Controlling two-Wheeled self-balancing robot moving on inclined plane
This project has successfully presented that two
– wheeled self – balancing robot can move on
plane, inclined plane and preset orbit. The robot
can itself balance when it was changed gravity by
force from outside. It also can be controlled from
smartphone or laptop.
However, there are still some shortcomings.
Although the robot can move forward and
backward on inclined plane, it cannot turn left or
turn right on inclined plane. Although robot can
move follow some preset orbit such as square,
triangular orbit with edge which can change by
people or orbit of number eight The orbit can
only observe directly by eye, cannot draw it on
computer.
This is a very challenging problem that should
be studied more in the future research. It is also
necessary to control exactly the position of the
robot so that it can be controlled to move to
narrow places where people cannot move.
Furthermore, the proposed straightening
algorithm is built on a small platform with
affordable and quality material, so that group
students can afford to have a project to apply their
knowledge.
8 trang |
Chia sẻ: huongthu9 | Lượt xem: 554 | Lượt tải: 0
Bạn đang xem nội dung tài liệu Controlling two-Wheeled self-balancing robot moving on inclined plane, để tải tài liệu về máy bạn click vào nút DOWNLOAD ở trên
Tạp chí Phát triển Khoa học và Công nghệ, tập 20, số K3-2017
45
Abstract—This research not only describes the
design and implementation of two - wheeled self -
balancing robot, but also shows how to simulate the
platform in Simulink Matlab using Proportional–
Integral–Derivative (PID) cascade control rule, then
compares it with reality platform. A Kalman filter is
used for state reconstruction in the final
implementation. A cascaded PID control algorithm
was proposed to combine the balancing and
movement. The movement of the robot is controlled
by using a distance controller that use rotary
encoder sensor to measure its traveled distance.
Besides the robot is able to move forward,
backward, turning and reach the desired angle
position by calculating the body's tilt angle. The
experiment shows that the robot is likely to climb up
slope with upon 25 degrees. Last but not least, this
research also shows how to control the robot by
using smartphone and C# form on laptop.
Index Terms— Algorithm, PID cascade, C#.
1 INTRODUCTION
he two-wheeled self-balancing robot has the
characteristics of flexible and simple
structure, working well in a small space,
especially in the poor working conditions and
complexity task places, such as exploration and
dangerous field. Therefore, the robot not only has
become an essential and classical experiment
facility but also is a good platform for researchers
to prove various kinds of control theory and
control method. The research on two wheels
balancing robot is based on inverted pendulum
model. Thus, two wheels balancing robot needs a
good controller to control itself in upright position
without the forces from outside.
Contrary to this existing research, this study
Manuscript Received on March 15th, 2017, Manuscript
Revised on November 01st, 2017.
Nguyen Duc To, Ho Chi Minh City University of
Technology, VNU–HCM, Viet Nam (e-mail:
nguyenducto0123@gmail.com)
aims to make the robot platform with a cheaper
budget, better results and providing feedback with
all the angles and position values in detail for
evaluation and comparison. After that, the
platform can be used in lab rooms at HCM City
University of Technology.
The structure of this paper is as follows:
Section 2, an overview of the proposed method is
presented to explain the whole procedure. This
section also discusses each step of the proposed
algorithm in detail. Design and construction of
experimental models of the robot show in Section
3. Experimental results of the proposed system are
presented in Section 4. Finally, Section 5
concludes the paper with suggestions for future
research.
Fig 1. Block diagram of proposed method
2 PROPOSED METHOD
The block diagram of the proposed algorithm is
shown in Fig. 1. As can be seen, the whole
algorithm is comprised of five steps: (2.1) reading
sensor value; (2.2) using filter to have the exactly
value; (2.3) applying control rule to have the
output to control the robot balancing and moving;
Controlling two-wheeled self-balancing robot
moving on inclined plane
Nguyen Duc To
T
46 Science and Technology Development Journal, vol 20, no.K3- 2017
(2.4) feedback all the results for comparison and
evaluation; (2.5) controlling robot by smartphone
and laptop. Design and construction of
experimental models of the robot show in Section
3. The experimental results in Section 4 will
visualize clearer each step of the proposed
processes achieved in this method.
2.1 Read Sensor
Established the I2C communication to start and
read angle from the MPU6050 sensor.
