Neural Network-Based Adaptive Nonsingular Fixed-Time Sliding Mode Control for Steer-by-Wire System Considering Dead-Zone Constraint

Steer-by-wire (SBW) system provides flexibility in vehicle design layout and steering ratio customization. However, the mechanical transmission components in SBW system can cause dead-zone effect, which degrades steering angle tracking performance and consequently affects steering maneuverability. To address this issue, a generalized regression neural network-based adaptive nonsingular fixed-time sliding mode control (GRNN-ANFSMC) method is proposed for the SBW system of vehicles. First, a dynamic model of the steering actuator considering input dead-zone is established. Next, an improved fixed-time stable system is designed to achieve a faster convergence rate and a smoother convergence process. Building on this, a segmented sliding mode surface is designed to prevent singularity issues and a reaching law with adaptive parameters is constructed to prevent chattering, which can enhance the dynamic response of SBW system. Meanwhile, GRNN is introduced and its weights are updated in real time by the designed adaptive law, which can effectively approximate and compensate the dead-zone of SBW system within a fixed time. Finally, the effectiveness of the proposed method is validated by the SBW-equipped vehicle. The experimental results demonstrate that compared to other fixed-time SMC, the proposed method can effectively reduce the angle tracking error and improve the dynamic response and robustness of SBW system with time-varying nonlinear dead-zone.