Active suspension control model of emergency rescue vehicles based on fuzzy control and radial basis function neural network

Abstract The current active suspension control models for vehicles suffer from an incomplete perception of road information and unsatisfactory control effects. In response to this, an active suspension control model based on fuzzy control and a radial basis function model is constructed. The model first establishes the vehicle’s dynamic model and road surface model through numerical simulation, providing a basis for subsequent suspension control. Afterwards, the fuzzy control model improved by combining a radial basis function is utilized to identify the current road surface level. A controller is designed based on the recognition results input into the active suspension robust control module, with minimizing the spring load mass acceleration as its output constraint, suspension dynamic stroke, actuator active force, and tire dynamic load as control constraints. The experimental results showed that the pavement grade recognition accuracy of the active suspension control model has reached 98.46%. The proposed model could effectively enhance the driving stability and ride comfort of emergency rescue vehicles in complex road conditions in real-time scenarios with incomplete road perception, significantly outperforming traditional passive suspension systems. The root mean square errors of its roll angle and pitch angle were only 1.8° and 1.0°, respectively. The dynamic load fluctuation power of its tires was 7.6%, and the vertical vibration acceleration was only 0.6 m s −2 . By integrating the strong adaptability of fuzzy logic with the high-precision identification capability of RBF networks, the model achieved rapid response and precise control to road surface changes. This indicates that the proposed active suspension control model can effectively ensure the driving stability and smoothness of emergency rescue vehicles and prevent traffic accidents caused by road bumps. The research results have significant practical significance and application value for the smooth progress of emergency rescue tasks.