A Model Predictive Backstepping Control Approach for Angle Tracking of Steer-by-Wire System

This paper presents a hybrid control approach, termed Model Predictive Backstepping Control, for steering angle manipulation. The design of stepping manifolds in the backstepping control incorporates the Lyapunov function, posing a challenge in determining optimal stepping parameters for each manifold. In contrast to the predominant focus on constant stepping coefficients in existing research, this study explores the less investigated variable approach. Stepping parameters are introduced as tunable variables and incorporated into a backstepping control law, computed using model predictive control with the backstepping control law using tunable variables as the system input. The hybrid control algorithm utilizes prior knowledge of the control system to identify optimal values for the stepping parameters. The paper comprehensively explores model predictive backstepping control applied to a steer-by-wire system, specifically addressing the resolution of variable stepping parameters through a cost function. The developed algorithm is implemented into a steering control unit and subsequently validated in a real-world steering test vehicle. The results demonstrate successful realization of angle following in the steer-by-wire system within an engineering practice context.