Engineering, Technology & Applied Science Research Vol. 7, No. 5, 2017, 2017-2021 2017  
 

www.etasr.com Ayed et al.: Software In the Loop Simulation for Robot Manipulators 
 

Software In the Loop Simulation  
for Robot Manipulators 

Mossaad Ben Ayed 
CES Laboratory, Sfax University, Tunisia 

and Al Majmaah University, KSA 
mm.ayed@mu.edu.sa, 

mossaad_benayed@yahoo.fr 

Lilia Zouari 
CES Laboratory 

Sfax University, Tunisia 
lilia6enis@yahoo.fr 

 

Mohamed Abid 
CES Laboratory 

Sfax University, Tunisia 
mohamed.abid@enis.rnu.tn 

 
 

Abstract—In the last decades, the classical verification of robotic 
software component is postponed until the code is developed 
enough to function in real hardware. For this reason, the 
verification of code at early stages is essential to reduce 
development costs and necessary time for embedded systems 
such as robot manipulator. Therefore, Software In the Loop 
(SIL) simulation may be realized in the early stages of software 
development. It offers the possibility to execute tests before the 
hardware is available and thus detect errors. In this paper, we 
propose a Software In the Loop (SIL) test for robot manipulator 
driven by a Brushless DC Motor without a target system 
hardware. Simulation results prove the rapidity and the good 
performance of the developed code for the controller’s part by 
the validation of the behavior of robot manipulator software. 

Keywords-software in the loop (SIL); robot manipulator; 
controllers; S-function 

I. INTRODUCTION 
The engineering of software and digital electronics 

represent an important place in the process of the design and 
the verification of the electronic systems [1, 2]. The levels of 
electronic components integration, the complexity and the size 
of embedded code are continually growing and this growth is 
accompanied by great sensitivity to the disturbance and 
uncertainties [3, 4]. The number of possible combinations 
states poses a problem of combinatorial explosion that led to 
huge numbers of possible states due to the complexity of 
mechatronic systems [5, 6]. This complexity is a challenge for 
dependability [1, 5, 7]. Indeed, new robotic applications 
representing the most known mechatronic system have 
required the lightest and most precise robots that can be driven 
with small amounts of energy [8-10]. Unfortunately, the 
flexibility of these robots leads to an oscillating behavior at 
the end of the link. Obtaining a precise trajectory tracking is 
an arduous task that requires a complex control in closed-loop 
[11-14]. To achieve the control objectives, such as the 
accuracy of the position and vibration suppression, many 
control techniques were applied to these flexible joint 
manipulators [15-18]. Thus, we consider the entire robotic 
system including actuator dynamics and flexibility in the joint. 
To guarantee a high performance of the control system, a 
flexible joint manipulator which is highly nonlinear, strongly 
coupled and uncertain is used [19-21]. The embedded systems 
in the robotic industry must accomplish high safety 
requirements [22]. Therefore, verification step is essential to 

verify the behavior of the proposed system and to detect and 
correct the errors at earlier stage without any hardware or 
physical environment [23]. 

This paper presents the SIL simulation technique for robot 
manipulator driven by Brushless DC Motor (BDCM). In 
section I, some related works were detailed. The second 
section described the manipulator control system. The third 
section defined the SIL simulation technique. The fourth 
section was devoted for SIL implementation method shown 
the speed and torque controller and the current hysteresis 
controller. Finally, simulation results in MATLAB/Simulink 
are shown in Section V. Extracted simulation time prove the 
role of the SIL technique to accelerate the simulation and 
prevent the risks. 