At the point of the robot’s balancing, the angle
was read and set it into Control Angle.
The Gyroscope bias drifted so it had to be
calibrated. First, when robot was keep standing
still, the gyro values were read and set into
GyroCalib.
After reading and calibrating the angle, the rest
of this part concerns calculating the Angle.
Accelerometer Angle:
arctan 2( / )Roll accY accZ (1)
Gyro Angle:
1k k kgyroAngle gyroAngle gyroRate * dt (2)
The final Angle calculated by using Gyro
Angle subtract to GyroCalib. After calculating the
Gyro and Accelerometer Angle, the filter was
used to combine it to compute the real and exact
angle.
2.2 Filter
2.2.1 Complementary filter:
The complementary filtering is needed because
the accelerometer is accurate in long term not
short term. The gyroscope is just the reverse.
Therefore, it’s necessary to combine and filter the
output of MPU6050 sensor.
The complementary filter is the combined low
pass filter and high pass filter.
This filter combines accelerometer and
gyroscope value to have final angle following this
formula:
1AngleC A* AngleC gyro* dt A * Angle
(3)
T value is the boundaries of trust about gyro and
accelerometer, so the recommend for value of A is
0.98 to have the suitable T.
2.2.2 Kalman filter:
The Kalman filter is an algorithm which uses a
series of measurements observed over time; in this
context, it uses an accelerometer and a gyroscope.
These measurements will contain noise that will
contribute to the error of the measurement. The
Kalman filter will then try to estimate the state of
the system, based on the current and previous
states, that tend to be more precise that than the
measurements alone. It is a good way to deal with
the accelerometer and gyroscope noise.
The Kalman filter includes two main steps:
Time Update and Measurement Update
Fig 2. PID cascade
2.3 Proportional–Integral–Derivative (PID)
Control System
PID control method has been widely used in the
feedback control system. It is a very typical way
of control strategy in industry [1]. PID is
composed of three parts [2]: The proportional part
which is used in the purpose of error elimination,
the integral part is used to average past error, the
derivative part is to predict the further error
through past error variation. The final control
value can be calculated by simply adding these
three terms together.
Three PID controller loops are used to make the
robot stand still and move [3]. The first PID was
used to let the error between Angle and Angle
control down to zero.
Tạp chí Phát triển Khoa học và Công nghệ, tập 20, số K3-2017
47
If the robot still moved forward and backward
in balancing mode, the second PID controller was
used to deal with this problem. It keeps the speed
of robot to reach the speed control. The output of
this speed PID is angle and became the input of
the first PID Angle.
Unfortunately, if the hardware is not perfect,
the speed of two motors was not the same so the
third PID was applied [3]. The output of these
PID controllers will bonus with the first two PID
above to have the final output to motors. The Fig.
2 will show it.
2.4 Feedback for evaluation and comparison
After using PID, all of the PID parameters and
result values display and setup through C form.
Therefore, we can more easily to find the suitable
PID parameter and control the response. The Fig.
3 has shown the method.
Fig 3. Transmit and receive algorithm
2.5 Control robot
The robot was not only controlled by using
form load C# Fig. 4 but also by android app.
When clicked the button, the “character” will be
sent to the microcontroller and the robot will
operate in suitable mode depending on the
character it received.
Transmission and reception of signal between
laptop or smartphone with robot via Bluetooth. So
we can control it in a short distance about 10
meters radius.
Fig 4. Form C# on laptop
3 DESIGN AND CONSTRUCTION OF
EXPERIMENTAL MODELS
3.1 Mathematical model of the robot
θ rad Raw angle
δ rad Yaw angle
MW kg Mass of wheel
MB kg Mass of body
R m Radius of wheel
L m Distance between the center of the
wheels and the robot’s center of
gravity.
