A Model-Tuned Predictive Backstepping Control Approach for Angle Following of Steer-by-Wire

An important function of the intelligent steer-by-wire requires a desired steering angle to be followed accurately. In this article, an algorithm-hybrid control is designed to realize the angle following in an electric motor steer-by-wire system, which is named as a model-tuned predictive backstepping control that consists of model tuning control, backstepping control and model predictive control. The model tuning control is used to adjust the variable modelling-term that includes the self-aligning torque, which means that some posteriori knowledge of the control system is utilized to make the model more accurate. The model predictive control is adopted to compute the variable stepping-parameters of the backstepping control, which means that some priori knowledge of the control system is utilized to optimize the control performance. Then we discuss a series of studies on the steer-by-wire system and the control algorithms that, collectively, develop an approach of how the hybrid control algorithm steers the front wheels based on a desired angle. The designed approach has been deployed into a steering control unit, and tested in a steering test vehicle to realize the angle following of electric motor steer-by-wire system. According to experimental results and statistics analyses, it can be concluded that the model-tuned predictive backstepping control is good candidate for the angle following control of steer-by-wire system.