Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network

The Formula SAE (FSAE) race track is characterized by a large number of corners, making cornering performance a key factor affecting lap time. Based on the proportional control strategy for rear-wheel steering angles, this paper proposes a steering angle optimization method using a Temporal Convolutional Network (TCN). The TCN model features a faster training speed than traditional sequential neural networks. In addition, dilated convolutions enable an exponential expansion of the receptive field without increasing computational costs, making it particularly suitable for capturing the temporal dependencies of vehicle states. By processing vehicle dynamic parameters including front-wheel steering angle, vehicle speed, yaw rate and sideslip angle, the model calculates the correction value of the rear-wheel steering angle. This correction value is then superimposed with the reference value of the rear-wheel steering angle derived from the proportional control strategy, which serves as the control value for rear-wheel steering. Rear-wheel steering can reduce the turning radius during low-speed driving and enhance the racing car’s stability during high-speed cornering. This method was validated on a typical race track via CarSim-MATLAB co-simulation, resulting in reduced lap time. To meet the real-time computing requirements of FSAE, MATLAB was used to simulate the discretization results of vehicle parameters such as vehicle speed and front-wheel steering angle, generating a Look-Up Table for rear-wheel steering angles, which provides a feasible solution for real-vehicle tests. The racing car is equipped with a manual switch for the driver to operate. The driver can manually turn the rear-wheel steering function on or off when cornering or whenever they deem it necessary.