Optimized self-adaptive PID speed control for autonomous vehicles

Yassine Kebbati (1) , Naïma Aït Oufroukh (1) , Vincent Vigneron (1, 2) , Dalil Ichalal (1) , Dominique Gruyer (3)

1 IBISC - Informatique, BioInformatique, Systèmes Complexes
2 School of Applied Sciences (FCA)
3 COSYS-PICS-L - Laboratoire sur la Perception, les Intéractions, les Comportements et la Simulation des usagers de la route et de la rue

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.

Keywords: Autonomous Vehicle; Adaptive PID Control; Neural Networks; Genetic Algorithms; Optimization

Domains: Automatic; Signal and Image processing

Yassine Kebbati, Naïma Aït Oufroukh, Vincent Vigneron, Dalil Ichalal, Dominique Gruyer. Optimized self-adaptive PID speed control for autonomous vehicles. 26th International Conference on Automation and Computing (ICAC 2021), Sep 2021, Portsmouth, United Kingdom. pp.1-6, ⟨10.23919/ICAC50006.2021.9594131⟩. ⟨hal-03442081⟩