Skip to main content
Log in

Implementation and Development of a Trajectory Tracking Control System for Intelligent Vehicle

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

Abstract

In this paper, a trajectory tracking control system, which consists of a model predictive control unit and an active safety steering control unit, has been developed. A nonlinear bicycle vehicle model, including the longitudinal, lateral, yaw, and quasi-static roll motions, was derived as a predictive model to simulate and test the proposed model predictive control (MPC) system. A 4-DOF vehicle model was used to reflect the characteristics of vehicle dynamics to avoid rollover accidents of automobiles. Simulation was performed and experiment results demonstrated good performance of both MPC unit and active safety steering control unit. Finally, it was proved that the proposed trajectory tracking control system is easy to realize with low cost.

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.

Similar content being viewed by others

References

  1. Sun, Y., Xiong, G.M., Chen, H.Y.: Evaluation of the intelligent behaviors of unmanned ground vehicles based on fuzzy-EAHP scheme. J. Autom. Eng. 36, 22–27 (2014)

    Google Scholar 

  2. Sai, S., Altintas, O., Kenney, J., et al.: Current and future ITS. IEICE Trans. Inf. Syst. 38(2), 176–183 (2013)

    Article  Google Scholar 

  3. Bell, M.G.H., Kaparias, I., Nocera, S., et al.: Presence of urban ITS architectures in Europe: results of a recent survey. Ingegneria Ferroviaria 67.5, 447–467 (2012)

    Google Scholar 

  4. Zuo, Z., Wang, C.: Adaptive trajectory tracking control of output constrained multi-rotors systems. Control Theory Appl. Iet 8.13, 1163–1174 (2014)

    Article  Google Scholar 

  5. Xu, R., Özgüner, Ü: Brief paper: sliding mode control of a class of underactuated systems. Automatica 44.1, 233–241 (2008)

    Article  MATH  Google Scholar 

  6. Leitner, J., Calise, A., Prasad, J.V.R.: Analysis of adaptive neural networks for helicopter flight control. J. Guid. Control. Dyn. 68.2, 251–261 (2012)

    MATH  Google Scholar 

  7. Schoellig, A.P., Mueller, F.L., D’Andrea, R.: Optimization-based iterative learning for precise quadrocopter trajectory tracking. Auton. Robot. 33, 103–127 (2012)

    Article  Google Scholar 

  8. Graichen, K., Kugi, A.: Stability and incremental improvement of suboptimal MPC without terminal constraints. IEEE Trans. Autom. Control 55, 2576–2580 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Liu, J., Jayakumar, P., Overholt, J.L., et al.: The role of model fidelity in model predictive control based hazard avoidance in unmanned ground vehicles using LIDAR sensors. Dynamic Systems and Control Conference, pp. V003T46A005 (2013)

  10. Falcone, P., Borrelli, F., Asgari, J., et al.: Predictive active steering control for autonomous vehicle systems. IEEE Trans. Control Syst. Technol. 15, 566–580 (2007)

    Article  Google Scholar 

  11. Yakub, F., Lee, S., Mori, Y.: Comparative study of MPC and LQC with disturbance rejection control for heavy vehicle rollover prevention in an inclement environment. J. Mech. Sci. Technol. 30, 3835–3845 (2016)

    Article  Google Scholar 

  12. Deets, D., Szalai, K.: Design and flight experience with a digital fly-by-wire control system using Apollo guidance system hardware on an F-8 aircraft. Aiaa Journal (1972)

  13. Janbakhsh, A.A., Kazemi, R.: A new approach for simultaneous vehicle handling and path tracking improvement through SBW system. J. Cell Sci. 114, 3137–45 (2010)

    Google Scholar 

  14. Auguet, T., Sebe, M.: Vehicle steering control without mechanical connection between the steering wheel and the steered wheels. US US8036793 (2011)

  15. Cetin, A.E., Adli, M.A., Barkana, D.E., et al.: Implementation and development of an adaptive steering-control system. IEEE Trans. Veh. Technol. 59, 75–83 (2010)

    Article  Google Scholar 

  16. Wang, H., Liu, L., He, P., et al.: Robust adaptive position control of automotive electronic throttle valve using PID-type sliding mode technique. Nonlinear Dyn. 85, 1331–1344 (2016)

    Article  Google Scholar 

  17. Li, L., Lu, Y., Wang, R., et al.: A 3-dimentional dynamics control framework of vehicle lateral stability and rollover prevention via active braking with MPC. IEEE Trans. Ind. Electron. 99, 1–12 (2016)

    Article  Google Scholar 

  18. Palmieri, G., Falcone, P., Tseng, H.E., et al.: A preliminary study on the effects of roll dynamics in predictive vehicle stability control 16, 5354–5359 (2009)

    Google Scholar 

  19. Liao, C., Wu, X., Huang, H.: LMI-based sliding mode anti-rollover control algorithm of vehicle active suspension. Sensors Transd., 1726–5479 (2014)

  20. Solmaz, S., Corless, M., Shorten, R.: A methodology for the design of robust rollover prevention controllers for automotive vehicles with active steering. In: IEEE Conference on Decision and Control, 2006, pp. 1739–1744. IEEE (2007)

  21. Prestonthomas, J., Woodrooffe, J.: A Feasibility Study of a Rollover Warning Device for Heavy Trucks. Transport Canada Publication, Canada (1990)

    Google Scholar 

  22. Hyun, D., Langari, R.: Modeling to predict rollover threat of tractor-semitrailers. Veh. Syst. Dyn. 39(6), 401–414 (2003)

