A vehicle lateral stability criterion fusing phase plane and RBF neural network

Precise stability criteria are essential for vehicle handling control, but conventional methods based on tire adhesion limits or linear models often lack robustness across diverse scenarios. To address this issue, this paper proposes a novel lateral stability criterion fusing phase plane analysis and RBF neural networks. The approach begins with an analysis of the vehicle’s stable state using the phase plane, followed by the division of the vehicle stability region employing the diamond method to generate a phase plane stability region database. Subsequently, the proposed phase plane-RBF stability criterion is constructed by leveraging the RBF neural network for nonlinear fitting of the stability region data, which is further refined through multiple rounds of optimization. Compared to traditional tire force and linear single-track model criteria, the proposed criterion demonstrates superior accuracy in identifying extreme conditions and enhanced adaptability across operational scenarios.