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International Conference on Electronics and Communication
Systems-2016
Design of
Adaptive Cruise Control System
in LabVIEW Platform
Presented By:
Guided By:
Mr. J. Sam Jeba Kumar
Asst. Prof.,
Dept. of Instrumentation
and Control
SRM University
Chennai, India
Dr. A. Vimala Juliet
Prof. and Head,
Dept. of Electronics
and Instrumentation
SRM University
Chennai, India
P. Ramani Ranjan Senapati, M.Tech Scholar
Dept. of Electronics and Control
SRM University
Chennai, India
Agenda
• Introduction
• Adapted Methodology
• Design Procedure
• Controller Design and Validation
• Discussion of Results
• Conclusions
• Future Work
2Design of ACC in LabVIEW Platform
Introduction
I-DAS
ACC
ABS
Collisio
n
Warnin
g
TCS
Airbag
Blind
Spot
Monitor
Tire
Pressure
Control
E-Call
System
Lane
Keepin
g
Steering
Assistan
ce
WhyI-DAS???
Fig.1 Parameters of an I-DAS
3Design of ACC in LabVIEW Platform
Adaptive Cruise Control
Fig.2 ACC system based car following the front vehicle
4Design of ACC in LabVIEW Platform
Why LabVIEW???
Advantages of LabVIEW
• Graphical user interface
• Drag & Drop built in
functions
• Multi Platforms
• Reduces cost
• Flexibility and Scalability
• Simple Application
distribution
• Object oriented design
• And etc….
Application in Automotive
Industry
• Rapid control photo typing
• Hardware in loop simulation
• Diverse input output
• Automotive end-of-line test,
• real time simulation
• Test cell measurement in
vehicle data logging and
control
• Online noise analysis,
vibrations and harshness test
is easier.
5Design of ACC in LabVIEW Platform
Methodology
• Passengers Safety and comfort
– Constant Speed Control Sub mode
– Variable Speed Control Sub mode
– Emergency Stop Control Sub mode
• Optimal Fuel Consumption
6Design of ACC in LabVIEW Platform
Fig.3 Operation of 3 sub modes in ACC system
7Design of ACC in LabVIEW Platform
Fig.4 Overall block diagram of the developed system
8Design of ACC in LabVIEW Platform
Design Procedure
( ) throttle brake road friction
d
MV F F F F
dt
   
ur
Vehicle mass 1 unit
Gain due to Throttle applied force 0.01 unit
Gain due to Brake applied force 0.01 unit
Gain due to Road inclination applied
force
1 unit
Gain due to Friction applied force 0.01 unit
VehicleModelingUsingForceBalanced
Equation
9Design of ACC in LabVIEW Platform
1
4.39
( )
0.1746
G s
s


2
1
( )
1
G s
s


Accelerator Model :
Brake Model :
is taken as 2.25.
BrakeandAcceleratormodel
selection
10Design of ACC in LabVIEW Platform
Constantspeedcontrollerdesigning
Anti Windup PID Controller
Fig.5 Block diagram of Anti windup PID controller
11Design of ACC in LabVIEW Platform
Controllerdesigningfortheproperselection
ofacceleratorandbrake
•Accelerator vs Velocity
• Brake vs Velocity
12Design of ACC in LabVIEW Platform
Overall Developed System
Vehicle Modeling Using
Force Balanced Equation
Brake and Accelerator
model selection
Constant speed controller
designing
Controller designing for the
proper selection of accelerator
and brake
ACC
13Design of ACC in LabVIEW Platform
Fig.6 Front panel diagram only when accelerator pedal is pressed by 40%
14Design of ACC in LabVIEW Platform
Fig. 7 Front panel diagram only when accelerator pedal is pressed by 40%
15Design of ACC in LabVIEW Platform
Fig. 8 Front panel diagram only when accelerator pedal is pressed by 40%
16Design of ACC in LabVIEW Platform
Fig. 9 Front panel diagram only when accelerator pedal is pressed by 40%
17Design of ACC in LabVIEW Platform
Conclusions
• Controller is designed depending on the
vehicle force balanced equation.
• Passengers Safety is taken into consideration.
