Skip to main content
SpringerLink
Log in

Simulation Validation of Three Nonlinear Model-Based Controllers in the Adaptive Cruise Control System

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

In this paper, the simulation validation of the hierarchical two-loop Adaptive Cruise Control (ACC) system is considered as a candidate for the application in the Advanced Driver Assistance Systems (ADAS), which aims to ensure driving safety and comfort as well as to improve fuel efficiency. Three different nonlinear model-based approaches for the inner-loop controllers are investigated for this system: the conventional Proportional-Integral Gain Scheduling controller (PI + GS) and two other strategies based on the simplified modelling of the vehicle dynamics: Balance-Based Adaptive Controller (B-BAC) and Nonlinear Model Predictive Controller (NMPC). The performance of each considered ACC system is tested by simulation under the same realistic scenarios for distance tracking mode and switching mode. The comparative criteria include driving safety, comfort of the driver and passengers and the fuel-economy aspect defined as BSFC (Brake Specific Fuel Consumption) index. The simulation results demonstrate that all the considered control algorithms meet the safety and car-following requirements while they provide slightly different level of driving comfort and fuel consumption, depending on the traffic situation and operating mode.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Xiao, L., Feng, G.: A comprehensive review of the development of adaptive cruise control system. Veh. Syst. Dyn. 48(10), 1167–1192 (2010)

    Article  Google Scholar 

  2. James, W.J., Neil, D.L., Steve, M., Scott, O., Brain, C.T.: Use of Advanced In-vehicle Technology by Young and Older Early Adopters. Survey Results on Adaptive Cruise Control Systems. Report No. DOT HS 810 917, National Highway Traffic Safety Administration (2008)

  3. Lattemann, F., Neiss, K., Terwen, S., Connolly, T.: The Predictive Cruise Control- A System to reduce Fuel Consumption of Heavy Duty Trucks. SAE World Congress. Detroit, MI, USA: SAE Technical Paper Series (2004)

  4. Rajamani, R., Tan, H.S., Law, B., Zhang, W.B.: Demonstration of integrated lateral and longitudinal control for the operation of automated vehicles in platoons. IEEE Trans. Contr. Syst. Technol. 8, 695–708 (2000)

    Article  Google Scholar 

  5. Rajamani, R., Zhu, C.: Semi autonomous adaptive cruise control systems. IEEE Trans. Veh. Technol. 51, 1186–1192 (2002)

  6. Vahidi, A., Eskandarian, A.: Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Trans. Intell. Transp. Syst. 4(3), 143–153 (2003)

  7. Hellström, E., Ivarsson, M., Åslund, J., Nielsen, L.: Look-ahead control of heavy trucks to minimize trip time and fuel consumption. In: 5th Symposium on Advances in Automotive Control. Monterey, CA, USA (2007)

  8. Luo, L., Liu, H., Li, P., Wang, H.: Model predictive control for adaptive cruise control with multi-objectives: comfort, fuel-economy, safety and car-following. Appl. Phys. Eng. 11(3), 191–201 (2010)

    MATH  Google Scholar 

  9. Marsden, G., McDonald, M., Brackstone, M.: Towards an understanding of adaptive cruise control algorithm. Veh. Syst. Dyn. 46(8), 661–690 (2011)

    Google Scholar 

  10. Brackstone, M., McDonald, M.: The role of the instrumented vehicle in the collection of data on driver behaviour. In: Proceedings of the IEE Colloquium on Monitoring of Driver and Vehicle Performance. Digest No. 97/122 (1997)

  11. Brookhuis, K.A., De Waard, D.: The human factor in advanced driver assistance systems. In: Proceedings of the Advanced Driver Assistance Systems (ADAS): Vehicle Control for the Future Seminar, pp. 59–65. IMechE, London, UK (1999)

  12. Bifulco, G.N., Pariota, L., Simonelli, F., Pace, R.D.: Development and testing of a fully adaptive cruise control system. Transp. Res. Part C Emerg. Technol. 29, 156–170 (2013)

    Article  Google Scholar 

  13. Martinez, J.J., Canudas-de-Wit, C.: A safe longitudinal control for adaptive cruise control and stop-and-go scenarios. IEEE Trans. Control Syst. Technol. 15(2), 246–258 (2007)

