Integral Backstepping Approach for Mobile Robot Control

TELKOMNIKA, Vol.15, No.3, September 2017, pp. 1173~1180
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v15i3.5667 ◼ 1173
Received January 27, 2017; Revised July 2, 2017; Accepted July 30, 2017
Integral Backstepping Approach for
Mobile Robot Control
Bouzgou kamel*1,2
, Ibari benaoumeur1,3
, Benchikh laredj 2
, Ahmed-foitih Zoubir1
1,2,4Laboratoire d’Electronique de Puissance, d’Energie Solaire et d’Automatiqu,
Université des sciences et de la technologie d’Oran Mohamed Boudiaf,
El Mnaouar, BP 1505, Bir El Djir 31000 Oran, Algérie.
1,3Laboratoire Informatique, Biologie Intégrative et Systè mes Complexes (IBISC),
Université d’E´ vry-Val-d’Essonne (UEVE),
40 rue d Pelvoux CE 1433 Courcouronnes, 91020, Evry Cedex, France
2Département d’Délectrotechnique, Université de mustapha stambouli, Mascara, Algérie
*corresponding author, e-mail: bouzgoukamel@hotmail.fr
Abstract
This paper presents the trajectory tracking problem of a unicycle-type mobile robots. A robust
output tracking controller for nonlinear systems in the presence of disturbances is proposed, the approach
is based on the combination of integral action and Backstepping technique to compensate for the dynamic
disturbances. For desired trajectory, the values of the linear and angular velocities of the robot are
assured by the kinematic controller. The control law guarantees stability of the robot by using
the lyapunov theorem. The simulation and experimental results are presented to verify the designed
trajectory tracking control.
Keywords: Robot Mobile, Backstepping, Trajectory tracking, nonlinear systems
Copyright © 2017 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
In the recent years, the mobile robots have been used and exploited in dangerous and
difficult works. They can also be found in: industry, science, education, medical, domestic
machines, entertainment and military applications [1].They are used in a range of
applications [2, 3]. Many research articles have been proposed for the trajectory tracking
problem in the literature. Studies and approaches have been developed in this field [4, 5]. In
order to design control laws for mobile robots, many models with different methods have been
proposed and has been studied by researchers. In [6], an Adaptive PID control law is
developed for the control of mobile robot in order to follow a path planning. In [7], a controller
based on distance measures using the sonar sensors is proposed to follow a wall, and to
command the robot in the workspace while avoiding obstacles this algorithm is tested in
the real implementation cases.
Many proposed methods in literature are based on the kinematics design, but in
the real experimental, the dynamic models are must be used and included to perform at high
speeds and heavy work. Thus, some controllers based on dynamic modeling have been
proposed. As an example in [8], a fuzzy Logic Controller is proposed for tacking of mobile robot
type unicycle using Mamdani model and backstepping technique, the dynamics parameters are
included for the development of the control approach with the Lagrangian Method. Moreover,
no experimental results were reported.
In [9], a controller fuzzy logic is proposed to control the system with unknown dynamics
variables for the development of the design control, this approach is used on mobile robot with
PIC 16f877 microcontroller, but the real implementation necessary to use a computer with high
configuration processor and acquisition cards. An adaptive approach for trajectory applied to
the model dynamic equation of mobile robot type unicycle is designed in [10], a stability
analysis using Lyapunov theory is analyzed to ensure the robot stability, the parameters are
updated online where the parameters of the system are not known or change the task, this
developed method control is tested with mobile robot type Pioneer 3-DX. Fuzzy adaptive
trajectory tracking control of mobile robot type nonholonomic is developed in [11], the objective

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TELKOMNIKA Vol. 15, No. 3, September 2017 : 1173 -1180
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of this method is to eliminate the perturbations in the dynamic and kinematic equations,
the designed approach is tested only with simulation results and no experimental results
were presented.
The robust controller is proposed in [12], to resolve the tracking problems and to
compensate the unknown nonlinear dynamics of the robot. The control structure is based on
the combination of the neural adaptation technique and the sliding mode control, real
implementation is presented. In [13], the Backstepping approach is introduced and applied on
robot type Arduino, the equations control laws developed for motors robot are studied and
tested with real implementation, the actual and reference linear and angular velocities are
calculated by the kinematics structure control, and for the stability stady, lyapunov theory is
used to guarantee the stability of the system.
