This paper investigates the design and implementation of the discrete-time adaptive neural network control with disturbance observer (DO) on a steer-by-wire (SbW) system, to simultaneously realize accurate tracking and anti-interference performance. Specifically, to approximate the lumped system uncertainty including the friction torque and self-aligning torque, the neural network is employed. To improve the steering tracking performance, the discrete-time identification model is proposed so that the tracking error and modeling error can be utilized to adjust the neural network updating law. Then, the unknown compound disturbances caused by external disturbance, Euler approximation errors and neural network approximation error are restrained by two DOs. Finally, the Lyapunov stability theory shows that the system tracking error is uniformly ultimately bounded. Both numerical simulations and experiments are implemented to show the superiority of the proposed controller.

本文研究了在线控转向 (SbW) 系统上带有干扰观测器 (DO) 的离散时间自适应神经网络控制的设计和实现，以同时实现精确跟踪和抗干扰性能。具体来说，为了近似包括摩擦扭矩和自对准扭矩在内的集总系统不确定性，采用了神经网络。为了提高转向跟踪性能，提出了离散时间辨识模型，利用跟踪误差和建模误差来调整神经网络更新规律。然后，由外部扰动、欧拉逼近误差和神经网络逼近误差引起的未知复合扰动被两个DO抑制。最后，Lyapunov 稳定性理论表明系统跟踪误差是一致最终有界的。数值模拟和实验都被实施以显示所提出的控制器的优越性。