Optimized self-adaptive PID speed control for autonomous vehicles
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916959910625280 |
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| author | Kebbati, Yassine Ait-Oufroukh, Naima Vigneron, Vincent Ichalal, Dalil Gruyer, Dominique |
| author_facet | Kebbati, Yassine Ait-Oufroukh, Naima Vigneron, Vincent Ichalal, Dalil Gruyer, Dominique |
| contents | The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive PID control using two different approaches: Genetic Algorithms (GA-PID) and then Neural Networks (NN-PID) respectively. The vehicle nonlinear longitudinal dynamics are modeled using Powertrain blockset library. Finally, simulations are performed to assess and compare the performance of the two controllers subject to external disturbances. Code can be found here: https://github.com/yassinekebbati/Self-adaptive-PID |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimized self-adaptive PID speed control for autonomous vehicles Kebbati, Yassine Ait-Oufroukh, Naima Vigneron, Vincent Ichalal, Dalil Gruyer, Dominique Optimization and Control The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive PID control using two different approaches: Genetic Algorithms (GA-PID) and then Neural Networks (NN-PID) respectively. The vehicle nonlinear longitudinal dynamics are modeled using Powertrain blockset library. Finally, simulations are performed to assess and compare the performance of the two controllers subject to external disturbances. Code can be found here: https://github.com/yassinekebbati/Self-adaptive-PID |
| title | Optimized self-adaptive PID speed control for autonomous vehicles |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.17214 |