• Fuel efficiency optimization is achieved in
some extent.
• Traditional PID, PI and Bang-Bang Controllers
are used.
18Design of ACC in LabVIEW Platform
Future Work
• PID controller should be optimized at the
starting of ACC mode.
• BLDC motor should be connected with the
throttle valve with Fuzzy Logic based position
algorithm.
• Model predictive controller based ACC vs PID
controller based ACC system.
19Design of ACC in LabVIEW Platform
References
• Elisabeth Füssl et al., “Methodological development of a specific tool for assessing
acceptability of assistive systems of powered two-wheeler-riders,” IET Intelligent
Transport Systems, Vol. 9, Iss. 1, pp. 12-21, August 2014.
• Vadim A. Butakov and Petros A. Ioannou, “Driver/Vehicle response diagnostic
system for the vehicle-following Case”, Transactions on Intelligent Transportations
Systems, IEEE, Vol. 15, no. 2, pp. 1947-1957, October 2014.
• Ata Khan, Ataur Bacchus and Stephen Erwin, “Surrogate safety measures as aid to
driver assistancesystem design of the cognitive vehicle”, IET Intelligent Transport
Systems, Vol. 8, Iss. 4, pp. 415-424, August 2013.
• Yugong Luo, Tao Chen and Keqiang Li, “Multi-objective decoupling algorithm for
active distance control of intelligent hybrid electric vehicle”, Mechanical Systems
and Signal Processing, ELSEVIER, Vol. 64-65, pp. 29-45, February 2015.
• E. Adell, A. Várhelyi, and M. D. Fontana, “The effects of a driver assistance system
for safe speed and safe distance—a real-life field study,” Transp. Res. Part C:
Emerging Technol., Vol. 19, no. 1, pp. 145–155, February 2011.
• A. Kesting, M. Treiber, M. Schönhof, and D. Helbing, “Adaptive cruise control
design for active congestion avoidance,” Transp. Res. Part C: Emerging Technol.,
Vol. 16, no. 6, pp. 668–683, December 2008.
• Adrian Zlocki and Philipp Themann, “Methodology for quantification of fuel
reduction potential for adaptive cruise control relevant driving strategies”, IET
Intelligent Transport Systems, Vol. 8, Iss. 1, pp. 68-75, December 2012.
Design of ACC in LabVIEW Platform 20
Contd…
• Shengbo Eben Li, Zhenzhong Jia, Keqiang Li and Bo Cheng, “Fast online
computation of a model predictive ontroller and its application to fuel economy–
oriented adaptive cruise control”, Transactions on Intelligent Transportation
Systems, IEEE, Vol. 16, no. 3, pp. 1199-1209, June 2015.
• Milanés V, Villagrá J, Pérez J and González C, “Low-speed longitudinal controllers
for mass produced cars-a comparative study”, Trans Ind Electron, IEEE, Vol. 59,
no. 1, pp. 620–628, 2012.
• E. Onieva, J. Godoy, J. Villagrá, V. Milanés and J. Pérez, “On-line learning of a
fuzzy controller for a precise vehicle cruise control system”, Expert Systems with
Applications, ELSEVIER, Vol. 40, pp. 1046-1053, 2013.
• S. Hassan Hosseinnia, Inés Tejado, Vicente Milanés, Jorge Villagrá, and Blas M.
Vinagre, “Experimental application of hybrid fractional-order adaptive cruise
control at low speed”, Transactions on Control System Technology, IEEE, Vol. 22,
no. 6, pp. 2329-2336, November 2014.
• Yugong Luo, Tao Chen, Shuwei Zhang, and Keqiang Li, “Intelligent hybrid electric
vehicle ACC with coordinated control of tracking ability, fuel economy, and ride
comfort”, Transactions on Intelligent Transportation Systems, IEEE, Vol. 16, no. 4,
pp. 2303-2308, August 2015.
• Meng Wang, Winnie Daamen, Serge Hoogendoorn and Bart van Arem, “Potential
impacts of ecological adaptive cruise control systems on traffic and environment”,
IET Intelligent Transport Systems, Vol. 8, Iss. 2, pp. 77-86, November 2012.
