Skip to main content
SpringerLink
Log in

Nonlinear and Adaptive Suboptimal Control of Connected Vehicles: A Global Adaptive Dynamic Programming Approach

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

Abstract

This paper studies the cooperative adaptive cruise control (CACC) problem of connected vehicles with unknown nonlinear dynamics. Different from the present literature on CACC, data-driven feedforward and optimal feedback control policies are developed by global adaptive dynamic programming (GADP). Due to the presence of nonvanishing disturbance, a modified version of GADP is presented. Interestingly, the developed policy is guaranteed to globally stabilize the vehicular platoon system, and is robust to unmeasurable nonvanishing disturbance. Numerical simulation results are presented to validate the effectiveness of the developed approach.

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. Angeli, D., Sontag, E.D.: Forward completeness, unboundedness observability, and their Lyapunov characterizations. Syst. Control Lett. 38(4-5), 209–217 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bamieh, B., Paganini, F., Dahleh, M.A.: Distributed control of spatially invariant systems. IEEE Trans. Autom. Control 47(7), 1091–1107 (2002)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  4. Gao, W., Huang, M., Jiang, Z.P., Chai, T.: Sampled data-based adaptive optimal output-feedback control of a 2-DOF helicopter IET Control Theory and Applications (2016). doi:10.1049/iet-cta.2015.0977

  5. Gao, W., Jiang, Y., Jiang, Z.P., Chai, T.: Output-feedback adaptive optimal control of interconnected systems based on robust adaptive dynamic programming. Automatica (2016). doi:10.1016/j.automatica.2016.05.008

  6. Gao, W., Jiang, Z.P.: Adaptive dynamic programming and adaptive optimal output regulation of linear systems. IEEE Trans. Autom. Control (2016). doi:10.1109/TAC.2016.2548662

  7. Gao, W., Jiang, Z.P., Ozbay, K.: Adaptive Optimal Control of Connected Vehicles. In: proceedings of the 10Th International Workshop on Robot Motion and Control, Pages 288–293, Poznan, Poland (2015)

  8. Ge, J.I., Orosz, G.: Optimal Control of Connected Vehicle Systems. In: proceedings of the 53Rd IEEE Conference on Decision and Control, Pages 4107–4112, Los Angeles, CA (2014)

  9. Horowitz, R., Varaiya, P.: Control design of an automated highway system. Proc. IEEE 88(7), 913–925 (2000)

    Article  Google Scholar 

  10. Ioannou, P.A., Chien, C.C.: Autonomous intelligent cruise control. IEEE Trans. Veh. Technol. 42(4), 657–672 (1993)

    Article  Google Scholar 

  11. Ioannou, P.A., Stefanovic, M.: Evaluation of ACC vehicles in mixed traffic: lane change effects and sensitivity analysis. IEEE Trans. Intell. Transp. Syst. 6(1), 79–89 (2005)

    Article  Google Scholar 

  12. Ioannou, P.A., Sun, J.: Robust Adaptive Control. Dover Publications, inc, mineola, NY (2012)

    MATH  Google Scholar 

  13. Jiang, Y., Jiang, Z.P.: Robust adaptive dynamic programming and feedback stabilization of nonlinear systems. IEEE Transactions on Neural Networks and Learning Systems 25(5), 882–893 (2014)

    Article  Google Scholar 

  14. Jiang, Y., Jiang, Z.P.: Global adaptive dynamic programming for continuous-time nonlinear systems. IEEE Trans. Autom. Control 60(11), 2917–2929 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  15. Jiang, Z.P., Jiang, Y.: Robust adaptive dynamic programming for linear and nonlinear systems: an overview. Eur. J. Control. 19(5), 417–425 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Jovanovic, M.R., Bamieh, B.: On the ill-posedness of certain vehicular platoon control problems. IEEE Trans. Autom. Control 50(9), 1307–1321 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  17. Kamalapurkar, R., Dinh, H., Bhasin, S., Dixon, W.E.: Approximate optimal trajectory tracking for continuous-time nonlinear systems. Automatica 51, 40–48 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  18. Kim, S.G., Tomizuka, M., Cheng, K.H.: Smooth motion control of the adaptive cruise control system by a virtual lead vehicle. Int. J. Automot. Technol. 13(1), 77–85 (2012)

