Title: Adaptive modified super-twisting sliding mode control based on PSO with neural network for lateral dynamics of autonomous vehicle

Authors: Rachid Alika; El Mehdi Mellouli; El Houssaine Tissir

Addresses: Department of Physics, LISAC Laboratory, Faculty of Sciences Dhar El Mehraz, University Sidi Mohammed Ben Abdellah, Fez, Morocco ' National School of Applied Sciences, LISA Laboratory, University Sidi Mohammed Ben Abdellah, Fez, Morocco ' Department of Physics, LISAC Laboratory, Faculty of Sciences Dhar El Mehraz, University Sidi Mohammed Ben Abdellah, Fez, Morocco

Abstract: In this article, we have developed a strategy for controlling the lateral dynamics of an autonomous vehicle. The bicycle model of the autonomous vehicle is used. In order to improve the systems performance, we take a new dynamic surface of the sliding mode and a novel expression of the super twisting part of the controller. The parameters of the controller are determined using the particle swarm optimisation (PSO). The objective of this strategy is to follow the reference trajectory of the autonomous vehicle while reducing the lateral displacement error. The steering angle is the control input, the outputs of this system are the lateral displacement and the yaw angle. The radial basis function neural network (RBFNN) is used to approximate the unknown nonlinear dynamic. Simulation results show some improvements over the literature.

Keywords: autonomous vehicles; STSMC; particle swarm optimisation; PSO; radial basis function neural network; RBFNN; nonlinear dynamic; path planning; Lyapunov's stability theory.

DOI: 10.1504/IJMIC.2023.131207

International Journal of Modelling, Identification and Control, 2023 Vol.42 No.4, pp.296 - 311

Received: 05 Mar 2022
Received in revised form: 11 Jun 2022
Accepted: 11 Jul 2022
Published online: 01 Jun 2023 *

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