Some of parameters systems are not modeled or are prone to change in time, with is
often impossible for the application. In this study, robust PI/Backstepping contro is applied to
compensate for the dynamic disturbances, and its stability is analyzed using the Lyapunov
theory, the objective is to ameliorate the Backstopping approach by adding nonlinear PI action
to control law equation. Proportional integral action control is added to reduce the error of the
trajectory tracking. A comparative study between the proposed approach and the backstepping
control is presented by simulations to show the performance of the control structure for stability.
The remainin part of the paper is presented as follows, first we gives the dynamic
model of unicycle type robot. Section 2 gives the dynamic unicycle-like robot model.
Section 3, presents and explains the proposed PI or backstepping controller design.
Respectively, in section 4, The findings of the paper, focusing on the designed trajectory
tracking control method. Finally, in section 5 we conclude this paper.
2. Robot Model
In this section, the model of the mobile robot type unicycle proposed by La Cruz and
Carelli in [14] is considered, the figure 1 show the mobile robot, where G is mass center, h is
the targeted point used. ω is angular velocity. ψ is the angle of rotation, a, b, c and d
are distances.
Figure 1. Mobile robot type unicycle

TELKOMNIKA ISSN: 1693-6930 ◼
Integral Backstepping Approach for Mobile Robot Control (Bouzgou Kamel)
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Forces and Moments equations for the system are written as [15]:
∑𝐹 𝑥′ = 𝑚 (𝑢
˙
− 𝑢̅𝜔) = 𝐹 𝑟𝑙𝑥′ + 𝐹 𝑟𝑟𝑥′ + 𝐹 𝑐𝑥′ , ∑𝐹 𝑦′ = 𝑚(𝑢
¯
˙
+ 𝑢𝜔) = 𝐹 𝑟𝑙𝑦′ + 𝐹 𝑟𝑟𝑦′ + 𝐹 𝑐𝑦′
∑𝑀𝑧 = 𝐼𝑧 𝜔
˙
(1)
and 𝐹𝑟𝑟𝑥, 𝐹𝑟𝑟𝑦, 𝐹 𝑟𝑙𝑥′, 𝐹 𝑟𝑙𝑦′, 𝐹 𝑐𝑥′ and 𝐹𝑐𝑦 are the longitudinal and lateral tire forces applied
the wheels the robot. The moment of inertia at mass center is Iz. The first part of model is
written in [14] as :
𝑥
˙
= 𝑢𝑐𝑜𝑠𝜓 − 𝑢
¯
𝑠𝑖𝑛𝜓 − (𝑎 − 𝑏)𝜔𝑠𝑖𝑛𝜓 , 𝑦
˙
= 𝑢𝑠𝑖𝑛𝜓 − 𝑢
¯
𝑐𝑜𝑠𝜓 + (𝑎 + 𝑏)𝜔𝑐𝑜𝑠𝜓 (2)
where 𝑢
¯
is the lateral velocity of the mass center. The final model of mobile robot type unicycle
is given in [14] by De La Cruz and Carelli as:
(
𝑥̇
𝑦̇
𝜓̇
𝑢̇
𝜔̇ )
=
(
𝑢cos (𝜓)−𝑎𝑤sin (𝜓)
usin(𝜓)+awcos(𝜓)
𝜔
𝜆3
𝜆1
𝜔2−
𝜆4
𝜆1
𝑢
−
𝜆5
𝜆2
𝑢𝜔−
𝜆6
𝜆2
𝜔 )
+
(
0 0
0 0
0 0
1
𝜆1
0
1
𝜆2
0
)
( 𝑢 𝑟𝑒𝑓
𝜔 𝑟𝑒𝑓
) (3)
and the parameters of the system are defined in [14] as:
𝜆1 =
1
2
2𝑟𝑘 𝐷𝑇+
𝑅 𝑎
𝑘 𝑎
(2𝐼 𝑒+𝑚𝑅 𝑡 𝑟)
(𝑟𝑘 𝑃𝑇)
, 𝜆2 =
1
2
2𝑟𝑑𝑘 𝐷𝑅+
𝑅 𝑎
𝑘 𝑎
(2𝑅 𝑡 𝑚𝑏2+𝑟(𝐼 𝑧)+𝐼 𝑒 𝑑2)
(𝑟𝑑𝑘 𝑃𝑅)
, 𝜆3 =
𝑅 𝑎
𝑘 𝑎
𝑚𝑏𝑅 𝑡
2𝑘 𝑃𝑇
𝜆4 =
𝑅 𝑎
𝑘 𝑎
(
𝑘 𝑎 𝑘 𝑏
𝑅 𝑎
+𝐵 𝑒)
(𝑟𝑘 𝑃𝑇)
+ 1 , 𝜆5 =
𝑅 𝑎
𝑘 𝑎
𝑚𝑏𝑅 𝑡
2𝑘 𝑃𝑅
, 𝜆6 =
𝑅 𝑎
𝑘 𝑎
(
𝑘 𝑎 𝑘 𝑏
𝑅 𝑎
+𝐵 𝑒)𝑑
(𝑟𝑘 𝑃𝑅)
+ 1
where m is the Arduino robot mass, Ie is the moment of inertia, Be is the viscous friction
coefficient, r is the right and left wheel radius, and Rt is the tire radius, ka is the torque
coefficient, kb is electromotive coefficient, Ra is the motors resistance. PD controllers are
implemented with PI kPT and kPR, and derivative gains kDT and kDR to control the velocities of
the right and left motor.