Design of ACC in LabVIEW Platform 21
Contd…
• Vibhor L. Bageshwar, William L. Garrard, and Rajesh Rajamani, “Model Predictive Control
of Transitional Maneuvers for Adaptive Cruise Control Vehicles “, Transactions on Vehicular
Technology, IEEE, Vol. 53, no. 5, pp. 1573-1585, September 2004.
• Attila Trohak, Janos Vegh, Gergely Kovacs and Zsofia Forgacs, “Implementation of driving
assistance systems using LabVIEW-based platform”, International Carpathian Control
Conference, IEEE, pp. 548-551, May 2015.
• K. Yi and J. Chung, “Nonlinear brake control for vehicle CW/CA systems,” IEEE/ASME
Transaction Mechatron., Vol. 6, no. 1, pp. 17–25, March 2001.
• Jullierme Emiliano Alves Dias, Guilherme Augusto Silva Pereira and Reinaldo Martinez
Palhares, “Longitudinal model identification and velocity Control of an autonomous car”,
Transactions on Intelligent Transportation Systems, IEEE, Vol. 16, no. 2, pp. 776-786, April
2015.
• A. Kamga and A. Rachid, “Speed, steering angle and path tracking controls for a tricycle
robot,” in Proc. IEEE Int. Symp. Comput. Aided Control Syst. Des., pp. 56–61, September
1996.
• I. Tejado, V. Milanés, J. Villagrá, and B. M. Vinagre, “Fractional network based control for
vehicle speed adaptation via vehicle-to infrastructure communications,” IEEE Trans. Control
Syst. Technol., vol. 21, no. 3, pp. 780–790, May 2013.
• I. Tejado, V. Milanés, J. Villagrá, J. Godoy, H. HosseinNia, and B. M. Vinagre, “Low speed
control of an autonomous vehicle by using a fractional PI controller,” in Proc. 18th IFAC
World Congr., vol. 18, 2011, pp. 15025–15030.
• S. H. HosseinNia, I. Tejado, B. M. Vinagre, V. Milanés, and J. Villagrá, “Low speed control of
an autonomous vehicle using a hybrid fractional order controller,” International Conference
on Control, Instrumentation and Automation, IEEE, pp. 116–121, December 2011.
Design of ACC in LabVIEW Platform 22
Thank You !!!
23Design of ACC in LabVIEW Platform

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International conference on electronics and communication systems 2016 (2)

  • 1. International Conference on Electronics and Communication Systems-2016 Design of Adaptive Cruise Control System in LabVIEW Platform Presented By: Guided By: Mr. J. Sam Jeba Kumar Asst. Prof., Dept. of Instrumentation and Control SRM University Chennai, India Dr. A. Vimala Juliet Prof. and Head, Dept. of Electronics and Instrumentation SRM University Chennai, India P. Ramani Ranjan Senapati, M.Tech Scholar Dept. of Electronics and Control SRM University Chennai, India
  • 2. Agenda • Introduction • Adapted Methodology • Design Procedure • Controller Design and Validation • Discussion of Results • Conclusions • Future Work 2Design of ACC in LabVIEW Platform
  • 4. Adaptive Cruise Control Fig.2 ACC system based car following the front vehicle 4Design of ACC in LabVIEW Platform
  • 5. Why LabVIEW??? Advantages of LabVIEW • Graphical user interface • Drag & Drop built in functions • Multi Platforms • Reduces cost • Flexibility and Scalability • Simple Application distribution • Object oriented design • And etc…. Application in Automotive Industry • Rapid control photo typing • Hardware in loop simulation • Diverse input output • Automotive end-of-line test, • real time simulation • Test cell measurement in vehicle data logging and control • Online noise analysis, vibrations and harshness test is easier. 5Design of ACC in LabVIEW Platform
  • 6. Methodology • Passengers Safety and comfort – Constant Speed Control Sub mode – Variable Speed Control Sub mode – Emergency Stop Control Sub mode • Optimal Fuel Consumption 6Design of ACC in LabVIEW Platform
