statement, which collects the current time and subtracts it off the previous time we had collected
when our state had risen from low to high. This lets us calculate how long our pulse high state was on for in
microseconds.
We can now use these pulse times to output the corresponding PWM to the ESCs, as seen in figure 6. In this
code, we simply turn each ESC to a high state for a period of time that corresponds to the pulse time recorded

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from the receiver by the interrupt sequence using a loop function that exits when the corresponding pulse time
has elapsed.

Figure 6: ESC output function contained in Arduino flight controller sketch. The timer values are obtained from
the pulse signals from the receiver recorded via the interrupt sequence.

2.5 Internal Measurement Unit

Figure 7: Quadcopter axes corresponding to the roll, pitch and yaw movements [7].

The internal measurement unit (IMU) is the eyes and ears of the quadcopter. The IMU is composed of two key
hardware parts the accelerometer and gyroscope. The IMU essentially measures the current orientation of the
quadcopter as angles from the yaw, roll and pitch motions (see figure 7. This angle calculation is done from the
raw data output of the gyroscope and accelerometer readings. The gyroscope measures the angles as a rate and
represents that rate as a raw integer value (depending on the sensitivity setting of the gyro the max rate in
degrees per second is 2000◦/s. With the full scale gyro range set to ±500◦/s the base rate of one degree per
second rotation will yield a value of 65.5 for each axis. Thus to scale the raw gyro output reading to be in degrees
per second the raw output is divided by 65.5. This angle reading is still insufficient for a quadcopter IMU unit
however, this is due to the initial noise and offset present in the first gyroscope readings. To correct for this the
axis outputs are averaged over 2000 readings and these averages are subtracted from the gyroscope
measurements. To obtain a traveled angle reading from a rate measurement, the rates must be summed
consecutively (analogous to taking an integral of velocity to obtain position). The rates are summed at a sample
rate of 250Hz (one sample every four milliseconds) and multiplied by a factor to give a travelled angle

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measurement. This factor is 1.66 × 10−5s−1 which accounts for the gyros full scale measurement (65.5) and the
sample rate of 250Hz. The last requirement is that the axis of the gyro be coupled together. This means that
rotation involving more than one axis can be measured by the quadcopter. The equation for the coupled axis
are:

Angle Roll Coupled = Angle Roll − Angle Pitch × sinAngle Yaw

Angle Pitch Coupled = Angle Pitch + Angle Roll × sinAngle Yaw

The coupled angle readings for the travelled angle are now usable for a short period of time. Because this is
a real world system there is an drift factor present in the angle measurement from just the gyroscope, thus for
use on a quadcopter the a gyroscope IMU is not useful.
The accelerometer is used to correct for this drift term and provide a starting angle position for the IMU. The
accelerometer works very similarly to the gyroscope, however it measures the acceleration present rather than
the velocity. The measurement comes out as a raw integer value and is converted to a quantity in g’s (1g being
the base force on the surface of the Earth). The vector direction is used to obtain a measured angle on the pitch
and roll axis. The following equations show how the angle is calculated:
q
atotal = a2x + a2y + a2z

ay
anglepitch = α × Angle Roll × arcsin
atotal
anglepitch = −α × Angle Roll
atotal
α = 57.3◦

Note that is a correction term dependent on the sensitivity full scale range of the accelerometer, in our case
we utilized a sensitivity range of +/-8g. While the accelerometer angles work in theory the vibrational
acceleration due to the motors provides too much noise for practical use. The final IMU utilizes a combination
of the gyroscope and accelerometer angle calculations.

2.6 Flight Controller

The flight controller is the brain of the quadcopter. We designed out flight controller on the ATmega328p MCU
using an Arduino development board [8]. The purpose of the flight controller is connecting the pilot’s
movements on the transmitter with the desired movement on the quadcopter. Since the quadcopter is
inherently an unstable system due to vibrations from the motors, wind and other factors, it is necessary to have
some feedback control system that regulates the flight control output to the motors with the desired user input
from the transmitter. For this reason, we utilze a proportional-integral-derivative (PID) controller that is
integrated into our flight controller.

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 Figure 8: Block diagram of PID feedback control within flight controller
The PID loop control mechanism works by considering a proportional, integral and derivative aspect of the
error between the user inputted output set-point and actual output [9]. . Consider the following equation:

Here, e(t) denotes the error, u(t) denotes the output, and Kp, Ki, Kd denote the proportional, integral and
derivative gain constants respectively. It is clear to see why we would need a proportional gain to reduce the
error and reach the user set-point - the larger the error, the larger the output response to counteract this error.
We can see two problems with this kind of error though: firstly, there is a possibility for the output response to
overshoot the set-point, and cause oscillations about this point. We can solve this problem by considering a time
derivative term that reacts to how the error is changing with time. Secondly, for there to be any output response
from the system there must always be an error, causing the final steady-state output to be above or below the
user set-point. To solve this problem we use an integral component which sums the error up over time, adding
up to fix this steady-state error.