3. Architecture Control
For the architecture control, we are used the kinematics controller for external loop
and a PI/backstepping for the dynamics model as see in Figure 2.
Figure 2. Structure control of PI/backstepping

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3.1. Kinematic Control
This design controller is developed with the inverse kinematics equations of the robot
mobile. The objective of this controller is to generate the reference values for
the PI/Backstepping controller is designed in order the desired values of the linear and angular
velocities. From (3), the kinematic model is given as:
( 𝑥̇
𝑦̇
) = (
𝑐𝑜𝑠(𝜓) −𝑎𝑠𝑖𝑛(𝜓)
𝑠𝑖𝑛(𝜓) 𝑎𝑐𝑜𝑠(𝜓)
) ( 𝑢 𝑟𝑒𝑓
𝜔 𝑟𝑒𝑓
) (4)
where uref the reference is value of the linear velocity, and ωref is angular velocity. The matrix
inverse is (5).
𝐴−1
= (
cos(ψ) sin(ψ)
−
1
a
sin(ψ)
1
a
cos(ψ)
) (5)
The control law can be chosen as:
(
𝑢 𝑟𝑒𝑓
𝑘
𝜔 𝑟𝑒𝑓
𝑘 ) = (
𝑐𝑜𝑠(𝜓) 𝑠𝑖𝑛(𝜓)
−
1
𝑎
𝑠𝑖𝑛(𝜓)
1
𝑎
𝑐𝑜𝑠(𝜓)
) (
𝑥
˙
𝑑+𝜀 𝑥
𝑦
˙
𝑑+𝜀 𝑦
) (6)
where εx = xd − x and 𝜀 𝑦 = 𝑦 𝑑 − 𝑦 the current are position errors, ℎ( 𝑥, 𝑦) and hd(xd, yd) are
the actual and reference points. The study of the stability for the kinematic controller is detailed
in [13].
