RNN-based linear parameter varying adaptive model predictive control for autonomous driving
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918181752274944 |
|---|---|
| author | Kebbati, Yassine Ait-Oufroukh, Naima Ichalal, Dalil Vigneron, Vincent |
| author_facet | Kebbati, Yassine Ait-Oufroukh, Naima Ichalal, Dalil Vigneron, Vincent |
| contents | 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. Code can be found here : https://github.com/yassinekebbati/GA-PSO-optimized-RNN-MPC |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00610 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RNN-based linear parameter varying adaptive model predictive control for autonomous driving Kebbati, Yassine Ait-Oufroukh, Naima Ichalal, Dalil Vigneron, Vincent Optimization and Control 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. Code can be found here : https://github.com/yassinekebbati/GA-PSO-optimized-RNN-MPC |
| title | RNN-based linear parameter varying adaptive model predictive control for autonomous driving |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2511.00610 |