An Adaptive Inverse Model Control Method of Vehicle Yaw Stability with Active Front Steering Based on Adaptive RBF Neural Networks

Abstract

In order to enhance vehicle yaw stability, this paper proposes a novel adaptive inverse model control (AIMC) method for active front steering (AFS) design based on adaptive radial basis function (RBF) neural networks. To approximate the uncertain nonlinearity and modeling errors in vehicle dynamics, an adaptive RBF neural network (ARBFNN) controller is firstly designed. For smaller tracking errors and better robustness, a RBF networks-based AIMC system is further established. Apart from the ARBFNN control law, another two RBF neural networks are utilized, which work as model identifier and inverse model controller, respectively. After being trained offline, both of them are updated by using online learning algorithms. For faster learning and stability, adaptive learning rates are developed. Consequently, the inverse model controller is able to generate required steering wheel angle. Finally, the comparative studies are carried out and the simulation results illustrate the robustness and effectiveness of proposed control strategy.