A Proposed Controller for an Autonomous Vehicles Embedded System
MOHAMED I. ABU EL-SEBAH, FATHY A. SYAM, EMAD A. SWEELEM,
MOHAMED M. EL-SOTOUHY
Electronics Research Institute, Cairo,
Joseph Tito St, Huckstep, El Nozha, Cairo Governorate 4473221,
EGYPT
Abstract:- Many research have observable development in the automated vehicle driving field during the last few
decades. This research proposed a simple optimum Intelligent PID (SO PID) controller to simplify the automated
vehicle motion control. Control of an autonomous vehicle’s steering routines plays an essential key role. Several
steering control procedures are proposed that improve automated vehicle performance. The design of secure
embedded control systems must overcome the difficulties associated with designing both computing and control
systems. Also, this research introduces a model of the autonomous car prototype controlled via an Arduino
microcontroller board and the GPS Module to receive the car coordinates. The car moves safely, and autonomously
consequently avoiding the risk of human faults. Several algorithms such as angle and distance calculations to the
waypoint and obstacle detection are combined to control the car movement.
Key-Words:- Automated Vehicle, Simplified Optimum PID, Angle calculation, Distance calculation, Optimum
PID, Simplified PID.
Received: March 22, 2022. Revised: November 25, 2022. Accepted: December 13, 2022. Published: February 7, 2023.
1 Introduction
Excluding human interaction, an autonomous vehicle
can be created that can distinguish its surroundings.
An autonomous vehicle is sometimes called a self-
driving vehicle, or driverless vehicle. Various
sensors, algorithms, and motors are used to control the
vehicle's movement and move from one place to
another without a human driver. The Internet is used
to supply sensors with data on the surrounding
environment and the vehicle's coordinates, speed,
direction, and obstacles that the vehicle may
encounter. Autonomous vehicles have been created to
increase the safety of transportation users. These
vehicles can sense their surrounding environment and
make decisions without external aid to produce an
optimal path to reach a destination. Even though the
concept seems futuristic and, if successfully
implemented, will address many existing
transportation-related problems, caution must be
exercised before putting it into practice, [1].
The autonomous vehicle is defined as an
autonomous robot that combines navigation and
positioning using multiple sensors, as well as a control
algorithm and intelligent decision-making. The
"Intelligent Pioneer" which indicates the autonomous
vehicle's control system design is examined, as well as
path tracking and motion stability for successful
navigation in uncharted territory, [2]. The path-
tracking is translated as a state space with a 2D of
freedom dynamic motion model. Traditional
controllers struggle to ensure performance and
stability for regulating the path error besides a large
variety of parameter variations and external
disturbances. So, a recently created adaptive PID
controller will be employed. The planned system is
primarily intended to prevent accidents and warn
drivers about the recommended speed for safe driving.
The creation of an intelligent vehicle that travels at the
safest speed in dangerous areas and continuously
monitors a variety of vehicle parameters before
sending the information to the base unit is addressed
in, [3]. A few systems perform the counting and
speed measurement using an image processing code
as a moving object detector such as a car. These
systems include vehicle counting systems and video
processing-based vehicle speed measurement. The
Intelligent Transportation System is still in its early
stages of development with this system. Blob
identification and background subtraction using the
Gaussian Mixture Model (GMM) algorithm are the
techniques used in this system, [4], [5]. Numerous
WSEAS TRANSACTIONS on CIRCUITS and SYSTEMS
DOI: 10.37394/23201.2023.22.1
Mohamed I. Abu El-Sebah, Fathy A. Syam,
Emad A. Sweelem, Mohamed M. El-Sotouhy
E-ISSN: 2224-266X
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Volume 22, 2023
studies, [6], [7], [8], [9], [10], [11], [12], [13]
presented vehicle tracking systems, which use a GPS
module and a GSM module to locate a vehicle and
provide a variety of control capabilities.
A robust controller design using a parameter space
approach to control the autonomous vehicle is studied.
This approach takes into consideration variables
vehicle characteristics variables such as vehicle mass,
vehicle speed, and road-tire friction coefficient. The
created multi-objective robust PID controller
simultaneously satisfies the constraints on D-stability,
phase margin, and mixed sensitivity. The model
predictive control-based autonomous vehicle path is
Investigated, [14], [15]. For autonomous vehicle
route following, numerous different steering control
techniques have been proposed by researchers.