  14. Milanés, V., Villagrá, J., Godoy, J., González, C.: Comparing fuzzy and intelligent PI Controllers in stop-and-go manoeuvres. IEEE Trans. Control Syst. Technol. 20(3), 770–778 (2012)

    Article  Google Scholar 

  15. Naranjo, J., González, C., Garcia, R., de Pedro, T.: ACC. + Stop&go maneuvers with throttle and brake fuzzy control. IEEE Trans. Intell. Trans. Syst. 7(2), 213–225 (2006)

    Article  Google Scholar 

  16. Nouveliere, L., Mammar, S.: Experimental vehicle longitudinal control using a second order sliding mode technique. Control. Eng. Pract. 15(8), 943–954 (2007)

    Article  Google Scholar 

  17. Gerdes, J.C., Hedrick, J.K.: Vehicle speed and spacing control via coordinated throttle and brake actuation. Control. Eng. Pract. 5, 1607–1614 (1997)

    Article  Google Scholar 

  18. Cherian, M., Sathiyan, S.P.: Neural network based ACC for optimized safety and comfort. Int. J. Comput. Appl. 42(14), 1–4 (2012)

    Google Scholar 

  19. Desjardins, C., Chaib-draa, B.: Cooperative adaptive cruise control: A reinforcement learning approach. IEEE Trans. Intell. Trans. Syst. 12(4), 1248–1260 (2011)

    Article  Google Scholar 

  20. Shakouri, P., Ordys, A., Collier, G.: Robotic implementation of the adaptive cruise control: Comparison of three control methods. In: Proceedings of International Conference of Mechatronics, pp. 633–640. Springer, Brno, Czech Republic (2013)

  21. Bengtsson, J.: Adaptive Cruise Control and Driver Modelling. MSc thesis, Lund University, Sweden (2001)

    Google Scholar 

  22. Shakouri, P., Ordys, A., Laila, D.S., Askari, M.R.: Adaptive Cruise Control System: Comparing Gain-Scheduling PI and LQ Controllers. 18th IFAC World Congress. Milano, Italy (2011)

  23. Riis, P.: Adaptive Cruise Controller Simulation as an Embedded Distributed System. MSc thesis, Linkoping University, Sweden (2007)

    Google Scholar 

  24. Bleek van den, R.A.P.M.: Design of a Hybrid Adaptive Cruise Control Stop-&-Go system. Master’s Thesis, TNO Science & Industry, Technische Universiteit Eindhoven (2007)

  25. Jonsson, J.: Fuel Optimized Predictive Following in Low Speed Condition. Master’s thesis, Linkopings University, Sweden (2003)

    Google Scholar 

  26. Shakouri, P.: Designing of the Adaptive Cruise Control System- Switching Controller. PhD Thesis, Kingston University of London, UK (2012)

    Google Scholar 

  27. Shakouri, P., Ordys, A., Askari, M.R.: Adaptive cruise control with stop & go function using the state-dependent nonlinear model predictive control approach. ISA Trans. 51(5), 622–631 (2012)

    Article  Google Scholar 

  28. Shakouri, P., Ordys, A.: Nonlinear model predictive control approach in design of adaptive cruise control with automated switching to cruise control. Control. Eng. Pract. 26, 160–177 (2014)

    Article  Google Scholar 

  29. Czeczot, J.: Model-based adaptive predictive control of fed-batch fermentation process with the substrate consumption rate application. In: Proceedings of IFAC Workshop on Adaptive Systems in Control and Signal Processing, pp. 357–362. University of Strathclyde, Glasgow, Scotland, UK (1998)

  30. Czeczot, J.: Balance-based adaptive control of a neutralization process. Int. J. Control. 79(12), 1581–1600 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  31. Deng, J., Becerra, V.M., Stobart, R.: Predictive control using feedback linearization based on dynamic neutral network. In: IEEE International Conference on Systems. Man and Cybernetics (ISIC 2007), Monteral, Canada (2007)

  32. Deng, J., Becerra, V.M., Stobart, R.: Input constraints handling in an MPC/feedback linearization scheme. Int. J. Appl. Math. Comput. Sci. 15, 219–232 (2009)

    MathSciNet  Google Scholar 

  33. Corona, D., Lazar, M., De Schutter, B., Heemels, M.: A hybrid MPC approach to the design of a Smart adaptive cruise controller. In: Proceedings of the 2006 IEEE International Conference on Control Applications (CCA 2006), pp. 231–236. Munich, Germany (2006)

