基于径向基神经网络的主动前轮转向自抗扰控制

为研究复杂环境下车辆主动前轮转向系统（AFS）稳定性问题，提出一种基于RBF神经网络的主动前轮转向自抗扰控制（ADRC）方法，通过设计RBF神经网络结构采用梯度下降法达到自抗扰控制器参数整定的目的，改善ADRC参数多整定耗时且控制效果难以保持最优的不足；针对车辆AFS定传动比的不足，设计固定横摆角速度增益的理想变传动比规则。结果表明，基于RBF神经网络的ADRC策略能够较好的实现动态跟踪主动前轮转向理想横摆角速度，同时相比ADRC抗干扰量提高了25.8%，有效抑制了横摆角速度的振荡幅值。可见该方法提高了理想横摆角速度的跟踪能力，改善了车辆的可控性和稳定性并具有良好控制精度。

Active Front Wheel Steering Active Disturbance Rejection Control Based on Radial Basis Function Neural Network

In order to study the stability of vehicle active front wheel steering (AFS) system in complex environment, an active front wheel steering active disturbance rejection control (ADRC) method based on RBF neural network is proposed. By designing the structure of RBF neural network, the gradient descent method is adopted to achieve the purpose of parameter tuning of active front wheel steering system (AFS); Aiming at the deficiency of fixed transmission ratio of AFS, the ideal variable transmission ratio rule with fixed yaw rate gain is designed. The results show that the ADRC strategy based on RBF neural network can effectively track the ideal yaw rate of active front wheel steering. Compared with ADRC, the anti-interference amount is increased by 25.8%, and the oscillation amplitude of yaw rate is effectively suppressed. It can be seen that this method improves the tracking ability of ideal yaw rate, improves the controllability and stability of the vehicle, and has good control accuracy.