Steering angle control in self-driving cars is usually organized in layers combining trajectory planning, path tracking, and low-level actuator control. The steering controller converts the planned path into a desired steering angle and then ensures accurate tracking by the electric power steering (EPS). However, automotive electric power steering (AEPS) systems face many problems caused by model uncertainties, disturbances, and unknown system dynamics. In this paper, a robust finite-time control strategy based on an adaptive backstepping scheme is proposed to handle these problems. First, radial basis function neural networks (NNs) are designed to approximate the unknown system dynamics. Then, an adaptive sliding-mode disturbance observer (ASMDO) is introduced to address the impacts of the lumped disturbance. Enhanced control performance for the AEPS system is implemented using a combination of the above technologies. Numerical simulations and a hardware-in-the-loop (HIL) experimental verification are performed to demonstrate the significant improvement in performance achieved using the proposed strategy.
Yu et al. (2026) studied this question.