This document describes the design and validation of a slip-based traction control system using co-simulation between ADAMS and MATLAB/SIMULINK. The objectives are to develop a traction control scheme to enhance vehicle stability under changing road conditions. A sliding mode controller is designed in SIMULINK and a vehicle model is created in ADAMS. Co-simulation is performed to validate that the controller can robustly control wheel slip as road parameters and vehicle mass vary. Simulation results demonstrate the controller tracks the desired slip ratios under different road surfaces and mass values, improving vehicle stability compared to open-loop control.

1. Slip Based Traction Control System Design and Validation Using
Co-Simulation between ADAMS and MATLAB/SIMULINK
By
SUDIPTA SAHA
Roll No. 191325009
Registration No. 235513015
Under the Guidance of
Dr. Suman Saha
CSIR-CMERI, Durgapur
&
H.P.Ikkurti
CSIR-CMERI, Durgapur
School of Mechatronics & Robotics
Indian Institute of Engineering Science and Technology, Shibpur
Howrah – 711103, West Bengal
Central Mechanical Engineering Research Institute, (CMERI), Durgapur- 713 209
India
May 2015

2. Objective
The main objectives are :-
 To develop a new scheme to enhance vehicle longitudinal stability with a
traction control system as road parameters changes during driving.
 To control wheel slip during road changes by varying the slip ratio as a
function of the slip angle.
 To build a plant and a controller model using kinematic and dynamic
equations in MATLAB/SIMULINK and design a robust control law using
sliding mode control technique and test its performance.
 For validation of the controller requires a vehicle model, which is to be
designed using CATIA and then model export to ADAMS.
 To create wheel velocity and chassis velocity as output and control torque
as input in ADAMS Plant model.
 To perform Co-Simulation between ADAMS Plant and controller block in
MATLAB/SIMULINK so that simulation results can verify that the
proposed scheme is robust and superior to the open loop control.

3. Outline of this Presentation
 Traction Control System (TCS)
 Mathematical Representation
 Controller Design
 Vehicle Design and ADAMS Interface
 Co-Simulation
 Results & Discussions
 Conclusion
 Future Scope of work
 References

4. Traction Control System (TCS)
 Traction control system is a method of preventing wheels from
spinning when traction is applied by limiting the amount of
power supplied to the wheel.
 Demonstration of TCS:-

5. Mathematical Representation

6. 𝛼𝑖𝑗 = 𝛿𝑗 − 𝜂𝑖𝑗
𝜆 𝐿𝑖𝑗 =
𝜔𝑖𝑗. 𝑟. cos(𝛼𝑖𝑗) − 𝑣 𝑤𝑖𝑗
𝜔𝑖𝑗. 𝑟. cos(𝛼𝑖𝑗
𝜆 𝑆𝑖𝑗 = tan(𝛼𝑖𝑗

8. Road Conditions C1 C2 C3
Asphalt Dry 1.2801 23.99 0.52
Asphalt Wet 0.857 33.822 0.347
Concrete Dry 1.1973 25.168 0.5373
Cobblestone Dry 1.3713 6.4565 0.6691
Cobblestone Wet 0.4004 33.708 0.1204
Snow 0.1946 94.129 0.0646
Ice 0.05 306.39 0
Serial No Road and Pavement
condition
Croll
1 Very good Asphalt 0.01-0.0125
2 Very good Concrete 0.008-0.1
3 Poor asphalt 0.23
4 Very good stone paving 0.033-0.055
5 Poor stone paving 0.085
6 Snow 0.025
7 Ice 0.037

9. Controller Design
Model Simplification:- As the motion needs to restrict for
longitudinal direction only dynamic equations reduces to
.
4  x roll dragmV F F F
.mij xij
ij
wi wi
T r F
J J
  
w
w
V V
V



( ( ), )f c 

10. Sliding Mode control
Sliding mode control, or SMC, is a nonlinear control method that alters the
dynamics of a nonlinear system by application of a discontinuous control
signal that forces the system to slide along the boundaries of the control
structures. The motion of the system as it slides along these boundaries is
called a sliding mode and the geometrical locus consisting of the boundaries is
called the sliding (hyper) surface.
𝑒 = 𝑓(𝑒) + 𝑔(𝑒). 𝑢
𝑢 = 𝑢 𝑒𝑞 + 𝑢 𝑠𝑤
𝑒 = 0
𝑒 𝑒 = −𝜂(𝑒), 𝜂 > 0

