RNN-based linear parameter varying adaptive model predictive control for autonomous driving

TL;DR

This paper introduces an adaptive LPV-MPC for autonomous driving that uses a recurrent neural network for prediction and is optimized with a hybrid GA-PSO algorithm, tested under variable wind disturbances.

Contribution

It presents a novel RNN-based adaptive LPV-MPC controller optimized with hybrid GA-PSO for improved autonomous vehicle control.

Findings

Effective handling of variable wind disturbances

Enhanced control performance over traditional methods

Successful validation on challenging track

Abstract

Autonomous driving is a complex and highly dynamic process that ensures controlling the coupled longitudinal and lateral vehicle dynamics. Model predictive control, distinguished by its predictive feature, optimal performance, and ability to handle constraints, makes it one of the most promising tools for this type of control application. The content of this article handles the problem of autonomous driving by proposing an adaptive linear parameter varying model predictive controller (LPV-MPC), where the controller's prediction model is adaptive by means of a recurrent neural network. The proposed LPV-MPC is further optimised by a hybrid Genetic and Particle Swarm Optimization Algorithm (GA-PSO). The developed controller is tested and evaluated on a challenging track under variable wind disturbance. Code can be found here : https://github.com/yassinekebbati/GA-PSO-optimized-RNN-MPC