    Article  Google Scholar 

  19. Kwon, J.-W., Chwa, D.: Adaptive bidirectional platoon control using a coupled sliding mode control method. IEEE Trans. Intell. Transp. Syst. 15(5), 2040–2048 (2014)

    Article  Google Scholar 

  20. Lewis, F.L., Vamvoudakis, K.G.: Reinforcement learning for partially observable dynamic processes Adaptive dynamic programming using measured output data. IEEE Trans. Syst. Man Cybern. B Cybern. 41(1), 14–25 (2011)

    Article  Google Scholar 

  21. Lewis, F.L., Vrabie, D.: Reinforcement learning and adaptive dynamic programming for feedback control. IEEE Circuits Syst. Mag. 9(3), 32–50 (2009)

    Article  Google Scholar 

  22. Lewis, F.L., Vrabie, D., Vamvoudakis, K.: Reinforcement learning and feedback control: Using natural decision methods to design optimal adaptive controllers. IEEE Control. Syst. Mag. 32(6), 76–105 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  23. Liu, T., Lu, X., Jiang, Z.P.: A junction-by-junction feedback-based strategy with convergence analysis for dynamic traffic assignment. Science China Information Sciences 59(1), 1–17 (2016)

    Article  Google Scholar 

  24. Oncu, S., Ploeg, J., van de Wouw, N., Nijmeijer, H.: Cooperative adaptive cruise control: Network-aware analysis of string stability. IEEE Trans. Intell. Transp. Syst. 15(4), 1527–1537 (2014)

    Article  Google Scholar 

  25. Ploeg, J., van de Wouw, N., Nijmeijer, H.: ℒp string stability of cascaded systems Application to vehicle platooning. IEEE Trans. Control Syst. Technol. 22(2), 786–793 (2014)

    Article  Google Scholar 

  26. Prajna, S., Papachristodoulou, A., Wu, F.: Nonlinear control synthesis by sum of squares optimization A Lyapunov-based approach. In: Asian Control Conference,volume 1, pp 157–165 (2004)

  27. Saridis, G.N., Lee, C.-S.G.: An approximation theory of optimal control for trainable manipulators. IEEE Trans. Syst. Man Cybern. 9(3), 152–159 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  28. Stankovic, S.S., Stanojevic, M.J., Siljak, D.D.: Decentralized overlapping control of a platoon of vehicles. IEEE Trans. Control Syst. Technol. 8(5), 816–832 (2000)

    Article  Google Scholar 

  29. Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning. MIT Press, Cambridge MA (1998)

    Book  MATH  Google Scholar 

  30. Swaroop, D., Hedrick, J.K., Choi, S.B.: Direct adaptive longitudinal control of vehicle platoons. IEEE Trans. Veh. Technol. 50(1), 150–161 (2001)

    Article  Google Scholar 

  31. Vamvoudakis, K.G.: Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems. IEEE/CAA Journal of Automatica Sinica 3, 282–293 (2014)

    Google Scholar 

  32. van Arem, B., van Driel, C., Visser, R.: The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans. Intell. Transp. Syst. 7(4), 429–436 (2006)

    Article  Google Scholar 

  33. Xiao, L., Gao, F.: A comprehensive review of the development of adaptive cruise control systems. Veh. Syst. Dyn. 48(10), 1167–1192 (2010)

    Article  Google Scholar 

  34. Xu, J., Xie, L., Wang, Y.: Simultaneous stabilization and robust control of polynomial nonlinear systems using SOS techniques. IEEE Trans. Autom. Control 54(8), 1892–1897 (2009)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, 11201, USA

    Weinan Gao & Zhong-Ping Jiang

Authors
  1. Weinan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong-Ping Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong-Ping Jiang.

Additional information

This work has been supported in part by the U.S. National Science Foundation grants ECCS-1230040 and ECCS-1501044.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, W., Jiang, ZP. Nonlinear and Adaptive Suboptimal Control of Connected Vehicles: A Global Adaptive Dynamic Programming Approach. J Intell Robot Syst 85, 597–611 (2017). https://doi.org/10.1007/s10846-016-0395-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-016-0395-3

Keywords

Search

Navigation