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RNN-based linear parameter varying adaptive model predictive control for autonomous driving

Author

Listed:
  • Yassine Kebbati
  • Naima Ait-Oufroukh
  • Dalil Ichalal
  • Vincent Vigneron

Abstract

Autonomous driving is a complex and highly dynamic process that ensures controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control, distinguished by its predictive feature, optimal performance, and ability to handle constraints, makes it one of the most promising tools for this type of control application. The content of this article handles the problem of autonomous driving by proposing an adaptive linear parameter varying model predictive controller (LPV-MPC), where the controller's prediction model is adaptive by means of a recurrent neural network. The proposed LPV-MPC is further optimised by a hybrid Genetic and Particle Swarm Optimization Algorithm (GA-PSO). The developed controller is tested and evaluated on a challenging track under variable wind disturbance.

Suggested Citation

  • Yassine Kebbati & Naima Ait-Oufroukh & Dalil Ichalal & Vincent Vigneron, 2025. "RNN-based linear parameter varying adaptive model predictive control for autonomous driving," International Journal of Systems Science, Taylor & Francis Journals, vol. 56(5), pages 996-1008, April.
    • Handle: RePEc:taf:tsysxx:v:56:y:2025:i:5:p:996-1008
    DOI: 10.1080/00207721.2024.2414122
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