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Arabian Journal for Science and Engineering

, Volume 44, Issue 3, pp 2103–2113 | Cite as

Switching Control Paradigms for Adaptive Cruise Control System with Stop-and-Go Scenario

  • Z. HaroonEmail author
  • B. Khan
  • U. Farid
  • S. M. Ali
  • C. A. Mehmood
Research Article - Electrical Engineering
  • 35 Downloads

Abstract

The adaptive cruise control (ACC) system is currently one of the most common research topics in automotive industry. The ACC system tracks the velocity of preceding vehicle by adjusting the throttle angle and applying brake, whenever needed. This system is acknowledged for improving the fuel efficiency due to coordination between brake and throttle. Inappropriate switching between brake and throttle results in loss of energy that increases fuel consumption. To address aforesaid problem, novel coordinated switching controllers for the ACC system are proposed that enhances the fuel efficiency. Moreover, the proposed control strategies are compared for various traffic scenarios, and then, fuel economy is observed for the proposed control schemes. Fuel economy is investigated using switching control paradigm design; namely, the proportional–integral–derivative (PID) controller, adaptive proportional–integral–derivative (APID) controller, and fuzzy PID controller for actual nonlinear and linearized model of the ACC system for various traffic scenarios including stop and proposed control strategies are compared using performance indices for various traffic scenarios, such as CC, ACC, and ACC stop and go in order to show the validity of the design. Furthermore, the comparison among the PID, APID, and fuzzy PID control schemes is investigated to analyze fuel economy of the actual nonlinear CC and ACC system.

Keywords

Adaptive cruise control (ACC) Adaptive proportional integral derivative (APID) Fuel economy Fuzzy PID Proportional integral derivative (PID) Stop and go (SG) 