D m Distance between the contact
patches of the wheels
g m/s2 Acceleration of gravity
48 Science and Technology Development Journal, vol 20, no.K3- 2017
Equation describes object:
2
2
2
1( cos ) cos sin cos3 3 3 3 cos
1
4 2 4 2 4 4 2
3 sin
4
(3
2
4
W B B
θ
W B W B W BB
W B
W B
sin θM R M L θ θ M L θ θ θ
θ C
M M L M M L M M RLM L
g θ
L
M R M L
M M
2
2
2
cos ) cos cos )3
sin
4
( cos )(1 sin )3 1
( sin
4
W B
B
W B
θ
B
θ θ M R M L θ
X M L θ θ
L L
M R M L θ
C
g
RM L
(4)
Linearizing nonlinear model of two-wheeled self-
balancing robots
With conditions:
2
1[ ]
sin ;sin ;cos
rad
Equation (5) becomes:
4
3
2
2
B
W B
W B
W B W B
C
X gθ Lθ
M L
M M C
M M θ X
M R M L R M R M L
(5)
Solve the system of equations (6) we obtain:
4 4 1
3 3
B
θ
B
θ
B
gM Y
θ θ C
X X
gM LY
X L g θ C
X X M L
(6)
With:
4
3 2
1
2
B
B W B
W B
B
W B
M
X M L M R M L
M M
M
Y
M M R L
3.2 Block diagram of system
The system block diagram is show in Fig. 5.
Functions of the blocks in the diagram:
- Transmit / Receive Universal Asynchronous
Receiver/Transmitter (UART): transmit code,
signal via serial communication protocol with the
8 bits data.
- Acceleration and Gyroscope: implementing
measure the angle and angular velocity of the
robot.
- H – bridge: control velocity and direction of
motor.
- Encoder: read pulse from the encoder and
transmit position, velocity of the robot to the
microcontroller.
- Microcontroller: read the signals from
acceleration, gyroscope, encoder, transmits
signals to the UART, processing the input signal
and provides output signals
Fig 5. Block diagram of system
3.3 Hardware architecture of robot
Hardware includes modules [4, 5]: read encoder
of two DC servo motors, PWM and DIR to
control two DC servo motors, read angle value of
the sensor - MPU6050, communicate with
Bluetooth HC-05, source... to connect peripheral
devices.
Tạp chí Phát triển Khoa học và Công nghệ, tập 20, số K3-2017
49
Fig 6. Schematic
3.4 Mechanical design
In this model, the structure of a self-balancing
robot is expressed. Mechanical parts of the car are
designed by AutoCAD software and simulated 3D
by SketchUp software.
Fig 7. Simulate 3D of robot
Fig 8. Real model
50 Science and Technology Development Journal, vol 20, no.K3- 2017
4 EXPERIMENTAL RESULTS
4.1 Complementary file and Kalman filter
The Fig. 9 and Fig. 10 show the angle output
after Kalman filter. In general, the angle output is
stable and good.
The Fig. 11 shows the Complementary filter is
not as accurate as the Kalman filter once when the
Angle began changing continuously.
Fig 9.Robot doesn’t oscillate
Fig 10. Robot oscillates
Fig 11. Complementary filter and Kalman filter
4.2 PID cascade
The Fig. 12 and Fig. 13 show the response of
robot when we use PID speed and PID Angle.
In the first experiment, it is verified how the
robot behaves given zero target velocity. It is seen
that velocity and tilt of the robot slightly oscillate
in control methods. The robot cannot be
absolutely immobile since its state is never
statically stable.
Fig 12.The angle of robot when it does not move.
Fig 13.The speed of robot does not move
When we use two PID controllers at the same
time the robot will become stable at the balance
point. On one hand, the robot still remains stable
after being pushed. On the other hands, if only use
PID Angle is used, the robot cannot stay stable
almost immediately after being push.
4.3 Robot moving on plane
Fig. 14 and Fig. 15 show the robot’s response
when it was moving forward, backward.
Besides that, it also shows the position of the
robot when it moves. The robot still keeps balance
while it was moving.
Because of three PID controller loops, the robot
will move straight to forward and backward. This
is the different compare with when we are not use
PID Angle rotate. The robot will turn left or right
when it moves for a long distance.
The robot meets installation parameters well.
Travelled distance moves forward steadily. The
robot’s rotation on two wheels always remain
stable (delta Pulse towards 0); on the other hand,
the robot’s angle is stable too.
Fig 14. The angle of robot moves with ± 20 (cm/s)
Fig 15. The speed of robot moves with ± 20 (cm/s)
The robot is able to stabilize itself and managed
to reject disturbances such as gentle pushes.
Fig. 16 and Fig. 17 show the robot’s response
when it was pushed by external factors.