  34. Corona, D., Necoara, I., De Schutter, B., Van den Boom, T.: Robust hybrid MPC applied to the design of an adaptive cruise controller for a road vehicle. In: Proceedings of the 45th IEEE Conference on Decision and Control, pp. 1721–1726. San Diego, California (2006)

  35. Corona, D., De Schutter, B.: Comparison of a linear and a hybrid adaptive cruise controller for a SMART. In: Proceedings of the 46 IEEE Conference on Decision and Control, pp. 4779–4784. New Orleans, Louisiana (2007)

  36. Dutka, A.S., Ordys, A., Grimble, M.J.: Optimized Discrete-Time State Dependent Riccati Equation Regulator. American Control Conference, Portland, OR, USA (2005)

    Book  Google Scholar 

  37. Youssef, A.M., Ordys, A., Grimble, M.J.: Nonlinear predictive control for fast constrained systems. In: IEEE Conference on Methods and Models in Automation and Robotics. Miedzyzdroje, Poland (2004)

  38. Kouvariatkis, B., Canon, M., Rossiter, J.A.: Nonlinear model based predictive control. Int. J. Control 72, 919–928 (1999)

    Article  Google Scholar 

  39. Shakouri, P., Ordys, A.: Application of the state-dependent nonlinear model predictive control in adaptive cruise control system. In: Proceedings of 14th International IEEE Conference on Intelligent Transportation Systems - ITSC 2011. Washington, DC, USA (2011)

  40. Czeczot, J.: Modelling for the effective control of the electric flow heaters – simulation validation. Simul. Model. Pract. Theory 16, 429–444 (2008)

    Article  Google Scholar 

  41. Czeczot, J., Laszczyk, P., Metzger, M.: Local balance-based adaptive control in the heat distribution system – practical validation. Appl. Therm. Eng. 30(8–9), 879–891 (2010)

    Article  Google Scholar 

  42. Shakouri, P., Ordys, A., Askari, M.R., Laila, D.S.: Longitudinal vehicle dynamics using Simulink/Matlab. In: Proceedings of UKACC International Conference on CONTROL. Coventry (2010)

  43. Wang, J.Y., 3rd edn: Theory of Ground Vehicle. Wiley Inter Science (2001)

  44. Short, M., Pont, M.J., Huang, Q.: Simulation of Vehicle Longitudinal Dynamic. Embedded System Laboratory University of Leicester, Safety and Reliability of Distributed Embedded Systems. Leicester (2004)

  45. Zhou, W., Zhang, S.: Analysis of distance headways. In: Proceedings Of the Eastern Asia Society for Transportation Studies (2003)

  46. Isidori, A.: Nonlinear Control Systems. Springer-Verlag, New York (1989)

    Book  MATH  Google Scholar 

  47. Bastin, G., Dochain, D.: On-line Estimation and Adaptive Control of Bioreactors. Elsevier Science Publishers B.V (1990)

  48. Rhinehart, R.R., Riggs, J.B.: Process control through nonlinear modeling. Control 3(7), 86 (1990)

    Google Scholar 

  49. Wang, L., 2nd edn: Model Predictive Control System Design and Implementation Using Matlab. Springer (2009)

  50. Moon, S., Moon, I., Yi, K.: Comfort evaluation of adaptive cruise control (ACC): A comparison between two different systems with collision avoidance. Control. Eng. Pract. 17, 442–455 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mechanical and Automotive Engineering, Kingston University London, Roehampton Vale Campus, Friars Avenue, London, SW15 3DW, UK

    Payman Shakouri

  2. Department of Systems Engineering, Military Technological College, Muscat, Oman

    Andrzej Ordys

  3. Institute of Automatic Control, Silesian University of Technology, ul. Akademicka 16, 44-100, Gliwice, Poland

    Jacek Czeczot

Authors
  1. Payman Shakouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacek Czeczot
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrzej Ordys
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Payman Shakouri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shakouri, P., Czeczot, J. & Ordys, A. Simulation Validation of Three Nonlinear Model-Based Controllers in the Adaptive Cruise Control System. J Intell Robot Syst 80, 207–229 (2015). https://doi.org/10.1007/s10846-014-0128-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-014-0128-4

Keywords

Search

Navigation