Robust Sliding Mode-Based Learning Control for Steer-by-Wire Systems in Modern Vehicles

In this paper, a robust sliding mode learning control (SMLC) scheme is developed for steer-by-wire (SbW) systems. It is shown that an SbW system with uncertain system parameters and unknown external disturbance from the interactions between the tires and the variable road surface can be modeled as a second-order system. A sliding mode learning controller can then be designed to drive both the sliding variable and the tracking error between the steered front-wheel angle and the hand-wheel reference angle to asymptotically converge to zero. The proposed SMLC scheme exhibits many advantages over the existing schemes, including: 1) no information about vehicle parameter uncertainties and self-aligning torque variations is required for controller design; and 2) the control algorithm is capable of efficiently adjusting the closed-loop response based on the most recent history of the closed-loop stability and ensuring a robust steering performance. Both simulations and experiments are presented to show the excellent steering performance and the effectiveness of the proposed learning control methodology.