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References

  1. 1.
    Metz, B.; Davidson, O.; Martens, J.-W.; Rooijen, S.V.; Mcgrory, L.V.W.: Methodological and Technological Issues in Technology Transfer. Cambridge University Press, Cambridge (2000)Google Scholar
  2. 2.
    Sperling, D.; Clausen, E.: The Developing World’s Motorization Challenge. University of California Transportation Center, Berkeley (2002)Google Scholar
  3. 3.
    Bin, Y.; Li., K; Feng, N.: Modelling and Nonlinear Robust Control of Longitudinal Vehicle Advanced ACC Systems. In: Bartoszewicz, A. (ed.) Challenges and Paradigms in Applied Robust Control. pp. 113-146. ISBN (2011)Google Scholar
  4. 4.
    Guvenc, B.A.; Kural, E.: Adaptive cruise control simulator: a low-cost, multiple-driver-in-the-loop simulator. IEEE Control Syst. 26(3), 42–55 (2006)CrossRefGoogle Scholar
  5. 5.
    Aguilar-Ibañez, C.: Stabilization of the pvtol aircraft based on a sliding mode and a saturation function. Int. J. Robust Nonlinear Control 27, 843–859 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Rubio, J.J.: Robust feedback linearization for nonlinear processes control. ISA Trans. (2018).  https://doi.org/10.1016/j.isatra.2018.01.017 Google Scholar
  7. 7.
    Aguilar-Ibanez, C.; Sira-Ramirez, H.; Suarez-Castanon, M.S.: A linear active disturbance rejection control for a ball and rigid triangle system. Math. Prob. Eng. 2016, 1–11 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Naranjo, J.E.; Gonzalez, C.; Reviejo, J.; Garcia, R.; de Pedro, T.: Adaptive fuzzy control for inter-vehicle gap keeping. IEEE Trans. Intell. Transp. Syst. 4(3), 132–142 (2003)CrossRefGoogle Scholar
  9. 9.
    Ganji, B.; Kouzani, Z.A.; Khoo, S.Y.; Shams-Zahraei, M.: Adaptive cruise control of a HEV using sliding mode control. Exp. Syst. Appl. Int. J. 41(2), 607–615 (2014)CrossRefGoogle Scholar
  10. 10.
    Rubio, J.J.; Soriano, E.; Juarez, C.F.; Pacheco, J.: Sliding mode regulator for the perturbations attenuation in two tank plants. IEEE Access 5(1), 20504–20511 (2017)CrossRefGoogle Scholar
  11. 11.
    Jesús, R.: Sliding mode control of robotic arms with deadzone. IET Control Theory Appl. 11(8), 1214–1221 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hassan, A.; Collier, G.: Mechatronics 2013. Adaptive Cruise Control for a Robotic Vehicle Using Fuzzy Logic. Springer, Cham (2014)Google Scholar
  13. 13.
    Thanok, S.; Parnichkun, M.: Adaptive cruise control of a passenger car using hybridof sliding mode control and fuzzy logic control. In: Paper presented at the international conference on automotive engineering (ICAE-8), ThailandGoogle Scholar
  14. 14.
    Desjardins, C.; Chaib-draa, B.: Cooperative adaptive cruise control: a reinforcement learning approach. IEEE Trans. Intell. Transp. Syst. 12(4), 1248–1260 (2011)CrossRefGoogle Scholar
  15. 15.
    Cherian, M.; Sathiyan, S.P.: Neural Network based ACC for optimized safety and comfort. Int. J. Comput. Appl. (IJCA) 42(14), 0975–8887 (2012)Google Scholar
  16. 16.
    Deka, J.; Haloi, R.: Study of effect of P, PI controller on car cruise control system and security. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. (IJAREEIE) 3(6), 9817–9822 (2014)Google Scholar
  17. 17.
    Sivaji, V.V.; Sailaja, D.M.: Adaptive cruise control systems for vehicle modeling using stop and go manoeuvres. Int. J. Eng. Res. Appl. (IJERA) 3(4), 2453–2456 (2013)Google Scholar
  18. 18.
    Shakouri, P.; Ordys, A.; Laila, D.S; Askari, M.: Adaptive cruise control system:comparing gain-scheduling PI and LQ controllers. In: International federation of automatic control (IFAC), Milano, Italy, August 28–September 2 (2011)Google Scholar
  19. 19.
    Ko, S.-J.; KAISTz D.; Lee, J.-J.: Fuzzy logic based adaptive cruise control with guaranteed string stability. In: Paper presented at the international conference on control, automation and systems, 2007. ICCAS ’07, Seoul 17–20 October (2007)Google Scholar
  20. 20.
    Gong, L.; Luo, L.; Wang, H.; Liu, H.: Adaptive cruise control design based on fuzzy-PID. In: Paper presented at the international conference on E-product E-service and E-entertainment (ICEEE), 2010, Henan, 7–9 Nov (2010)Google Scholar
  21. 21.
    Shakouri, P.; Ordys, A.; Darnell, P.; Kavanagh, P.: Fuel efficiency by coasting in the vehicle. Int. J. Veh. Technol. 2013, 14 (2013)Google Scholar
  22. 22.
    Lee, J.: Vehicle Inertia Impact on Fuel Consumption of Conventional and Hybrid Electric Vehicle Using Acceleration and Coast Driving Strategy. Virgina Polytechnic Institute and State University, Blacksburg (2009)CrossRefGoogle Scholar
  23. 23.
    Rajamani, R.: Vehicle Dynamics and Control. Mechanical Engineering Series. Springer, New York (2006)zbMATHGoogle Scholar
  24. 24.
    Payman, S.; Andrzej, O.: Nonlinear model predictive control approach in design of adaptive cruise control with automated switching to cruise control. Control Eng. Pract. 26, 160–177 (2014)CrossRefGoogle Scholar
  25. 25.
    Fossati, A.; Schonmann, P.; Fua, P.: Real-time vehicle tracking for driving assistance. Mach. Vis. Appl. 22, 439–448 (2011)CrossRefGoogle Scholar
  26. 26.
    Karnfelt, C.; Peden, A.; Bazzi, A.; El Haj Shhade, G.: 77 GHz ACC radar simulation platform. In: Paper presented at the intelligent transport systems telecommunications (ITST), Lille 20–22 OctGoogle Scholar
  27. 27.
    Hofmann, P.: Object Detection and Tracking with Side Cameras and Radar in an Automotive Context. Free University of Berlin, Berlin (2013)Google Scholar
  28. 28.
    Martinez, J.-J.; Canudas-de-Wit, C.: A safe longitudinal control for adaptive cruise control and stop-and-go scenarios control systems technology. IEEE Trans. 15(2), 246–258 (2007)Google Scholar
  29. 29.
    Wong, J.Y.: Theory of Ground Vehicles, 3rd edn. Wiley, New York (2001)Google Scholar
  30. 30.
    Ma, R.; Kaber, D.B.: Situation awareness and workload in driving while using adaptive cruise control and a cell phone. Int. J. Ind. Ergon. 35, 939–953 (2005)CrossRefGoogle Scholar
  31. 31.
    Shakouri, P.: Designing of the Adaptive Cruise Control System-Switching Controller. Kingston University London, London (2012)Google Scholar
  32. 32.
    Short, M.; Pont, J.M.; Huang, Q.: Safety and reliability of distributed embeded systems. In: University of Leicester, Embedded systems laboratory (2004)Google Scholar
  33. 33.
    Zulfatman,; Rahmat, M.F.: Application of self-tuning fuzzy PID controller on industrial hydraulic actuator using system identification approach. Int. J. Smart Sens. Intell. Syst. 2(2), 246–261 (2009)Google Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Z. Haroon
    • 1
    Email author
  • B. Khan
    • 1
  • U. Farid
    • 1
  • S. M. Ali
    • 1
  • C. A. Mehmood
    • 1
  1. 1.Department of Electrical EngineeringCOMSATS Institute of Information TechnologyAbbottabadPakistan

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