11.  Reference slip Calculation:-
Asphalt Wet = 0.175,Asphalt Wet =0.1326,Concrete
Dry=0.1705,Cobblestone Dry =0.4002,Cobblestone Wet
=0.1415,Snow =0.0595,Ice =0.0398.
 Sliding Mode Control with Integral Action:-
.
  mf hT
2
2
(4. . ( ( ), ) . . . . )
2.1
(1 ). . . ( ( ), )
 
  
  
   
 
&roll D
w
w
g f c g c Ac x
m
f
rV
m g f c
J

 
  
(1 ).
.


w w
r
h
J V

( ) f f G 
min max
min max
,min ,max
,min ,max
 
 
 
 
D D D
roll roll roll
m m m
r r r
c c c
c c c
2
2 2
4 ( ( ), ) ( ( ), )
. .
2
(1 ).
. . ( ( ), ) . ( ( ), )
roll roll
D D
w
w
f c f c c c
g
G A x c c
V
r r
m f c m f c
J
   


   
 
 
   
 
    
 
  
 
  
&

12.  Sliding Surface design:-
 Formulation of Control Law:-
( , )  %
ne t   %
ref  
0
( , ) ( ).  % %
t
ie t K d    
0&e ( ) ( ) 0   & &
ref i refK   
1
( )     meq i refT f K
h
 
1
( )     meq i refT f K
h
 
1
( ) sgn( )      i reff K K e
h
 
 m meq mhtT T T
 K G 
21
2
V e
V ee& &
sgn( )e f f K e    
 e f f K e  
G e K e 
e 

13. Chattering :-
When the system is implemented in a digital controller, due to
switching of control law at infinite frequency, actuator can’t
cope up with it. So the trajectories will chatter across e = 0
resulting in high control effort and oscillations in the system
response.
1
mht
e
T Ksat
h 
  
   
  
( ) ( )
  
       
  
m i ref
e
T f K G sat  

1
1
e
e e
sat e
e

 
 


 
  
    
   


14. Vehicle Design and ADAMS Interface
CAD Model Using CATIA:-
 Parts (Wheel, Chassis) & Assembly Design
 Creating Joints
 Applying Materials
 Road Design
CATIA model export to Adams:-
 Using Sim Designer extension in CATIA V5 Cad model is
converted in Windows Command Script (.cmd) file extension
file which can be opened in Adams/View.
 Importing in Adams (1, 2)

15. ADAMS Interface:-
 Modifying Revolute Joints
 Dummy Creation
 Creating Translational Joints (1, 2)
 Defining torque
 Contact Surface design (1, 2, 3)
Vehicle Model:-

16. Co-Simulation
 Co-simulation is the process of simulating a system where two or more
separate simulation programs are simultaneously used to model various
aspects of the system and these simulation environments communicate
during run-time, to simulate the whole system, thus affecting each other’s
output. In this case the vehicle is modeled in Adams-View whereas the
Controller is modeled in SIMULINK and a co-simulation is setup to run the
vehicle model in Adams using the Controller model in SIMULINK.

17. Design Process using ADAMS Controls:-

18. Selection of Input and Output Variables:-
Variable Name Description
Torque_1
Rear left control torque
Torque_2
Rear right control torque
Torque_3
Front left control torque
Torque_4
Front right control torque
Variable Name Description
Vel_1
Rear left angular velocity
Vel_2
Rear right angular velocity
Vel_3
Front left angular velocity
Vel_4
Front right angular velocity
chesis
Chassis velocity

19. Steps of Co-Simulation:-
 Load Adams/Controls (1)
 Selecting Input and Output Variables
 Set References for Input Variables
 Adams Block Exporting to MATLAB/SIMULINK
 Linking Adams Plant model and the Controller model in
SIMULINK (1, 2, 3)
 Starting Co-simulation
 Checklist before Co-Simulation.

20. Results & Discussions
Four sample sets is used in simulation
 Asphalt Dry-Snow-Concrete Dry
 Cobblestone Dry-Ice-Cobblestone Wet
 Asphalt Wet-Concrete Dry-Ice
 Cobblestone Wet-Cobblestone Dry-Snow.
Simulation time is set to 10 (s) in all. There are three road conditions
included into each of sample sets. For first duration of time is from 0 (s) to
3 (s) and second duration from 3 (s) to 6 (s), third duration of time is 6 (s)
to 10 (s.
To prove the robustness to the parameter uncertainty, mass of the vehicle
are being changed simultaneously. For each set of road condition mass of
the vehicle varies as m=450 (kg), m=600(kg), m=800(kg), m=1050(kg).
In above of every figure name of set and mass are indicated, for example
Asphalt Dry-Snow-Concrete Dry written as Ash D- Snow-Con D-600.