However, a variety of real-world issues, including
model uncertainty, outside disturbances, and steering
system time delays, might impair the path following
the performance. In this dissertation, a methodical
way to resolve these issues is suggested. First, the
parameter space method-based robust PID controller
is investigated. It takes into account differences in the
vehicle's mass, speed, and road-tire friction
coefficient. Two free PID parameters are chosen as
free design parameters, and an uncertainty box is
constructed to depict parameter changes. The
predicted vehicle motion in the future has been
anticipated using communication with the internal and
external data gathered by the onboard sensors. The
danger of a collision and the automatic drive mode are
calculated with precisely predicted movements of a
distant vehicle, [16], [17], [18].
Many researchers pay attention to modifying
the PID controller to improve the controller response.
A few researchers try to simplify the PID designing
techniques, [19], [20], [21], [22].
This article presents a proposed autonomous vehicle
PID controller called Simplified Optimum PID (SO
PID) controller, this vehicle can sense its environment
and make decisions without any external aid to
produce an optimal route to reach a destination. A
model of the autonomous car prototype controlled by
the Arduino microcontroller board and the GPS
Module to receive the car coordinates.
2 Embedded System Design and
implementation
The embedded system (as illustrated in Fig.1) is quite
a complex expression. Simply it is a gathering of both
software hardware and to perform as a component of a
larger system. The hardware of the embedded systems
is customized to implement the required application as
Computers on chips are embedded to control
electronics and achieve the product's functionality.
Embedded systems become an incentive for change in
computing processes, data communications,
telecommunications, industrial control, and
entertainment area. Modern innovative applications in
this area such as home networking and car information
will roll out in the near future.
The microcontroller-based control system is created
and implemented to execute a function or multiple
functions and is not provide the capability to be
programmed by the user. Generally, the users do not
allow replacing a different code on the embedded
system devices. The embedded systems are
constructed to perform a specific function
accomplished with several alternatives and various
choices.
Fig. 1: Block Diagram of the System.
Due to the implanted intelligence code, a vehicle can
operate without human involvement. The Global
Positional System (GPS), which uses satellites to
transmit positioning information, proves to be an
incredibly useful tool for this purpose. Wide area
DGPS offers a reliable technology that deals with
satellite clock errors and selective availability errors
with ease to maintain higher accuracy.
3 System Modeling
The dc motor armature control model used for
controlling the car position is illustrated in Fig.2.
Many models can be used to represent the Dc motor,
[23]. Finally, the motor model can be simplified as in
equation (1).
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DOI: 10.37394/23201.2023.22.1
Mohamed I. Abu El-Sebah, Fathy A. Syam,
Emad A. Sweelem, Mohamed M. El-Sotouhy
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Fig. 2: DC Motor armature control Model
(1)
3.1 A Proposed Simplified Optimum (SO)
Controller
The simple Optimum SO PID design formula
proposed is based on the process transfer function to
determine the optimum PID controller coefficient.
Figure 3 illustrates a general 2nd order system with a
controller deduced based on the optimum response
depending on the process transfer function as the
following equations, [24], [25].
Fig. 3: PID controller in the closed-loop system
The process transfer function
(2)
(3)
(4)
Substituting
(5)
(6)
(7)
(8)
Equaling coefficient of equation (8) with its
corresponding equation (9).
(9)
The controller constant
(10)
(11)
(12)
Where T is chosen as a control program sampling
time or multiple of the control program sampling
time. Applying the above PID Controller design
technique with the following system parameters
illustrated in Table (1) results in the following
controller constants. The parameters for the electrical
motor were taken from the datasheet. For a system
model of a higher order than second order, use only
the second order terms. The dc motor position control
can be simplified as illustrated in the following block
diagram (as shown in Fig.4. By substituting from
Table (1) into equation (1) results from the following
equation. Also, Fig. 5 shows the control system using
a traditional fuzzy controller.
(13)
(14)
Table 1. The System Parameters
Parameter
Values
Armature Resistance ()
2.06
Armature Inductance (mH)
0.238
EMF Constant (mNm/rad/sec)
23.5
Car and motor Inertia (m Nm/A)
10.6
Car and motor friction
Coefficient (mNm/rad/sec)
1.06
Sampling Time
0.0001
Fig. 4: Proposed SO PID controller in the Speed loop
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Mohamed I. Abu El-Sebah, Fathy A. Syam,
Emad A. Sweelem, Mohamed M. El-Sotouhy
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Fig. 5: Proposed Fuzzy controller in Speed loop
The response can be accelerated by using T equal half
sampling time.
