Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910003839893504 |
|---|---|
| author | Abtahi, Mohammad Rabbani, Mahdis Abdolmohammadi, Armin Nazari, Shima |
| author_facet | Abtahi, Mohammad Rabbani, Mahdis Abdolmohammadi, Armin Nazari, Shima |
| contents | The highly nonlinear dynamics of vehicles present a major challenge for the practical implementation of optimal and Model Predictive Control (MPC) approaches in path planning and following. Koopman operator theory offers a global linear representation of nonlinear dynamical systems, making it a promising framework for optimization-based vehicle control. This paper introduces a novel deep learning-based Koopman modeling approach that employs deep neural networks to capture the full vehicle dynamics-from pedal and steering inputs to chassis states-within a curvilinear Frenet frame. The superior accuracy of the Koopman model compared to identified linear models is shown for a double lane change maneuver. Furthermore, it is shown that an MPC controller deploying the Koopman model provides significantly improved performance while maintaining computational efficiency comparable to a linear MPC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame Abtahi, Mohammad Rabbani, Mahdis Abdolmohammadi, Armin Nazari, Shima Systems and Control Machine Learning Robotics Optimization and Control The highly nonlinear dynamics of vehicles present a major challenge for the practical implementation of optimal and Model Predictive Control (MPC) approaches in path planning and following. Koopman operator theory offers a global linear representation of nonlinear dynamical systems, making it a promising framework for optimization-based vehicle control. This paper introduces a novel deep learning-based Koopman modeling approach that employs deep neural networks to capture the full vehicle dynamics-from pedal and steering inputs to chassis states-within a curvilinear Frenet frame. The superior accuracy of the Koopman model compared to identified linear models is shown for a double lane change maneuver. Furthermore, it is shown that an MPC controller deploying the Koopman model provides significantly improved performance while maintaining computational efficiency comparable to a linear MPC. |
| title | Multi-Step Deep Koopman Network (MDK-Net) for Vehicle Control in Frenet Frame |
| topic | Systems and Control Machine Learning Robotics Optimization and Control |
| url | https://arxiv.org/abs/2503.03002 |