Skip to main content
Log in

Distributed fixed-time NN tracking control of vehicular platoon systems with singularity-free

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper focuses on a distributed adaptive singularity-free fixed-time neural network tracking control problem for vehicular platoon with model uncertainties. The reference trajectory of platoon is modeled based on the actual driving conditions including four stages. Moreover, the adaptive neural network and \(H_\infty \) control theory are adopted to tackle unknown nonlinearities and mismatched complete disturbances of third-order vehicle dynamics. Bying integrating fixed-time control with backstepping technology, a distributed adaptive singularity-free fixed-time control protocol is constructed. Meanwhile, a smooth switching function is designed to effectively deal with the conventional fixed-time singularity problem caused by differentiation of a virtual control law. Compared with the existing results, both cases of the designed switching function are practically fixed-time stable. Finally, the effectiveness of the presented control strategy is further attested by simulation experiments of four different scenarios that may take place in actual traffic, including simulation comparisons and one noise analysis.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Shaout A, Jarrah M (1997) Cruise control technology review. Comput Electr Eng 23(4):259–271

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Eom H, Lee SH (2015) Human–automation interaction design for adaptive cruise control systems of ground vehicles. Sensors 15(6):13916–13944

    Article  Google Scholar 

  4. Li S, Li K, Rajamani R, Wang J (2010) Model predictive multi-objective vehicular adaptive cruise control. IEEE Trans Control Syst Technol 19(3):556–566

    Article  Google Scholar 

  5. Donà R, Mattas K, He Y, Albano G, Ciuffo B (2022) Multianticipation for string stable adaptive cruise control and increased motorway capacity without vehicle-to-vehicle communication. Transp Res Part C: Emerg Technol 140:103687

    Article  Google Scholar 

  6. Vite L, Juárez L, Gomez MA, Mondié S (2022) Dynamic predictor-based adaptive cruise control. J Franklin Inst. https://doi.org/10.1016/j.jfranklin.2022.06.006

    Article  MathSciNet  MATH  Google Scholar 

  7. Berger T, Rauert A-L (2020) Funnel cruise control. Automatica 119:109061

    Article  MathSciNet  MATH  Google Scholar 

  8. Luo Y, Chen T, Zhang S, Li K (2015) Intelligent hybrid electric vehicle ACC with coordinated control of tracking ability, fuel economy, and ride comfort. IEEE Trans Intell Transp Syst 16(4):2303–2308

    Article  Google Scholar 

  9. Dai S, Koutsoukos X (2020) Safety analysis of integrated adaptive cruise and lane keeping control using multi-modal port-hamiltonian systems. Nonlinear Anal Hybrid Syst 35:100816

    Article  MathSciNet  MATH  Google Scholar 

  10. Flores C, Spring J, Nelson D, Iliev S, Lu XY (2022) Enabling cooperative adaptive cruise control on strings of vehicles with heterogeneous dynamics and powertrains. Veh Syst Dyn. https://doi.org/10.1080/00423114.2022.2042568

    Article  Google Scholar 

  11. Zhu Y, Zhao D, He H (2020) Synthesis of cooperative adaptive cruise control with feedforward strategies. IEEE Trans Veh Technol 69(4):3615–3627

    Article  Google Scholar 

  12. Ploeg J, Semsar-Kazerooni E, Lijster G, van de Wouw N, Nijmeijer H (2014) Graceful degradation of cooperative adaptive cruise control. IEEE Trans Intell Transp Syst 16(1):488–497

    Article  Google Scholar 

  13. Ma Y, Li Z, Malekian R, Zhang R, Song X, Sotelo MA (2018) Hierarchical fuzzy logic-based variable structure control for vehicles platooning. IEEE Trans Intell Transp Syst 20(4):1329–1340

    Article  Google Scholar 

  14. Nie L, Guan J, Lu C, Zheng H, Yin Z (2018) Longitudinal speed control of autonomous vehicle based on a self-adaptive PID of radial basis function neural network. IET Intel Transp Syst 12(6):485–494

    Article  Google Scholar 

  15. Khanapuri E, Chintalapati VVTK, Sharma R, Gerdes R (2021) Learning based longitudinal vehicle platooning threat detection, identification and mitigation. IEEE Trans Intell Veh. https://doi.org/10.1109/TIV.2021.3122144

    Article  Google Scholar 

  16. Li S, He J, Li Y, Rafique MU (2016) Distributed recurrent neural networks for cooperative control of manipulators: a game-theoretic perspective. IEEE Trans Neural Netw Learn Syst 28(2):415–426

    Article  MathSciNet  Google Scholar 

  17. Li S, Kong R, Guo Y (2014) Cooperative distributed source seeking by multiple robots: algorithms and experiments. IEEE/ASME Trans Mechatron 19(6):1810–1820

