Abstract: A commercial vehicle lateral stability optimization control strategy based on particle swarm optimization and neural network optimization algorithm is proposed, and upper and lower double control mode is designed, yaw rate velocity and vehicle side slip angle are taken by upper controller as the control target, according to the feedback information of vehicle driving condition, scale factor parameters of fuzzy controller is optimized by particle swarm optimization algorithm dynamically, and the front wheel additional steering angle and yawing movement control is finished. The lower controller utilizes RBF neural network algorithm to make vehicle yaw velocity deviation adaptive learning, and optimizes the allocation of left and right front wheel brake force and corrects front wheel steering angle. Vehicle lateral stability simulation analysis is conducted by typical test conditions under TruckSim and Matlab/Simulink co-simulation environment. The experiment results show that compared with traditional electronic stability control(ESC) strategy, after optimization control the dynamic response values of yaw rate velocity, vehicle side slip angle and lateral acceleration can meet the requirement, and perform well in tracking the target path, comprehensive improve the lateral stability of vehicle under low adhesion road driving conditions.

Key words: commercial vehicle, lateral control, particle swarm optimization(PSO), radical basis, automobile engineering