Skip to main content
SpringerLink
Log in

A novel multi-objective tuning strategy for model predictive control in trajectory tracking

  • Original Article
  • Published:
Journal of Mechanical Science and Technology Aims and scope Submit manuscript

Abstract

Accuracy and efficiency are two important performances of model predictive control in trajectory tracking and they are seriously affected by the control parameters of model predictive control. To make the model predictive control with high accuracy and efficiency simultaneous, the paper proposed a strategy to tune the control parameters for model predictive control. The proposed strategy converts the tuning problem to a multi-objective optimization problem and employs non-dominated sorting genetic algorithm (NSGA-II) to solve it. The proposed strategy is employed to tune the control parameters for a classical model predictive control in a typical trajectory tracking condition. The simulation results show that the comprehensive performances of model predictive controller tuned by the proposed method are better than other tuning methods. The proposed tuning strategy is validated and it can be applied to tune the control parameters for model predictive control in trajectory tracking.

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. X. Liu, J. Liang and J. Fu, A dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(7) (2021) 1808–1824.

    Google Scholar 

  2. Y. Liu and J. Jiang, Optimum path-tracking control for inverse problem of vehicle handling dynamics, Journal of Mechanical Science and Technology, 30(8) (2016) 3433–3440.

    Article  MathSciNet  Google Scholar 

  3. H. Pang et al., A practical trajectory tracking control of autonomous vehicles using linear time-varying MPC method, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 236(4) (2022) 709–723.

    Google Scholar 

  4. Z. Wang and C. Wei, Human-centred risk-potential-based trajectory planning of autonomous vehicles, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 237 (2–3) (2022).

  5. J.-K. Yin and W.-P. Fu, Framework of integrating trajectory replanning with tracking for self-driving cars, Journal of Mechanical Science and Technology, 35(12) (2021) 5655–5663.

    Article  Google Scholar 

  6. C. Zhang et al., Neural network adaptive position tracking control of underactuated autonomous surface vehicle, Journal of Mechanical Science and Technology, 34(2) (2020) 855–865.

    Article  Google Scholar 

  7. A. P. Aguiar and J. P. Hespanha, Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty, IEEE Transactions on Automatic Control, 52(8) (2007) 1362–1379.

    Article  MathSciNet  Google Scholar 

  8. D. Calzolari, B. Schürmann and M. Althoff, Comparison of trajectory tracking controllers for autonomous vehicles, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan (2017).

  9. J. Cao et al., Trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics, IEEE Access, 8 (2020) 59470–59484.

    Article  Google Scholar 

  10. L. Caracciolo, A. De Luca and S. Iannitti, Trajectory tracking control of a four-wheel differentially driven mobile robot, Proceedings 1999 IEEE International Conference on Robotics and Automation, 4 (1999) 2632–2638.

    Google Scholar 

  11. S. Li et al., Dynamic trajectory planning and tracking for autonomous vehicle with obstacle avoidance based on model predictive control, IEEE Access, 7 (2019) 132074–132086.

    Article  Google Scholar 

  12. A. S. Poznyak et al., Nonlinear adaptive trajectory tracking using dynamic neural networks, IEEE Transactions on Neural Networks, 10(6) (1999) 1402–1411.

    Article  Google Scholar 

  13. N. H. Amer et al., Modelling and control strategies in path tracking control for autonomous ground vehicles: a review of state of the art and challenges, Journal of Intelligent and Robotic Systems, 86(2) (2017) 225–254.

    Article  Google Scholar 

  14. J. L. Garriga and M. Soroush, Model predictive control tuning methods: a review, Industrial and Engineering Chemistry Research, 49(8) (2010) 3505–3515.

    Article  Google Scholar 

  15. A. Mohammadi et al., Multiobjective and interactive genetic algorithms for weight tuning of a model predictive control-based motion cueing algorithm, IEEE Transactions on Cybernetics, 49(9) (2018) 3471–3481.

    Article  Google Scholar 

  16. M. Alhajeri and M. Soroush, Tuning guidelines for model-predictive control, Industrial and Engineering Chemistry Research, 59(10) (2020) 4177–4191.

    Article  Google Scholar 

  17. T. Baumeister, S. L. Brunton and J. N. Kutz, Deep learning and model predictive control for self-tuning mode-locked lasers, JOSA B, 35(3) (2018) 617–626.

    Article  Google Scholar 

  18. K. Y. Rani and H. Unbehauen, Study of predictive controller tuning methods, Automatica, 33(12) (1997) 2243–2248.

    Article  MathSciNet  Google Scholar 

  19. A. R. McIntosh, D. Fisher and S. Shah, Performance tuning of adaptive generalized predictive control, IFAC Proceedings Volumes, 23(8) (1990) 339–344.

    Article  Google Scholar 

  20. Q. Zhang and S. Li, Enhanced performance assessment of subspace model — based predictive controller with parameters tuning, The Canadian Journal of Chemical Engineering, 85(4) (2007) 537–548.

