A Control Method for Dual Motor Redundant Steer System Based on Zeroing Neural Networks

The reliability of the steering system directly impacts the safety of autonomous driving. Addressing the issue of trajectory deviation easily caused by motor failure in redundant steer-by-wire (SBW) systems, this paper aims to improve vehicle tracking accuracy under fault conditions. A hierarchical fault-tolerant control strategy based on a zeroing neural network (ZNN) is proposed: the upper layer uses the Stanley algorithm for path planning, while the lower layer designs a ZNN controller with preset performance constraints, and instantaneous power reconfiguration is achieved through Jacobi pseudo-inverse. Simulation results show that under high-speed lane changes and sinusoidal conditions, this strategy can achieve millisecond-level task reassignment, and compared to PID control, the maximum absolute error of lateral tracking under fault conditions is reduced by over 50%, and the root mean square error is reduced by over 30%. This method effectively improves driving safety and trajectory fidelity when actuators fail.