II. RELATED WORKS 
The Software In the Loop (SIL) approach allow us to 

integrate software component with an environment simulation 
[23-24]. Besides, this approach allows testing different 
scenarios and control algorithms very quickly and benefit 
from its flexibility. In [25], authors realized a SIL 
environment for the development of control strategies for 
hybrid vehicles. This environment allows the evaluation of 
different vehicle dynamic behavior for each additional masses 
and each position of gravity center of the vehicle. In [26], 
authors described the SIL validation of a flight control system 
needed to develop low cost Unmanned Aerial Vehicles 
(UAV). Moreover, this technique allows modeling, linearizing 
and decoupling of the highly nonlinear six degrees-of-freedom 
dynamics of the vehicle. In [27], authors presented a low cost 
SIL simulation to evaluate performance of control law for 
quadricopter using MATLAB/Simulink for control law and X-
plane for vehicle dynamics and flight environment. In [22], 
authors described a cost effective SIL simulation methodology 
for mobile nodes representing large-scale pervasive system. 
This methodology permits deploying the real code once and 
replicating environments and over virtual devices as often as 
they wish. In [28], authors used the High Level Simulation 
Framework Open Virtual Platforms (OVP) for SIL simulation 
of embedded control applications to benefit from rapid 
prototyping in early stages of the development cycle. This 
approach was applied for two cases which are the motor 
control application and the image processing application. In 
[29], authors described SIL simulators for autonomous 



Engineering, Technology & Applied Science Research Vol. 7, No. 5, 2017, 2017-2021 2018  
 

www.etasr.com Ayed et al.: Software In the Loop Simulation for Robot Manipulators 
 

navigation algorithms of planetary rovers due to temporarily 
unavailable hardware. This simulator is necessary to facilitate 
the continued algorithms development even in the absence of 
hardware. In [30], authors studied SIL technique for flying 
robots. In fact, the high production expenses need the 
validation of such system by simulation to predict the robot 
reactions in various flight modes. This technique was realized 
using MATLAB for control part and ADAMS for structural 
design. 

Nowadays, validating complex systems in early stages 
needs a good simulation technique. This paper proposes the 
SIL simulation for robot arm driven by BDCM. The 
development of robot control algorithms for robot arm driven 
by BDCM often hinges on access to robot hardware that its 
access is usually very limited. In order to facilitate the 
continued development of these algorithms, the SIL 
simulation was used to integrate each algorithm change at low 
cost. 

III. MANIPULATOR CONTROL SYSTEM 
To implement control algorithms for the proposed 

manipulator, it is essential to check and study the performance 
of the manipulator in the MATLAB/Simulink environment. In 
fact, we focus on the implementation of the dynamic model of 
the proposed manipulator taking into consideration the 
controllers studied in the MATLAB/Simulink. Figure 1 
presents the block diagram of the proposed manipulator. 

Based on Figure 1, we distinguish three blocks describing 
the proposed system: 

 The model of the system,  

 The controllers,  

 The reference trajectory. 

IV. SIL TECHNIQUE 
Designing a system with its controller requires 

mathematical model followed by adjusted certain parameters. 
Recently, the X In the Loop approach is often used to check 
the system in the middle stage of the design [31]. This 
approach is an effective way to produce a fast and reliable 
short-time system. In the X In the Loop approach, there are 
two levels of the simulation configurations based on the PC 
shown in Figure 2: Model In The Loop (MIL) and SIL [31]. 
Each configuration provides certain levels of progress and 
reduced the gap in the development process between the 
mathematical model and the proposed platform [31, 32]. 
Indeed, these techniques are essential to predict possible risks 
before the implementation phase and to accelerate the 
simulation time. Thus a check is made by exploiting several 
verification techniques. MIL simulation refers to the model 
verification in the modeling environment such as 
MATLAB/Simulink. Check system at MIL means that the 
model and its controllers are simulated in the modeling 
framework without physical hardware. When checking the 
implementation of complex systems models, it is necessary to 
distinguish between the verification of modules and the check 
of the overall system. This technique verifies the system at an 
earlier stage of the realization. In this step, any problem of the 
mathematical model of the controller can be quickly found 
and fixed [33].  

In SIL simulation, the used model is replaced by 
executable code running on the same computer platform of a 
fixed-point arithmetic way. This step allows the developer to 
quickly find some bad choices of memory size and to 
ameliorate the controller code performance. Checking system 
in SIL level means that embedded software is verified in a 
simulation environment, but without any hardware such as 
mechanical or hydraulic components or sensors or actuators 
[33]. Normally, these two steps are performed using a single 
integrated platform in a PC [33]. 
 