Tạp chí Phát triển Khoa học và Công nghệ, tập 20, số K3-2017
51
Fig 16. The angle of robot when being pushed
Fig 17. The speed of robot when being pushed
Fig. 18 and Fig. 19 show the robot’s response
when it was rotated.
Fig 18. The angle of robot when being rotated
Fig 19. The speed of robot when being rotated.
4.4 Robot moving on inclined plane
The Fig. 20 and Fig. 21 shows that angle of
robot is greater than angle of slope when moving
up slope. The robot tilted back to reduce speed
when moving down slope and when robot
switched from plane to inclined plane or from
inclined plane to plane, it has time of transition.
Fig 20. The angle of robot moves with ± 5 (cm/s)
Fig 21. The speed of robot moves with ± 5 (cm/s)
5 CONCLUSION AND FUTURE WORK
This project has successfully presented that two
– wheeled self – balancing robot can move on
plane, inclined plane and preset orbit. The robot
can itself balance when it was changed gravity by
force from outside. It also can be controlled from
smartphone or laptop.
However, there are still some shortcomings.
Although the robot can move forward and
backward on inclined plane, it cannot turn left or
turn right on inclined plane. Although robot can
move follow some preset orbit such as square,
triangular orbit with edge which can change by
people or orbit of number eightThe orbit can
only observe directly by eye, cannot draw it on
computer.
This is a very challenging problem that should
be studied more in the future research. It is also
necessary to control exactly the position of the
robot so that it can be controlled to move to
narrow places where people cannot move.
Furthermore, the proposed straightening
algorithm is built on a small platform with
affordable and quality material, so that group
students can afford to have a project to apply their
knowledge.
REFERENCES
[1] Nguyen Thi Phuong Ha and Huynh Thai Hoang, Automatic
Control Theory, Vietnam National University, 2005.
[2] Huynh Thai Hoang, Slide Advanced Control Theory,
University of Technology, 2011.
[3] Duong Duy Chien, Two-wheeled self-balancing robot
moving on plane, Electrical & Electronic Engineering,
University of Technology, 2016.
[4] Mai Tuan Dat, Two – wheeled self – balancing robot
moving on plane, Mechatronics Engineering, University
of Technology, 2002.
[5] Nguyen Gia Minh Thao, Two–wheeled self–balancing
robot, Electrical & Electronic Engineering, University of
Technology, 2010.
52 Science and Technology Development Journal, vol 20, no.K3- 2017
Duc To-NGUYEN was born in Quang Ngai
Province, Vietnam in 1994. He received the
degree in Control Engineering and Automation
from Ho Chi Minh City University of
Technology, VNU-HCM in 2017.
From 2012 to 2017, he was a student at Ho Chi
Minh City University of Technology, VNU-HCM.
His research interests include Advanced Control
Theory, Intelligent Control, Control Systems.
Tóm tắt— Nghiên cứu này không những mô tả thiết kế và thực hiện robot hai bánh tự cân, mà còn cho thấy cách
mô phỏng trên Simulink Matlab sử dụng các luật điều kiển PID cascade và so sánh với mô hình thực tế. Bộ lọc
Kalman được sử dụng để lọc nhiễu cho góc nghiêng của robot. Thuật toán điều khiển PID cascade được sử dụng để
kết hợp cân bằng và chuyển động. Sự di chuyển của robot được điều khiển bằng cách sử dụng một bộ điều khiển sử
dụng encoder để đo khoảng cách di chuyển của nó. Bên cạnh đó, robot có thể di chuyển về phía trước, lùi, quay và
đạt được vị trí góc mong muốn bằng cách tính góc nghiêng của robot. Thí nghiệm cho thấy robot có khả năng di
chuyển trên mặt phẳng nghiêng với 250. Cuối cùng nhưng không kém phần quan trọng, nghiên cứu này cũng cho
thấy cách điều khiển robot bằng smartphone và laptop.
Từ khóa— Algorithm, PID cascade, C#.
Điều khiển rô-bốt hai bánh tự cân bằng di
chuyển trên mặt phẳng nghiêng
Nguyễn Đức Tô
Các file đính kèm theo tài liệu này:
- controlling_two_wheeled_self_balancing_robot_moving_on_incli.pdf