LTV-RBF Approach for Yaw Stability Control of Distributed Drive Electric Vehicles

This paper proposed a torque distribution strategy based on linear time-varying radial basis function (LTV-RBF) neural networks for yaw stability control of electric vehicles equipped with in-wheel motors. The desired yaw rate is calculated by the two-degree-of-freedom (2-DOF) bicycle dynamic model. The influence of the time-varying steering angle is considered for reducing the yaw rate error. To solve this problem, the constant connection weight of conventional RBF networks is converted into a time-varying variable which is used to track the reference trajectory and optimize the torque distribution. The torques of in-wheel motors are restricted in high efficiency range to improve the system energy efficiency. Simulation results of the proposed torque distribution strategy based on dSPACE simulator show that LTV-RBF networks can effectively track the reference yaw rate and stabilize the system.