    Article  Google Scholar 

  18. Li S, Zhou M, Luo X, You Z-H (2016) Distributed winner-take-all in dynamic networks. IEEE Trans Autom Control 62(2):577–589

    Article  MathSciNet  MATH  Google Scholar 

  19. Li S, Qin F (2013) A dynamic neural network approach for solving nonlinear inequalities defined on a graph and its application to distributed, routing-free, range-free localization of wsns. Neurocomputing 117:72–80

    Article  Google Scholar 

  20. Li S, Guo Y (2012) Distributed source seeking by cooperative robots: all-to-all and limited communications. In: 2012 IEEE international conference on robotics and automation, pp 1107–1112 IEEE

  21. Li S, Wang Z, Li Y (2013) Using laplacian eigenmap as heuristic information to solve nonlinear constraints defined on a graph and its application in distributed range-free localization of wireless sensor networks. Neural Process Lett 37(3):411–424

    Article  Google Scholar 

  22. Li S, Chen S, Liu B (2013) Accelerating a recurrent neural network to finite-time convergence for solving time-varying Sylvester equation by using a sign-bi-power activation function. Neural Process Lett 37(2):189–205

    Article  Google Scholar 

  23. Liu Y, Yao D, Li H, Lu R (2021) Distributed cooperative compound tracking control for a platoon of vehicles with adaptive nn. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3044883

    Article  Google Scholar 

  24. He X, Hashemi E, Johansson KH (2021) Distributed control under compromised measurements: resilient estimation, attack detection, and vehicle platooning. Automatica 134:109953

    Article  MathSciNet  MATH  Google Scholar 

  25. Liu Y, Li H, Zuo Z, Li X, Lu R (2022) An overview of finite/fixed-time control and its application in engineering systems. IEEE/CAA J Autom Sin. https://doi.org/10.1109/JAS.2022.105413

    Article  Google Scholar 

  26. Caiazzo B, Lui DG, Petrillo A, Santini S (2021) Distributed double-layer control for coordination of multiplatoons approaching road restriction in the presence of IoV communication delays. IEEE Internet Things J 9(6):4090–4109

    Article  Google Scholar 

  27. Li Y-X (2019) Finite time command filtered adaptive fault tolerant control for a class of uncertain nonlinear systems. Automatica 106:117–123

    Article  MathSciNet  MATH  Google Scholar 

  28. Xu B, Zhang Q, Pan Y (2016) Neural network based dynamic surface control of hypersonic flight dynamics using small-gain theorem. Neurocomputing 173:690–699

    Article  Google Scholar 

  29. Liu Y, Zhu Q, Zhao N, Wang L (2021) Fuzzy approximation-based adaptive finite-time control for nonstrict feedback nonlinear systems with state constraints. Inf Sci 548:101–117

    Article  MathSciNet  MATH  Google Scholar 

  30. Nguyen NP, Oh H, Moon J (2022) Continuous nonsingular terminal sliding mode control with integral-type sliding surface for disturbed systems: application to attitude control for quadrotor UAVs under external disturbances. IEEE Trans Aerosp Electron Syst. https://doi.org/10.1109/TAES.2022.3177580

    Article  Google Scholar 

  31. Zhu Y, Zhu F (2018) Distributed adaptive longitudinal control for uncertain third-order vehicle platoon in a networked environment. IEEE Trans Veh Technol 67(10):9183–9197

    Article  Google Scholar 

  32. Guo G, Li D (2019) Adaptive sliding mode control of vehicular platoons with prescribed tracking performance. IEEE Trans Veh Technol 68(8):7511–7520

    Article  Google Scholar 

  33. Guo G, Li P, Hao L-Y (2020) Adaptive fault-tolerant control of platoons with guaranteed traffic flow stability. IEEE Trans Veh Technol 69(7):6916–6927

    Article  Google Scholar 

  34. Xiao W, Ren H, Zhou Q, Li H, Lu R (2021) Distributed finite-time containment control for nonlinear multiagent systems with mismatched disturbances. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3042168

    Article  Google Scholar 

  35. Ba D, Li Y-X, Tong S (2019) Fixed-time adaptive neural tracking control for a class of uncertain nonstrict nonlinear systems. Neurocomputing 363:273–280

    Article  Google Scholar 

  36. Yang H, Ye D (2018) Adaptive fixed-time bipartite tracking consensus control for unknown nonlinear multi-agent systems: an information classification mechanism. Inf Sci 459:238–254

    Article  MathSciNet  MATH  Google Scholar 

  37. Wang F, Lai G (2020) Fixed-time control design for nonlinear uncertain systems via adaptive method. Syst Control Lett 140:104704

