Advertisement

Tuning of Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance

Conference paper
  • 382 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)

Abstract

This work is concerned with tuning a nonlinear Model Predictive Control (MPC) algorithm. Typically, the weighting coefficients associated with the predicted control errors are constant for the consecutive sampling instants over the prediction horizon. This work discusses a tuning procedure whose objective is to find a set of coefficients which scale the influence of the consecutive predicted control errors. The coefficients are determined using an original simulation-based method. In order to demonstrate effectiveness of the method, an MPC algorithm for vehicle obstacle avoidance is developed. It is shown that the discussed tuning methods makes it possible to obtain much better control quality in comparison with the classical approach.

Keywords

Model Predictive Control Tuning Control system performance Obstacle avoidance 

References

  1. 1.
    Chaber, P., Ławryńczuk, M.: Fast analytical model predictive controllers and their implementation for STM32 arm microcontroller. IEEE Trans. Industr. Inf. 15, 4580–4590 (2019)CrossRefGoogle Scholar
  2. 2.
    Exadaktylos, V., Taylor, C.J.: Multi-objective performance optimisation for model predictive control by goal attainment. Int. J. Control 83, 1374–1386 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Grosso, J.M., Ocampo-Martinez, C., Puig, V.: Reliability-based economic model predictive control for generalised flow-based networks including actuators’ health-aware capabilities. Int. J. Appl. Math. Comput. Sci. 26, 361–654 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Júnior, G.A., Martins, M.A.F., Kalid, R.: A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty. ISA Trans. 53, 560–567 (2014)CrossRefGoogle Scholar
  5. 5.
    Ławryńczuk, M.: Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach. Studies in Systems, Decision and Control, vol. 3. Springer, Cham (2014)CrossRefGoogle Scholar
  6. 6.
    Nebeluk, R., Marusak, P.: Influencing predictive control system performance by reference trajectory shaping. Pomiary Automatyka Robotyka 23, 21–30 (2019). (in Polish)CrossRefGoogle Scholar
  7. 7.
    Nebeluk, R., Marusak, P.: Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors. Arch. Control Sci. (in review)Google Scholar
  8. 8.
    Obstacle Avoidance Using Adaptive Model Predictive Control: Model Predictive Control Toolbox for MATLAB. https://www.mathworks.com/help/mpc/ug/obstacle-avoidance-using-adaptive-model-predictive-control.html
  9. 9.
    Pour, F.K., Puig, V., Ocampo-Martinez, C.: Multi-layer health-aware economic predictive control of a pasteurization pilot plant. Int. J. Appl. Math. Comput. Sci. 28, 97–110 (2018)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Sawulski J., Ławryńczuk M.: Optimisation-based tuning of dynamic matrix control algorithm for multiple-input multiple-output processes. In: Proceedings of the 23th IEEE International Conference on Methods and Models in Automation and Robotics MMAR 2018, pp. 160–165. Międzyzdroje, Poland (2018)Google Scholar
  11. 11.
    Scattolini, R., Bittanti, S.: On the choice of the horizon in long-range predictive control-some simple criteria. Automatica 26, 915–917 (1990)CrossRefGoogle Scholar
  12. 12.
    Seborg, D.E., Edgar, T.F., Mellichamp, D.A., Doyle III, F.J.: Process Dynamics and Control. Wiley, New York (2011)Google Scholar
  13. 13.
    Seybold, L., Witczak, M., Majdziek, P., Stetter, R.: Towards robust predictive fault-tolerant control for a battery assembly unit. Int. J. Appl. Math. Comput. Sci. 25, 849–862 (2015)CrossRefGoogle Scholar
  14. 14.
    Shridhar, R., Cooper, D.J.: A tuning strategy for unconstrained multivariable model predictive control. Ind. Eng. Chem. Res. 37, 4003–4016 (1998)CrossRefGoogle Scholar
  15. 15.
    Shridhar, R., Cooper, D.J.: A tuning strategy for unconstrained SISO model predictive control. Ind. Eng. Chem. Res. 36, 729–746 (1997)CrossRefGoogle Scholar
  16. 16.
    Tatjewski, P.: Offset-free nonlinear Model Predictive Control with state-space process models. Arch. Control Sci. 27, 595–615 (2017)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Tatjewski, P.: Advanced Control of Industrial Processes. Structures and Algorithms. Springer, London (2007)zbMATHGoogle Scholar
  18. 18.
    Trierweiler, J.O., Farina, L.A.: RPN tuning strategy for model predictive control. J. Process Control 13, 591–598 (2003)CrossRefGoogle Scholar
  19. 19.
    Yamashita, A.S., Alexandre, P.M., Zanin, A.C., Odloak, D.: Reference trajectory tuning of model predictive control. Control Eng. Pract. 50, 1–11 (2016)CrossRefGoogle Scholar
  20. 20.
    Zhou, F., Peng, H., Zhang, G., Zeng, X.: A robust controller design method based on parameter variation rate of RBF-ARX model. IEEE Access 7, 160284–160294 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarsawPoland

Personalised recommendations