3.2. Nonlinear PI-based Backstepping Controller Design
The combination of integral action and backstepping equations is proposed to design
the structure control that improves the robustness when the dynamics parameters of the robot
are not well-known. The dynamic part of (3) is:
𝑢
˙
=
𝜆3
𝜆1
𝜔2
−
𝜆4
𝜆1
𝑢 +
𝑢 𝑟𝑒𝑓
𝜆1
, 𝜔
˙
=
𝜆5
𝜆2
𝑢𝜔 −
𝜆6
𝜆2
𝜔 +
𝜔 𝑟𝑒𝑓
𝜆2
(7)
first, we consider the errors control laws:
𝜀1𝑢 = 𝑢 𝑟𝑒𝑓 − 𝑢 ⇒ 𝜀
˙
1𝑢 = 𝑢
˙
𝑟𝑒𝑓 − 𝑢
˙
,𝜀1𝜔 = 𝜔 𝑟𝑒𝑓 − 𝜔 ⇒ 𝜀
˙
1𝜔 = 𝜔
˙
𝑟𝑒𝑓 − 𝜔
˙
(8)
the lyapunov functions is derived as
𝐹(𝜀1𝑢) =
1
2
𝜀1𝑢
2
, 𝐹(𝜀1𝜔) =
1
2
𝜀1𝜔
2
(9)
the time derivative of the Lyapunov candidate functions is calculated as
𝐹
˙
(𝜀1𝑢) = 𝜀1𝑢 𝜀
˙
1𝑢, 𝐹
˙
(𝜀1𝜔) = 𝜀1𝜔 𝜀
˙
1𝜔 (10)
the stabilization of the dynamics errors system can be obtained by introducing a virtual
controls input:
𝑢 𝑢
𝑣
= 𝑢
˙
𝑟𝑒𝑓 + 𝐾1𝑢 𝜀1𝑢 + 𝛼 𝑢 𝜒 𝑢 , 𝑢 𝜔
𝑣
= 𝜔
˙
𝑟𝑒𝑓 + 𝐾1𝜔 𝜀1𝜔 + 𝛼 𝜔 𝜒 𝜔 (11)
Where the integral actions are:
𝜒 𝑢 = ∫ 𝜀1𝑢(𝜏) ∂𝜏 , 𝜒 𝜔 = ∫ 𝜀1𝜔(𝜏) ∂𝜏 (12)

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with 𝐾1𝑢, 𝐾1𝜔, 𝛼 𝑢 and 𝛼 𝜔are design parameters. And now, we calculate the new errors as:
𝜀2𝑢 = 𝑢
˙
˙
+ 𝐾1𝑢 𝜀1𝑢 + 𝛼 𝑢 𝜒 𝑢 , 𝜀2𝜔 = 𝜔
˙
˙
+ 𝐾1𝜔 𝜀1𝜔 + 𝛼 𝜔 𝜒 𝜔 (13)
and
𝜀
˙
2𝑢 = 𝑢
¨
¨
+ 𝐾1𝑢 𝜀
˙
1𝑢 + 𝛼 𝑢 𝜀1𝑢 , 𝜀
˙
2𝜔 = 𝜔
¨
¨
+ 𝐾1𝜔 𝜀
˙
1𝜔 + 𝛼 𝜔 𝜀1𝜔 (14)
from (11) and (14), it follows that:
𝜀
˙
1𝑢 = −𝐾1𝑢 𝜀1𝑢 − 𝛼 𝑢 𝜒 𝑢 + 𝜀2𝑢 , 𝜀
˙
1𝜔 = −𝐾1𝜔 𝜀1𝜔 − 𝛼 𝜔 𝜒 𝜔 + 𝜀2𝜔 (15)
and
𝜀
˙
2𝑢 = −𝐾2𝑢 𝜀2𝑢 − 𝜀1𝑢 , 𝜀
˙
2𝜔 = −𝐾2𝜔 𝜀2𝜔 − 𝜀1𝜔 (16)
where 𝐾2𝑢, 𝐾2𝜔 are positive constant of stability. So, that is result, the integral backstepping
control laws of elevation, linear and angular velocities are:
𝑢 𝑢 = [
𝜆1(𝑢
¨
𝑟𝑒𝑓 + (𝐾1𝑢 + 𝐾2𝑢)𝜀2𝑢
+(1 − 𝐾1𝑢
2
+ 𝛼 𝑢)𝜀1𝑢 − 𝛼 𝑢 𝜒 𝑢)
+2𝜆3 𝜔𝜔
˙
− 𝜆4 𝑢
˙
] (17)
and
𝑢 𝜔 = [
𝜆2(𝜔
¨
𝑟𝑒𝑓 + (𝐾1𝜔 + 𝐾2𝜔)𝜀2𝜔
+(1 − 𝐾1𝜔
2
+ 𝛼 𝜔)𝜀1𝜔 − 𝛼 𝜔 𝜒 𝜔)
−𝜆5(𝑢
˙
𝜔 + 𝑢𝜔
˙
) − 𝜆6 𝜔
˙
] (18)
the stability of the controller can be studied with the Lyapunov theory. The lyapunov candidate
functions is given as:
𝐹(𝜀1𝑢, 𝜀2𝑢) =
𝛼 𝑢 𝜒 𝑢
2+𝜀1𝑢
2 +𝜀2𝑢
2
2
, 𝐹(𝜀1𝜔, 𝜀2𝜔) =
𝛼 𝜔 𝜒 𝜔
2 +𝜀1𝜔
2 +𝜀2𝜔
2
2
(19)
whose first time derivative
𝐹
˙
(𝜀1𝑢, 𝜀2𝑢) = −𝐾1𝑢 𝜀1𝑢
2
− 𝐾2𝑢 𝜀2𝑢
2
≤ 0 , 𝐹
˙
(𝜀1𝜔, 𝜀2𝜔) = −𝐾1𝜔 𝜀1𝜔
2
− 𝐾2𝜔 𝜀2𝜔
2
≤ 0 (20)
are negative defined, which means that, the tracking error is stable.