    Article  MathSciNet  MATH  Google Scholar 

  38. Zuo Z (2015) Nonsingular fixed-time consensus tracking for second-order multi-agent networks. Automatica 54:305–309

    Article  MathSciNet  MATH  Google Scholar 

  39. Zhang T, Xia M, Yi Y (2017) Adaptive neural dynamic surface control of strict-feedback nonlinear systems with full state constraints and unmodeled dynamics. Automatica 81:232–239

    Article  MathSciNet  MATH  Google Scholar 

  40. Liu Y, Liu X, Jing Y, Zhang Z (2021) Semi-globally practical finite-time stability for uncertain nonlinear systems based on dynamic surface control. Int J Control 94(2):476–485

    Article  MathSciNet  MATH  Google Scholar 

  41. Zhang J-X, Yang G-H (2016) Robust adaptive fault-tolerant control for a class of unknown nonlinear systems. IEEE Trans Ind Electron 64(1):585–594

    Article  Google Scholar 

  42. Balogh T, Boussaada I, Insperger T, Niculescu S-I (2022) Conditions for stabilizability of time-delay systems with real-rooted plant. Int J Robust Nonlinear Control 32(6):3206–3224

    Article  MathSciNet  Google Scholar 

  43. Ghiti Sarand H, Karimi B (2019) Adaptive consensus tracking of non-square mimo nonlinear systems with input saturation and input gain matrix under directed graph. Neural Comput Appl 31(7):2171–2182

    Article  Google Scholar 

  44. Razaq MA, Rehan M, Ahn CK, Hong K-S (2022) Observer-based relative-output feedback consensus of one-sided lipschitz multi-agent systems subjected to switching graphs. IEEE Trans Control Netw Syst. https://doi.org/10.1109/TCNS.2022.3181526

    Article  Google Scholar 

  45. Jia T, Pan Y, Liang H, Lam H-K (2021) Event-based adaptive fixed-time fuzzy control for active vehicle suspension systems with time-varying displacement constraint. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2021.3075490

    Article  Google Scholar 

  46. Karaki BJ, Mahmoud MS (2022) Event-triggered leader-following consensus for a class of nonlinear multiagent systems with time-varying delay. Int J Robust Nonlinear Control 32(6):3314–3333

    Article  MathSciNet  Google Scholar 

  47. Yao D, Li H, Lu R, Shi Y (2022) Event-triggered guaranteed cost leader-following consensus control of second-order nonlinear multiagent systems. IEEE Trans Syst Man Cybern Syst 52(4):2615–2624

    Article  Google Scholar 

  48. Cao L, Yao D, Li H, Meng W, Lu R (2022) Fuzzy-based dynamic event triggering formation control for nonstrict-feedback nonlinear mass. Fuzzy Sets Syst. https://doi.org/10.1016/j.fss.2022.03.005

    Article  Google Scholar 

  49. Pan Y, Wu Y, Lam H-K (2022) Security-based fuzzy control for nonlinear networked control systems with dos attacks via a resilient event-triggered scheme. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2022.3148875

    Article  Google Scholar 

  50. Cavagnari G, Lisini S, Orrieri C, Savaré G (2022) Lagrangian, Eulerian and Kantorovich formulations of multi-agent optimal control problems: equivalence and gamma-convergence. J Differ Equ 322:268–364

    Article  MathSciNet  MATH  Google Scholar 

  51. Sun J, Zhang H, Wang Y, Fu M (2021) Optimal tracking control of switched systems applied in grid-connected hybrid generation using reinforcement learning. Neural Comput Appl 33(15):9363–9374

    Article  Google Scholar 

  52. Pan Y, Li Q, Liang H, Lam H-K (2021) A novel mixed control approach for fuzzy systems via membership functions online learning policy. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2021.3130201

    Article  Google Scholar 

  53. Li H, Wu Y, Chen M, Lu R (2021) Adaptive multigradient recursive reinforcement learning event-triggered tracking control for multiagent systems. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3090570

    Article  Google Scholar 

  54. Wen X, Wang Y, Qin S (2021) A nonautonomous-differential-inclusion neurodynamic approach for nonsmooth distributed optimization on multi-agent systems. Neural Comput Appl. https://doi.org/10.1007/s00521-021-06026-2

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grant 62003097, 62103214. In part by Talent Introduction and Cultivation Plan for Youth Innovation of Universities in Shandong Province.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yang Liu.

Ethics declarations

Conflict of interest

The authors declare that there are no potential conflicts of interest regarding the publication of this work. And there are no financial and personal relationships with other people or organizations that can inappropriately influence our work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, J., Liu, Y., Sun, J. et al. Distributed fixed-time NN tracking control of vehicular platoon systems with singularity-free. Neural Comput & Applic 35, 2527–2540 (2023). https://doi.org/10.1007/s00521-022-07725-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07725-0

Keywords

Navigation