    Article  Google Scholar 

  23. Kong, X.: Research of rollover warning system for heavy vehicles based on hidden Markov Model. Hebei University of Engineering (2013)

  24. Xiaoguo, L., Wang, Z., Qian, F., et al.: Necessary conditions and application of establishing automial regression model. Math. Pract. Theory 38(16), 109–115 (2008)

    MathSciNet  Google Scholar 

  25. Liu, J., Wang, S., He, G.G., et al.: On-line prediction system of vehicle attitude angle based on auto-regressive model. Comput. Eng. 37(13), 202–204 (2011)

    Google Scholar 

  26. Lu, S., Chon, K.H.: Nonlinear autoregressive and nonlinear autoregressive moving average model parameter estimation by minimizing hypersurface distance. IEEE Trans. Signal Process. 51(51), 3020–3026 (2003)

    MathSciNet  MATH  Google Scholar 

  27. Muller, B., Deutscher, J., Grodde, S.: Continuous curvature trajectory design and feedforward control for parking a car. IEEE Trans. Control Syst. Technol. 15(3), 541–553 (2007)

    Article  Google Scholar 

  28. Treacy, P.J., Jones, K., Mansfield, C.: Flipped out of control: single-vehicle rollover accidents in the Northern Territory. Med. J. Aust. 176(6), 260–263 (2002)

    Google Scholar 

  29. Piyabongkarn, D., Yuan, Q., Lew, J.Y.: Method of identifying predictive lateral load transfer ratio for vehicle rollover prevention and warning systems: WO US7873454 (2011)

  30. Akaike, H.: Akaike’s information criterion. International Encyclopedia of Statistical Science, 25 (2011)

  31. Yamaoka, K., Nakagawa, T., Uno, T.: Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J. Pharmacokinet. Biopharma. 6(2), 165 (1978)

    Article  Google Scholar 

  32. Singh, B., Reddy, A.H.N., Murthy, S.S.: Hybrid fuzzy logic proportional plus conventional integral-derivative controller for permanent magnet brushless DC motor. In: IEEE International Conference on Industrial Technology, vol. 1, pp. 185–191. IEEE (2000)

  33. Boada, B.L., Boada, M.J.L., DãAz, V.: Fuzzy-logic applied to yaw moment control for vehicle stability. Veh. Syst. Dyn. 43(10), 753–770 (2005)

    Article  Google Scholar 

  34. Zhicheng, J., Yanxia, S., Jianguo, J.: A novel fuzzy PI intelligent control method of BLDCM speed servo system. Electric Mach. Control 7(3), 248–254 (2003)

    Google Scholar 

  35. Kumar, V., Rana, K.P.S., Mishra, P.: Robust speed control of hybrid electric vehicle using fractional order fuzzy PD and PI controllers in cascade control loop. J. Franklin Inst. 353(8), 1713–1741 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  36. Guo, W., Wang, G., Yu, Q., et al.: Study on active steering control of vehicle based on adaptive fuzzy PI control. Agricultural Equipment & Vehicle Engineering (2015)

  37. Mamdani, E.H., Gaines, B.R.: Mamdani Gaines: Fuzzy Reasoning and its Applications. Academic Press (1981)

  38. NovAtel: Data Sheet. SPAN-CPT, February (2014)

  39. Melexis, N.V.: Data Sheet MLX90316 (2007)

  40. Zhou, H.S.: Steering-by-wire control strategy research based on BLDCM. Grad Thesis, Jiangsu University PRC (2014)

  41. Liu, J., Zhou, H.S., Jia, L.X.: Steering-by-wire control strategy research for rollover warning. Mach. Des. Manuf. 6, 143–145 (2014)

    Google Scholar 

  42. Changfu, Z., Guo, K.: Objective evaluation index for handling and stability of vehicle. Nat. Sci. J. Jilin Univ. Technol. 30(1), 1–6 (2000)

    Google Scholar 

  43. Lu, X., Zhuoping, Y., Wei, J., et al.: Research on vehicle stability control of 4WD electric vehicle based on longitudinal force cintrol allocation. Nat. Sci. J. Tongji Univ. (Nat. Sci.) 38(3), 417–421 (2010)

    Google Scholar 

  44. Falcone, B.P., Tseng, H.E., Borrelli, F., et al.: MPC-based yaw and lateral stabilization via active front steering and braking. Vehicle System Dynamics (2010)

  45. Zanten, A.T.V., Erhardt, R., Landesfeind, K., et al.: VDC systems development and perspective. Vacuum 28(12), 429 (1998)

    Google Scholar 

  46. Zanten, A.T.V., Erhardt, R., Bartels, H., et al.: Simulation for the development of the Bosch-VDC. In: Proceedings of the institute of natural sciences Nihon University, pp. 363–366 (1996)

  47. Zhisheng, Y.: Automotive Theory. 5th edn. Machinery Industry Press (2009)

Download references

Funding

The author(s) disclose receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by The National Natural Science Fund (No. U1564201 and No. U51675235) and The Research Innovation Program for College Graduates of Jiangsu Province (No.4061120007).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haobin Jiang.

Ethics declarations

Conflict of interests

The author(s) declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, J., Jiang, H., Chen, L. et al. Implementation and Development of a Trajectory Tracking Control System for Intelligent Vehicle. J Intell Robot Syst 94, 251–264 (2019). https://doi.org/10.1007/s10846-018-0834-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0834-4

Keywords

Navigation