    Article  Google Scholar 

  21. A. Georgiou, C. Georgakis and W. L. Luyben, Nonlinear dynamic matrix control for high — purity distillation columns, AIChE Journal, 34(8) (1988) 1287–1298.

    Article  Google Scholar 

  22. P. R. Maurath et al., Predictive controller design by principal components analysis, Industrial and Engineering Chemistry Research, 27(7) (1988) 1204–1212.

    Article  Google Scholar 

  23. J. Lee and Z. Yu, Tuning of model predictive controllers for robust performance, Computers and Chemical Engineering, 18(1) (1994) 15–37.

    Article  Google Scholar 

  24. P. O. Scokaert and J. B. Rawlings, Constrained linear quadratic regulation, IEEE Transactions on Automatic Control, 43(8) (1998) 1163–1169.

    Article  MathSciNet  Google Scholar 

  25. R. Shridhar and D. J. Cooper, A novel tuning strategy for multivariable model predictive control, ISA Transactions, 36(4) (1997) 273–280.

    Article  Google Scholar 

  26. K. Pereida and A. P. Schoellig, Adaptive model predictive control for high-accuracy trajectory tracking in changing conditions, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain (2018).

  27. K. Han, J. Zhao and J. Qian, A novel robust tuning strategy for model predictive control, 2006 6th World Congress on Intelligent Control and Automation, Dalian (2006) 6406–6410.

  28. J. H. Lee and Z. Yu, Worst-case formulations of model predictive control for systems with bounded parameters, Automatica, 33(5) (1997) 763–781.

    Article  MathSciNet  Google Scholar 

  29. A. Al-Ghazzawi et al., On-line tuning strategy for model predictive controllers, Journal of Process Control, 11(3) (2001) 265–284.

    Article  Google Scholar 

  30. W. Liu and G. Wang, Auto-tuning procedure for model-based predictive controller, 2000 IEEE International Conference on Systems, Man and Cybernetics. Cybernetics Evolving to Systems, Humans, Organizations, and Their Complex Interactions, Nashville (2000) 3421–3426.

  31. J. Gong, Y. Jiang and W. Xu, Model Predictive Control for Self-Driving Vehicles, Beijing Institute of Technology Press, Beijing, China (2014).

    Google Scholar 

  32. F. Borrelli et al., MPC-based approach to active steering for autonomous vehicle systems, International Journal of Vehicle Autonomous Systems, 3(2–4) (2005) 265–291.

    Article  Google Scholar 

  33. F. Kuhne, W. F. Lages and J. G. da Silva Jr, Model predictive control of a mobile robot using linearization, Proceedings of Mechatronics and Robotics, 4(4) (2004) 525–530.

    Google Scholar 

  34. K. Deb et al., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2) (2002) 182–197.

    Article  Google Scholar 

  35. P. Falcone et al., MPC-based yaw and lateral stabilisation via active front steering and braking, Vehicle System Dynamics, 46(S1) (2008) 611–628.

    Article  Google Scholar 

  36. B.-A. Kim et al., Comparative study of approximate, proximate, and fast model predictive control with applications to autonomous vehicles, 2012 12th International Conference on Control, Automation and Systems, Jeju, Korea (2012) 479–484.

  37. S.-H. Lee et al., Proximate model predictive control strategy for autonomous vehicle lateral control, 2012 American Control Conference (ACC), Montereal, Canada (2012) 3605–3610.

  38. P. Falcone et al., Predictive active steering control for autonomous vehicle systems, IEEE Transactions on Control Systems Technology, 15(3) (2007) 566–580.

    Article  Google Scholar 

  39. B. Varma, N. Swamy and S. Mukherjee, Trajectory tracking of autonomous vehicles using different control techniques (PID vs LQR vs MPC), 2020 International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), Bengaluru, India (2020) 84–89.

Download references

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang University of Technology, Shenyang, 110000, China

    Jianqiao Chen, Guofu Tian & Yanbo Fu

Authors
  1. Jianqiao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guofu Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanbo Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guofu Tian.

Additional information

Jianqiao Chen is a doctor of the School of Mechanical Engineering of Shenyang University of Technology, China. He obtained a master’s degree in Vehicle Engineering from Liaoning University of Technology. His research interests include vehicle system dynamics and control.

Guofu Tian is a postdoctoral fellow at the School of Mechanical Engineering, Tianjin University. He received a Ph.D. in mechanical engineering from Northeastern University. He is currently employed as a Professor of Vehicle Engineering in the School of Mechanical Engineering, Shenyang University of Technology. His main research directions are intelligent driving and lightweight body design.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Tian, G. & Fu, Y. A novel multi-objective tuning strategy for model predictive control in trajectory tracking. J Mech Sci Technol 37, 6657–6667 (2023). https://doi.org/10.1007/s12206-023-1137-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12206-023-1137-7

Keywords

Search

Navigation