Autonomous driving using an optimized neural network based adaptive LPV-MPC controller

Abstract

Driverless vehicles are complex systems operating in constantly changing environments. Automated driving is achieved by controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control is one of the most promising tools for this type of application due to its optimal performance and ability to handle constraints. This paper addresses autonomous driving with an adaptive linear parameter varying model predictive controller (LPV-MPC), which is adapted by a neural network and optimized by an improved Genetic Algorithm. The proposed controller is evaluated on a challenging track under variable wind disturbance. Code can be found here: https://github.com/yassinekebbati/GA-optimized-MLP-based-LPV_MPC