Optimized self-adaptive PID speed control for autonomous vehicles

Abstract

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

References in corpus (1)

• Optimized adaptive MPC for lateral control of autonomous vehicles

Cited by in corpus (6)

• Lateral control for autonomous vehicles: A comparative evaluation
• Design, modeling and control of a hybrid grid-connected photovoltaic-wind system for the region of Adrar, Algeria
• Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles
• Neural Network and ANFIS based auto-adaptive MPC for path tracking in autonomous vehicles
• Autonomous driving using an optimized neural network based adaptive LPV-MPC controller
• Learning-based model predictive control with moving horizon state estimation for autonomous racing