Figure 9: Complete PID calculation function contained within Arduino flight controller sketch

For our quadcopter, we required three PID controllers each for 3 movements: pitch, roll and yaw. The full
code method that implements equation 2.6 for these movements is seen in figure 9. You can also refer to figure

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8 to see a block diagram version of how the PID controller is used in our project. We tuned our gain parameters
manually, through flight and testing. Unfortunately, we do not quite have the best gain settings as of yet.

Figure 10: Snippet of Arduino flight controller sketch that calculates the necessary ESC output
The last aspect of the flight controller is to communicate these PID outputs to the ESCs. This can be seen using
the code snippet in figure 10. Here, the outputs of the pitch, yaw and roll PID calculations are either subtracted
or added to the throttle value. To determine whether the value should be added or subtracted, you simply have
to consider what the motor response should be for a given movement. To pitch the nose up, you need the front
two motors to be spinning faster than the rear two motors. To roll right, you need the left two motors spinning
faster than the right two motors. Finally, for yawing right, you need the front right motor and left rear motor
spinning faster than the other two motors. For the opposite movement, the vice versa is true. This completes
the primary objective flight controller.

3 Project Evaluation Metric

(a) Initial project evaluation metric (b) Final project evaluation metric

Figure 12: The two project evaluation metrics are show as pie charts in the figure above.

The original proposed project evaluation metric shown in ,the figure above definitely, had a few flaws. Firstly,
the physical drone build took up too much space on the metric this was corrected in the final metric. Assembling
the drone was much easier than anticipated and should not have constituted nearly half of the evaluation metric.
Also the PID flight stabilization was increased in the final metric, as this was the core of the project. Finally,
when considering the difficulty present in implementing the RF control into the flight controller this category
was chosen to be increased.

4 Conclusion
As stated in the timeline section above, we were overly ambitious for this project and should have focused solely
on our primary objectives rather than considering the secondary objectives. Nevertheless, we learned a lot
about programming the ATmega328p specifically rather than relying on Arduino based functions, coding in

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C/C++, and understanding how ESC and BLDC motors, I2C communication, PID feedback loop control, and,
obviously, quadcopters work. The final setback of ordering the wrong propellers was tough and cost us a lot of
time and testing.
The final result of our project was a quadcopter that could lift off and turn, but not to a full enough range
where it could be considered flyable. Over the first week of summer, we plan to tweak our quadcopter to make
it ‘flyable’, and perhaps begin adding our secondary objective features.

5 Acknowledgements
We would like to thank Professor Christian Schneider for all his guidance and teaching done for this class as it
was greatly appreciated. Lastly, we would also like to thank Anthony Ransford and Jacob Saret for their help,
support and motivation throughout the building of our project.

References
[1] J.M. Brokking. Project YMFC-AL - the arduino auto-level quadcopter, Apr 2017.

[2] LHI 4x 2212 920KV Brushless Motor (CW / CCW) + 4x SIMONK 30A ESC For DJI Phantom. https:
//www.amazon.com/LHI-920KV-Brushless-SIMONK-Phantom/dp/B00XQYTZQ2/.

[3] Lynxmotion. SimonK ESC User Guide. Revision 1.0.

[4] Brushless DC Motors Control - How it Works (Part 1 of 2). https://www.youtube.com/watch?v= ZAY5JInyHXY,
June 2012.

[5] FLOUREON 3S 11.1V 2800mAh 35C Lipo Battery Pack with XT60 Plug for RC Airplane RC Helicopter RC Car
RC Truck RC Boat, RC Hobby. https://www.amazon.com/
FLOUREON-2800mAh-Battery-Airplane-Helicopter/dp/B00RXA02QW.

[6] Flysky. FS-T6 Digital proportional radio control system, Instruction Manual, 2012.

[7] Faisal Zaman. Nothing Beats a Clean Signal (especially for Drones/UAVs). http://www.qualtre.com/
2015/10/nothing-beats-a-clean-signal/, October 2015.

[8] Atmel. ATmega328/P Datasheet Complete, November 2016.

[9] John Bechhoefer. Feedback for physicists: A tutorial essay on control. Rev. Mod. Phys., 77:783–836, Aug 2005.

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