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

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.