  • 7. Fig.3 Operation of 3 sub modes in ACC system 7Design of ACC in LabVIEW Platform
  • 8. Fig.4 Overall block diagram of the developed system 8Design of ACC in LabVIEW Platform
  • 9. Design Procedure ( ) throttle brake road friction d MV F F F F dt     ur Vehicle mass 1 unit Gain due to Throttle applied force 0.01 unit Gain due to Brake applied force 0.01 unit Gain due to Road inclination applied force 1 unit Gain due to Friction applied force 0.01 unit VehicleModelingUsingForceBalanced Equation 9Design of ACC in LabVIEW Platform
  • 10. 1 4.39 ( ) 0.1746 G s s   2 1 ( ) 1 G s s   Accelerator Model : Brake Model : is taken as 2.25. BrakeandAcceleratormodel selection 10Design of ACC in LabVIEW Platform
  • 11. Constantspeedcontrollerdesigning Anti Windup PID Controller Fig.5 Block diagram of Anti windup PID controller 11Design of ACC in LabVIEW Platform
  • 13. Overall Developed System Vehicle Modeling Using Force Balanced Equation Brake and Accelerator model selection Constant speed controller designing Controller designing for the proper selection of accelerator and brake ACC 13Design of ACC in LabVIEW Platform
  • 14. Fig.6 Front panel diagram only when accelerator pedal is pressed by 40% 14Design of ACC in LabVIEW Platform
  • 15. Fig. 7 Front panel diagram only when accelerator pedal is pressed by 40% 15Design of ACC in LabVIEW Platform
  • 16. Fig. 8 Front panel diagram only when accelerator pedal is pressed by 40% 16Design of ACC in LabVIEW Platform
  • 17. Fig. 9 Front panel diagram only when accelerator pedal is pressed by 40% 17Design of ACC in LabVIEW Platform
  • 18. Conclusions • Controller is designed depending on the vehicle force balanced equation. • Passengers Safety is taken into consideration. • Fuel efficiency optimization is achieved in some extent. • Traditional PID, PI and Bang-Bang Controllers are used. 18Design of ACC in LabVIEW Platform
  • 19. Future Work • PID controller should be optimized at the starting of ACC mode. • BLDC motor should be connected with the throttle valve with Fuzzy Logic based position algorithm. • Model predictive controller based ACC vs PID controller based ACC system. 19Design of ACC in LabVIEW Platform
  • 20. References • Elisabeth Füssl et al., “Methodological development of a specific tool for assessing acceptability of assistive systems of powered two-wheeler-riders,” IET Intelligent Transport Systems, Vol. 9, Iss. 1, pp. 12-21, August 2014. • Vadim A. Butakov and Petros A. Ioannou, “Driver/Vehicle response diagnostic system for the vehicle-following Case”, Transactions on Intelligent Transportations Systems, IEEE, Vol. 15, no. 2, pp. 1947-1957, October 2014. • Ata Khan, Ataur Bacchus and Stephen Erwin, “Surrogate safety measures as aid to driver assistancesystem design of the cognitive vehicle”, IET Intelligent Transport Systems, Vol. 8, Iss. 4, pp. 415-424, August 2013. • Yugong Luo, Tao Chen and Keqiang Li, “Multi-objective decoupling algorithm for active distance control of intelligent hybrid electric vehicle”, Mechanical Systems and Signal Processing, ELSEVIER, Vol. 64-65, pp. 29-45, February 2015. • E. Adell, A. Várhelyi, and M. D. Fontana, “The effects of a driver assistance system for safe speed and safe distance—a real-life field study,” Transp. Res. Part C: Emerging Technol., Vol. 19, no. 1, pp. 145–155, February 2011. • A. Kesting, M. Treiber, M. Schönhof, and D. Helbing, “Adaptive cruise control design for active congestion avoidance,” Transp. Res. Part C: Emerging Technol., Vol. 16, no. 6, pp. 668–683, December 2008. • Adrian Zlocki and Philipp Themann, “Methodology for quantification of fuel reduction potential for adaptive cruise control relevant driving strategies”, IET Intelligent Transport Systems, Vol. 8, Iss. 1, pp. 68-75, December 2012. Design of ACC in LabVIEW Platform 20