 
Fig. 1.  Block diagram of the proposed manipulator 

 



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Fig. 2.  Different simulation techniques 

V. SIL IMPLEMENTATION METHOD 
After the algorithms in MIL stage operate as designed, the 

model of the controller can be converted into C code with a 
coder as Simulink Coder and TargetLink. The exported code 
can as well be connected to the external sources of code. The 
created code can first be tested in Simulink and also 
communicated with other systems and subsystems. In this 
section, we will present the modeling of the control part and 
simulation via SIL simulation. In SIL, the used model in MIL 
technique is replaced with executable code running on the 
same computer platform in a fixed-point arithmetic way. This 
step is performed using an integrated platform in a single 
computer. Figure 3 presents the block diagram of the proposed 
manipulator replacing the control block by the adequate S-
function block to communicate with the executable code as 
well as an image of the C code area. To perform a SIL 
simulation, the flexible joint manipulator with brushless DC 
motor must be modeled. The control part to be replaced by the 
equivalent C code is articulated on two controllers that are the 
speed and torque controller and the current controller. Stills 
from the fuzzy logic PI controller code and hysterisis 
controller in C code are shown in Figure 4. For the speed and 
torque control part, the inputs are: the speed error (rad/s) and 
the position error (rad) and the output is the amplitude of the 
reference current. In order to obtain a compromise in 
performance between the simulation time and the reliability of 
the studied controllers and simulation results on previous 
works [34-25], we adopt the fuzzy PI controller.  For the 
current control part that uses the hysteresis controller, the 
inputs are the current errors in the 3 phases (A) and the 
outputs are the states of IGBTs (S1-6) (1 or 0). In this part, the 
states of IGBTs applied to the inverter through the hysteresis 
controller will be verified through the SIL simulation 
replacing it by its equivalent C code.  

VI. SIMULATION RESULTS 
In order to implement these control algorithms of the 

studied manipulator, it is essential to check and study as a first 
step the controllers of the manipulator driven by BDCM in 
MATLAB/Simulink environment. To evaluate the 
effectiveness and performances of SIL simulation technique 
for different controllers, we are interested to the following 
principal variables: speed (rad/sec), position (rad), position 
error (rad), speed error (rad/sec), control law and simulation 
time. The system parameters are given in Table I. Figure 5 
represents respectively the evolution of the speed, the 

position, the error of the position and speed and the 
electromagnetic torque in the case of fuzzy logic PI controller. 
To evaluate the performance of the SIL technique, we adopted 
the simulation results given on the MATLAB/Simulink 
environment specifying the execution time of the various 
controllers’ codes. After checking the control algorithm in 
MATLAB/Simulink, SIL technique produces the same results 
in the case of MIL simulation by reaching the desired speed 
and position. The simulation time is decreased compared to 
those of MIL simulation technique. Table II represents a 
simulation time recapitulation for each system for both MIL 
and SIL simulation techniques.  

TABLE I.  SYSTEM PARAMETERS 

Symbol Description Numeric value 
R Resistance 0.625 Ω 
L Inductance 1.595e-3 H 
Jm du Motor inertia 1e-5Kg/m

2 
m1 Manipulator mass 0.8619 Kg 
l1 Length of manipulator 0.3 m 
J1 Inertia of the manipulator 0.0065 N/m2 
N Reduction ratio 74 
η Transmission efficiency 0.72 

f Friction 1.164e-3 Kgm/s2 
Kt Torque constant 0.0382 
KE Electromotive constant 0.0382 

TABLE II.  SIMULATION TIME OF SYSTEMS FOR MIL AND SIL 
SIMULATION TECHNIQUES 

System MIL SIL 
Motor 6 mn 40 sec 4 mn 40 sec 

Manipulator 18 h 9 mn 34 sec 15 h 13 mn 55 sec 

VII. CONCLUSION 
This work was developed within the SIL simulation 

technique for the verification of the proposed robot 
manipulator driven by Brushless DC Motor at an earlier stage. 
In such a complex system, the SIL simulation can guarantee 
the verification of software component i.e. C code without any 
hardware and benefit from its flexibility to validate different 
controller algorithms. So, the manipulators including their 
controllers have been presented. Then, the SIL technique has 
been defined. Also, SIL implementation method has been 
detailed. Finally, simulation results have shown that the SIL 
simulation technique leads to high performance at low cost 
and time and allows detecting errors code before 
implementation. 



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Fig. 3.  Different speed and torque controllers opted for the proposed manipulator (left) and the code area (right) 

 

      
Fig. 4.  Fuzzy logic PI controller code (left) and hysterisis controller in C code (right) 

 

 
Fig. 5.  Simulation results for fuzzy logic PI controller 

 



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