26. Validation
Chassis Velocity with Control & without Control:-

27. Wheel Velocity with control & without control:-

28. Animation

29. Reference slip tracking in Co-Simulation

30. Conclusion
 Asphalt wet, Ice, Snowy roads are the most critical conditions identified
and controlled at closed loop control, in which open loop control fails to
show the desired result.
 In case of closed loop control after 4(s) Slip based traction controller
follows the reference slip in a 10(s) co-simulation between ADAMS and
MATLAB/SIMULINK.
 In the Simulation during 0(s) to 3(s) peak value of slip=1 occurs because of
the high value of starting torque given by the controller and oscillations
occur as the chosen values of integral gain and design parameter are not
optimized.
 The control focuses on maximizing the driving force by setting the optimal
value of slip ratio within the specified variation in mass and road
conditions.
 Validation leads to development of four wheeler in-hub electric vehicle
using CATIA and transportation to ADAMS. A road model has been
designed in ADAMS constructed of three different friction coefficients and
co-simulation have been achieved between ADAMS/View and
MATLAB/Simulink to test the robustness of the controller.

31. Future scope of work
 The scope of work will be to maintain the initial reference
torque well within the motor maximum torque limit by
controlling the starting torque to avoid initial jerk.
 To find a optimal value of integral gain and design
parameter for avoiding oscillations.
 To attain a desired performance other control strategies can
be applied e.g. PI, PID , Sliding mode with fuzzy logic etc.
 To get a complete control of electric vehicle, in the
controller design yaw control for lateral stability should also
be taken into account.

32. References
[1] Y. Hori, Y. Toyoda, Tsuruoka Y., Traction control of electric vehicle based on the estimation of road surface
condition-basic experimental results using the test EV UOT Electric March, Power Conversion Conference -
Nagaoka 1997.
[2] T.A. Johansen, I. Peterson, J. Kalkkuhl, J. Ludemann Gain-scheduled Wheel Slip Control in Automotive
Brake Systems, Control Systems Technology, IEEE Transactions on, Volume: 11,Issue: 6, Nov. 2003.
[3] W. E. Ting, J. S. Lin Nonlinear back stepping design of anti-lock braking systems with assistance of active
suspensions, Proceedings of the 16th IFAC World Congress, 2005
[4] I. Petersen, T. A. Johansen, J. Kalkkuhl, and J. Ludemann Wheel slip control using gain-scheduled lq-
lpv/lmi analysis and experimental results, in Proc. European Control Conference, Cambridge, United
Kingdom, 2003
[5] O. T.C. Nyandoro, J. O. Pedro, B. Dwolatzky, and O. A. Dahunsi State Feedback Based Linear Slip Control
Formulation for Vehicular Antilock Braking System, Proceedings of the World Congress on Engineering 2011
Vol I, WCE 2011
[6] P. Ratiroch-Anant, H. Hirata, M. Anabuki, S. Ouchi Adaptive Controller Design for Anti-Slip System of
EV, Conference on Robotics, Automation and Mechatronics, 2006.
[7] J. Yi, L. Alvarez, X. Claeys, R. Horowitz, Emergency Braking Control with an Observer-based Dynamic
Tire/Road Friction Model and Wheel Angular Velocity Measurement, Vehicle System Dynamics, Vol. 39, No.
2, 2003.
[8] S.Li, T.Kawabe,”Slip Suppression of Electric Vehicles Using Sliding Mode Control Method” , Intelligent
Control and Automation, 2013, 4, 327-334
[9] Hans-Christian ,Becker Jensen “Design of Slip-based Active Braking and Traction Control System for the
Electric Vehicle QBEAK”, 2012
[10] CATIA V5 Part design, Tutorials MSC Corporation.
[11] “Getting Started with Adams Control”,
[12] S .J.Rao, “Vehicle Modeling and ADAMS-Simulink Co-Simulation With Intregated Continuously
Controlled Electronic Suspension (CES) and Electronic Stability Control (ESC) Models”, The Ohio State
University 2009

33. Thank You