Intelligent vehicle lateral control based on radial basis function neural network sliding mode controller

Based on the predigestion of the dynamic model of the intelligent firefighting vehicle, a linear 2-DOF lateral dynamic model and a preview error model are established. To solve the problems of a highly non-linear vehicle model, time-varying parameters, output chattering, and poor robustness, the Radial Basis Function neural network sliding mode controller is designed. Then, different driving speeds are used to conduct simulation tests under standard double-shifting and smooth curve road conditions, and the simulation results are used to analyse the tracking effect of the lateral motion controller on the desired path. The simulation results reveal that the controller designed has high accuracy in tracking the desired path and has good robustness to the disturbance of intelligent firefighting vehicle speed changes.

Automated summary

Based on the dynamic model of the intelligent firefighting vehicle, a linear 2-DOF lateral dynamic model and a preview error model are established. A Radial Basis Function neural network sliding mode controller is designed to solve the problems of non-linearity, time-varying parameters, output chattering, and poor robustness. Simulation results show that the controller has high accuracy in tracking the desired path and has good robustness to speed changes of the vehicle.