A model-tuned predictive sliding control approach for steering angle following of full self-driving vehicles

Abstract

Steer-by-wire is a key technology to realize full self-driving for intelligent vehicles, where this is a challenge for steering angle following accurately. Therefore, a hybrid control thinking is proposed to design a model-tuned predictive sliding control approach, which is utilized to realize angle following of the electric motor steer-by-wire system. Here, sliding mode control is used as a core algorithm to be fit for the dynamics characteristics of the steer-by-wire system. Model tuning control is proposed to update model parameters by a receding horizon method, which means that some posteriori knowledge of the control system is utilized to make the model more accurate. Model predictive control is used to optimize the sliding manifold parameters of sliding mode control, which means that some priori knowledge of the control system is used to make the control law much fitter. Then we discuss a series of studies on the steer-by-wire system model and the control algorithm that, collectively, develop an approach of how the hybrid control algorithm precisely makes the front wheel angles follow desired steering commands. The designed approach has been deployed into a steering control unit, and tested in a steering test vehicle to realize the angle following of the electric motor steer-by-wire system. Based on the experimental results and statistical analysis, it can be concluded that the hybrid control thinking is a good idea of how to fuse several algorithms into a control approach, and the model-tuned predictive sliding control approach is a good candidate for the steering angle following of full self-driving vehicles.