Fig. 6 shows the response of the proposed SO PID
controller against a traditional fuzzy controller in
individual tow cases. Figure 6 (a) is utilizing a fast
fuzzy controller while Fig. 6 (b) uses a slow fuzzy
controller. The comparison between the SO PID and
fuzzy controller leads to using the SO PID controller
to avoid overshoot and slow response
Fig. 6: The system response under SO PID and fast
and slow Fuzzy controller
Fig-7: Proposed SO PID position Controller Motor
The position loop illustrated in Fig. 7 is used to
design SO PID controller based on the 2nd order
transfer function and neglecting the higher order
Laplace operator which leads to applying a PD
Controller for the resultant transfer function of
equation (15)
(15)
In the case of 2nd order system, the coefficient of
the S2 should be normalized, and apply the previous
method to the 2nd order equation
The performance of controlled output is shown in
Fig.8.
Fig. 8: PID controller performance
The designed position and speed controllers are
inserted in the speed and position loops. The inner
(a)
(b)
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Mohamed I. Abu El-Sebah, Fathy A. Syam,
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loop controller is modified with a slow execution time
(at least 10 sampling time). Figure 9 shows the
SimuLink block diagram while Fig. 12 illustrates the
system speed and position response.
Fig. 9: Position and speed loop of the car motor
Fig. 10: The motor response under SO PID
3.2 Determines Position utilizes GPS Model
The GPS utilizes a trilateration analytical technique to
perform the positioning algorithm. The position is
determined using the measured distance from
satellites and user position as illustrated in Fig.11
through four satellites used to locate the position of
the receiver on the earth’s surface. Three of these
four satellites are used to track the position while the
4th satellite confirms the target position for each of
those space vehicles. GPS is composed of satellites,
control stations, monitor stations, and receivers. The
GPS receiver collects the information from the
satellites and uses the triangulation technique to
determine the user position, [26], [27], [28].
Fig. 11: Positioning of the User.
A GPS receiver must be locked on to the signal of
at least three satellites in order to calculate a 2D
position (latitude and longitude) and track movement.
When four or more satellites are visible, the receiver
can determine your three-dimensional position
(latitude, longitude, and altitude). A GPS receiver will
generally track 8 or more satellites, but this depends
on the time of day and where you are on the planet.
GPS satellites orbit the Earth every twelve
hours in a precise orbit. Each satellite transmits a
unique signal and orbital parameters that allow GPS to
calculate the precise location of the satellite via the
decoding process. GPS receiver devices use this data
and trilateration technique to allocate a user position.
Essentially, the GPS receiver measures the distance to
each satellite using the period between the transmitted
signal and receiving it. The receiver can calculate a
user position based on the distance measurements
from a few more satellites and display it.
There are numerous error sources that can degrade
the accuracy of positions computed by a GPS
receiver. The time it takes for GPS satellite signals to
travel between each other can be affected by
atmospheric conditions. A GPS signal is refracted as it
travels through the ionosphere and troposphere,
resulting in differences in the speed of the signal and
the speed of a GPS signal in space. Another source of
error is noise; additionally, signal distortion causes
electrical interference or errors in the GPS receiver
itself. The information about satellite orbits will also
cause errors in determining position because the
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DOI: 10.37394/23201.2023.22.1
Mohamed I. Abu El-Sebah, Fathy A. Syam,
Emad A. Sweelem, Mohamed M. El-Sotouhy
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Volume 22, 2023
satellites are not where the GPS receiver "thought"
they were based on the information it received when
determining position. Small variations in the satellite's
atomic clocks cause large position errors. When
signals transmitted from satellites bounce off a
reflective surface before reaching the receiver
antenna, the receiver receives the signal in a straight-
line pathway, similar to a delayed path.
3.3 Control Algorithm
The challenge of determining a car's position in
relation to its surroundings via sensor readings is
known as position localization in Fig. 12. Successful
autonomous robot systems must be able to localize
their positions, which is referred to as the most
fundamental difficulty of equipping a mobile robot
with autonomous capabilities. The robot needs to
keep a precise understanding of its position and
orientation in order to perform autonomous
navigation. The ability of a robot to precisely
determine its position and orientation is necessary for
the completion of all other navigational tasks. For
position localization, this system makes use of GPS
and inertial-specific sensors. By accurately timing the
signals supplied by GPS satellites located far above
the Earth, a GPS receiver determines its location.
Every satellite sends out messages on a regular basis
that include the time the message was sent and the
satellite's position at that moment. The receiver uses
the information message it has received to calculate
the speed of light to calculate the distance to each
satellite and the duration of each communication. A
sphere is defined by each of these distances and the
positions of the satellites. The coordinates of the
receiver on the surface of each of these spheres are
utilized to determine the receiver's location and
position using the navigation equations.
