In this paper, an observer-based adaptive neural network controller is developed for the Steer-by-Wire (SbW) system of automated vehicles with uncertain nonlinearity and unmeasured state. An observer is introduced to estimate the angular velocity of the front wheels, so the hardware cost and the complexity of mechanical structure and electronic circuits are reduced. Then, an observer-based adaptive neural network controller is proposed for the SbW system to achieve excellent steering precision. A radial basis function (RBF) neural network is used to model the uncertain nonlinearity, which mainly includes self-aligning torque and unknown friction torque with strong nonlinearity. The adaptive law of the RBF neural network designed by fuzzy basis functions rather than by its filtering is derived by Lyapunov stability theory and the Strictly Positive Real (SPR) condition. The tracking error and the observation error can be guaranteed to converge asymptotically to zero. Simulation and experimental results for two paths highlight the effectiveness of the proposed control algorithm.

本文针对具有不确定非线性和未测状态的自动驾驶汽车的线控转向（SbW）系统，开发了一种基于观察者的自适应神经网络控制器。引入了观察者以估计前轮的角速度，因此降低了硬件成本，并降低了机械结构和电子电路的复杂性。然后，针对SbW系统，提出了一种基于观测器的自适应神经网络控制器，以实现优异的转向精度。径向基函数神经网络（RBF）用于不确定性的非线性建模，主要包括自调矩和未知的具有强非线性的摩擦力矩。由Lyapunov稳定性理论和严格正实（SPR）条件推导了通过模糊基函数而不是通过滤波设计的RBF神经网络的自适应律。可以保证跟踪误差和观察误差渐近收敛到零。两条路径的仿真和实验结果突出了所提出的控制算法的有效性。