4. Experiment results
The fourth section present the implementation of the proposed control law on a
Arduino Robot Mobile, with Radius: 185 mm, height: 85 and weight: 1.50 kilogram, it has also
two processors based on the ATmega32u4, see Figure 3, the wheels are driven by DC motors
having rated torque 30 mNm at 15000 rpm, encoders output of 500 ticks/revolution are
integrated for this motors, the sample time of the robot is 0.1s. Arduino Yun is connected to
the robot to send the control law from MATLAB to the robot using the wireless network
connection. The odometric sensors are used to sensing the point h of robot position.
The approach is programmed with the MATLAB using the windows system. In order to test
the proposed PI/Backstepping control law of this paper, several experiments were
carried out with circular track reference trajectory of 2 m. The robot start from the position
P0 = (x, y, ψ) = (0, 0, 0). The gains of the implemented controller are selected as:

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[ 𝐾1𝑢 = 120, 𝐾2𝑢 = 75] [ 𝐾1𝜔 = 14, 𝐾2𝜔 = 2] [𝛼 𝑢 = 12.75, 𝛼 𝜔 = 3.5]
The robot mobile tracks the circular way with smaller error. Thus, show good
performance as see in Figure 4 (a), the evolutions of the desired and actual linear velocity and
angular velocity are plotted in Figure 4 (b), in the figures results, we show that the robot tracks
the reference trajectory correctly and the errors tend to zero. Figure 5 (a) illustrates the control
the robot's wheels speed signal generated by the Motor board processor. The linear velocity
and angular control laws are indicated in Figure 5 (b). The Yun Arduino card is used to receive
the control laws from MATLAB to the robot through wireless card.
Figure 3. Arduino robot mobile
(a) (b)
Figure 4. Simulation results (a) The trajectory followed by the mobile robot,
(b) The linear velocity and angular velocity
4.1. Comparative Study
The second experiment is designed to compare between the Backstepping approach
proposed in [13] and PI/backstepping, we carried out the experiment with same circular track
reference trajectory of 2 meters of radius and same initial posture 𝑃0 = (𝑥, 𝑦, 𝜃) = (0,0,0).
Figure 6 presents the results of the structure control with the evolution the distance error using
the approach and backstepping controller, it can be see that the PI/backstepping is the most
stable and accurate method comparing the backstepping approach. Now in Table 1, we have

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the numerical results: the mean error, variance and standard deviation of trajectory using
backstepping and PI/backstepping approaches, we notice that the PI/backstepping gives good
results in accuracy, by the numerical results its can show the effectiveness of this algorithm.
One of the advantages of this approach is that they can usually to compensate for the dynamics
not modeled as hysteretic damping, wheel tire diameters and vibrations.
(a) (b)
Figure 5. Experimental results (a) Power linear velocity, (b) Control law inputs
Table 1. Numerical comparison results
Trajectory
Backstepping PI/backstepping
X(meter) Y(meter) X(meter) Y(meter)
Mean Error (m) 0.1359 0.0551 0.1169 0.0090
Variance,(m2) 0.0482 0.0045 0.0516 0.0003
Standard deviation 0.2195 0.0671 0.2271 0.0181
Figure 6. Distance errors for backstepping and PI/backstepping.
5. Conclusion
The paper addressed the control structure of mobile robot type unicycle.
The proposed approach is based on the backstepping technique combined with integral action;
the stability study of the system is demonstrated with Lyapunov method, the design controller is
implemented on an Arduino mobile robot. The integral part of algorithm is added to eliminate
the tracking errors and the disturbances and dynamics not modeled as wheel and tire

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diameters, mass, inertia, etc, the comparative results explain the advantages the proposed PI or
Backstepping controller in precision and stability. The force estimator design can be added to
guarantee the robustness of the controller. This work can be applied to remote control using
the virtual reality [16], and augmented reality [17]. We intend also to improve the system in an
environment with obstacles.
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