When the orientation of the car on an inclined
plane with the waypoint the car moving to it will start
to calculate the angle needed for the car to rotate with
the following equation:
(16)
Then it will calculate the distance required to move
the car by the following equation:
(17)
Where;
ᴪ
: angle needed for the car to rotate
d: distance required for the car to move
: longitude of the car
1: longitude of the waypoint
: latitude of the car
1: latitude of the waypoint
Fig. 12: Flow Chart of Control System.
4 Experimental Work
The car illustrated in Fig.13 started moving from the
location that it is at where the GPS reads the
coordinates of this location first thing in the code.
Then, it takes the input which is the desired waypoint,
and works out the equations (3) and (4), to know the
distance between the current position and the intended
waypoint, [29], [30], [31]. This area was chosen for a
lot of short buildings area rather than the tall buildings
that would affect the receiving information of the
GPS.
The main problem that we faced is that the servo
needed so much power to work properly without
glitches and while carrying the weight of the car.
Many types of batteries were used, but they did not
work as they drew a very high current and all the
batteries used supplied a low current. A 1.5 kg battery
that uses 7.5 V and generates 3A current which is
Arrived
at end
YES
Turn on Buzzer
for Patient to Place
Finger
Measurement Data
and Storing
Start
Robot Turns
Ultrasonic Sensor
On
NO
NO
YES
Any
Object
Continue
Movement
Change Direction
YES
NO
Another
Patient?
Return to Base
STOP
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Mohamed I. Abu El-Sebah, Fathy A. Syam,
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more than enough to power the servo which that car is
heavy and works very efficiently without any glitches.
Ultrasonic sensors enable the car to virtual
imagination and recognize obstacles and measure the
separation distance. The ultrasonic transducer
generates waves continuously from the transmitter
part of the sensor head. The information about the
obstacle is passed to the microcontroller, which
controls the car's movement direction.
Fig. 13: Implementation of the System Components.
As the code is executed and the car starts to move
forward, the GPS will continue giving out readings of
the new current location coordinates as it is changing
results from the movement of the car. All the
movement and coordinates are illustrated on the PC
monitor through Bluetooth connected to the
microcontroller. It is decided that the motor should
stop immediately, and all the components would shut
down. This means that there is no error that the car
could move by mistake or anything. Only the serial
monitor will notify the message that the vehicle has
reached the desired waypoint.
5 Work Results
The presence of buildings as an obstacle to receiving
signals from satellites leads to weak signals received,
which causes instability in the readings of longitude
and latitude antenna numbers, as it was not perfectly
connected due to the weak signal of the antenna with
satellites, [32], [33]. Therefore, GPS readings are
inaccurate and change every second when working
around tall buildings, so it is recommended that the
work area is at a sufficient distance from the
buildings.
From a set of points to another point. Fig.12
shows a comparative plot of actual and measured
distances. The measured path is represented by a red
path while the blue path is the actual one. The figure
shows the accuracy of the controller for adjusting the
path of the car is more than 90%.
Fig. 14: Comparison between reference path and
actual path
Fig. 14 shows four assumption paths of the car
movement, where the actual path was compared with
the reference values in the car path experimental
model. The speed loop and position loop are
performed utilizing the SO PID designed in
simulation work. It is noticed in all the assumed paths
that there is a small amount of error because of using
the proposed control model. In all paths, the path in
the practical model is higher than the actual path Due
to the delayed response time of the controller. In the
first figure (a), a path was assumed in the form of a
straight line, while in the other paths, a zigzag path
was considered either in the form of a straight line as
in the second figure (b) or in the form of a curve as in
the two paths (c), (d). An increase in the error is
observed due to the curvature of the path by a small
amount, which indicates the quick response of the
controller in adjusting the car path.
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Mohamed I. Abu El-Sebah, Fathy A. Syam,
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6 Conclusion
A proposed simple optimum PID (SO PID) Controller
is derived and applied to the autonomous car system.
This proposed PID Controller calculates the controller
constant by inspection through a simplified technique.
The proposed controller performance actually has an
ideal response in a transient (peak overshoot and rise
time) and steady state. The human error problem in
driving a car to specified position coordinates is
almost eliminated by developing a car that can move
autonomously from any location position to any given
location coordinates. Also, it can avoid obstacles that
come in its path without colliding with them. This
research was successfully implemented and developed
the outdoor tracking location unit using GPS. As a
result, location latitude, location longitude, and the
short distance between two different points on the
earth are measured with an average accuracy of more
than 90% of the actual value.
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DOI: 10.37394/23201.2023.22.1
Mohamed I. Abu El-Sebah, Fathy A. Syam,
Emad A. Sweelem, Mohamed M. El-Sotouhy
E-ISSN: 2224-266X
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed in the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare
that are relevant to the content of this article.
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Creative Commons Attribution License 4.0
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