  • 21. Contd… • Shengbo Eben Li, Zhenzhong Jia, Keqiang Li and Bo Cheng, “Fast online computation of a model predictive ontroller and its application to fuel economy– oriented adaptive cruise control”, Transactions on Intelligent Transportation Systems, IEEE, Vol. 16, no. 3, pp. 1199-1209, June 2015. • Milanés V, Villagrá J, Pérez J and González C, “Low-speed longitudinal controllers for mass produced cars-a comparative study”, Trans Ind Electron, IEEE, Vol. 59, no. 1, pp. 620–628, 2012. • E. Onieva, J. Godoy, J. Villagrá, V. Milanés and J. Pérez, “On-line learning of a fuzzy controller for a precise vehicle cruise control system”, Expert Systems with Applications, ELSEVIER, Vol. 40, pp. 1046-1053, 2013. • S. Hassan Hosseinnia, Inés Tejado, Vicente Milanés, Jorge Villagrá, and Blas M. Vinagre, “Experimental application of hybrid fractional-order adaptive cruise control at low speed”, Transactions on Control System Technology, IEEE, Vol. 22, no. 6, pp. 2329-2336, November 2014. • Yugong Luo, Tao Chen, Shuwei Zhang, and Keqiang Li, “Intelligent hybrid electric vehicle ACC with coordinated control of tracking ability, fuel economy, and ride comfort”, Transactions on Intelligent Transportation Systems, IEEE, Vol. 16, no. 4, pp. 2303-2308, August 2015. • Meng Wang, Winnie Daamen, Serge Hoogendoorn and Bart van Arem, “Potential impacts of ecological adaptive cruise control systems on traffic and environment”, IET Intelligent Transport Systems, Vol. 8, Iss. 2, pp. 77-86, November 2012. Design of ACC in LabVIEW Platform 21
  • 22. Contd… • Vibhor L. Bageshwar, William L. Garrard, and Rajesh Rajamani, “Model Predictive Control of Transitional Maneuvers for Adaptive Cruise Control Vehicles “, Transactions on Vehicular Technology, IEEE, Vol. 53, no. 5, pp. 1573-1585, September 2004. • Attila Trohak, Janos Vegh, Gergely Kovacs and Zsofia Forgacs, “Implementation of driving assistance systems using LabVIEW-based platform”, International Carpathian Control Conference, IEEE, pp. 548-551, May 2015. • K. Yi and J. Chung, “Nonlinear brake control for vehicle CW/CA systems,” IEEE/ASME Transaction Mechatron., Vol. 6, no. 1, pp. 17–25, March 2001. • Jullierme Emiliano Alves Dias, Guilherme Augusto Silva Pereira and Reinaldo Martinez Palhares, “Longitudinal model identification and velocity Control of an autonomous car”, Transactions on Intelligent Transportation Systems, IEEE, Vol. 16, no. 2, pp. 776-786, April 2015. • A. Kamga and A. Rachid, “Speed, steering angle and path tracking controls for a tricycle robot,” in Proc. IEEE Int. Symp. Comput. Aided Control Syst. Des., pp. 56–61, September 1996. • I. Tejado, V. Milanés, J. Villagrá, and B. M. Vinagre, “Fractional network based control for vehicle speed adaptation via vehicle-to infrastructure communications,” IEEE Trans. Control Syst. Technol., vol. 21, no. 3, pp. 780–790, May 2013. • I. Tejado, V. Milanés, J. Villagrá, J. Godoy, H. HosseinNia, and B. M. Vinagre, “Low speed control of an autonomous vehicle by using a fractional PI controller,” in Proc. 18th IFAC World Congr., vol. 18, 2011, pp. 15025–15030. • S. H. HosseinNia, I. Tejado, B. M. Vinagre, V. Milanés, and J. Villagrá, “Low speed control of an autonomous vehicle using a hybrid fractional order controller,” International Conference on Control, Instrumentation and Automation, IEEE, pp. 116–121, December 2011. Design of ACC in LabVIEW Platform 22
  • 23. Thank You !!! 23Design of ACC in LabVIEW Platform