AutoMPC: A Code Generator for MPC-based Automated Driving
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916908180176896 |
|---|---|
| author | Schildbach, Georg Pflughaupt, Jasper |
| author_facet | Schildbach, Georg Pflughaupt, Jasper |
| contents | Model Predictive Control (MPC) is a powerful technique to control nonlinear, multi-input multi-output systems subject to input and state constraints. It is now a standard tool for trajectory tracking control of automated vehicles. As such it has been used in many research and development projects. However, MPC faces several challenges to be integrated into industrial production vehicles. The most important ones are its high computational demands and the complexity of implementation. The software packages AutoMPC aims to address both of these challenges. It builds on a robustified version of an active set algorithm for Nonlinear MPC. The algorithm is embedded into a framework for vehicle trajectory tracking, which makes it easy to used, yet highly customizable. Automatic code generation transforms the selections into a standalone, computationally efficient C-code file with static memory allocation. As such it can be readily deployed on a wide range of embedded platforms, e.g., based on Matlab/Simulink or Robot Operating System (ROS). Compared to a previous version of the code, the vehicle model and the numerical integration method can be manually specified, besides basic algorithm parameters. All of this information and all specifications are directly baked into the generated C-code. The algorithm is suitable driving scenarios at low or high speeds, even drifting, and supports direction changes. Multiple simulation scenarios show the versatility and effectiveness of the AutoMPC code, with the guarantee of a feasible solution, a high degree of robustness, and computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13656 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AutoMPC: A Code Generator for MPC-based Automated Driving Schildbach, Georg Pflughaupt, Jasper Systems and Control Mathematical Software Robotics 93-04 Model Predictive Control (MPC) is a powerful technique to control nonlinear, multi-input multi-output systems subject to input and state constraints. It is now a standard tool for trajectory tracking control of automated vehicles. As such it has been used in many research and development projects. However, MPC faces several challenges to be integrated into industrial production vehicles. The most important ones are its high computational demands and the complexity of implementation. The software packages AutoMPC aims to address both of these challenges. It builds on a robustified version of an active set algorithm for Nonlinear MPC. The algorithm is embedded into a framework for vehicle trajectory tracking, which makes it easy to used, yet highly customizable. Automatic code generation transforms the selections into a standalone, computationally efficient C-code file with static memory allocation. As such it can be readily deployed on a wide range of embedded platforms, e.g., based on Matlab/Simulink or Robot Operating System (ROS). Compared to a previous version of the code, the vehicle model and the numerical integration method can be manually specified, besides basic algorithm parameters. All of this information and all specifications are directly baked into the generated C-code. The algorithm is suitable driving scenarios at low or high speeds, even drifting, and supports direction changes. Multiple simulation scenarios show the versatility and effectiveness of the AutoMPC code, with the guarantee of a feasible solution, a high degree of robustness, and computational efficiency. |
| title | AutoMPC: A Code Generator for MPC-based Automated Driving |
| topic | Systems and Control Mathematical Software Robotics 93-04 |
| url | https://arxiv.org/abs/2508.13656 |