两相混合式步进电机是一个非线性、多变量、强耦合的系统。针对两相混合式步进电机开环控制定位精度低的问题，提出了一种基于径向基函数(RBF)神经网络的反步控制方法，该方法克服了单一反步控制对非线性系统控制参数选取困难的缺点，利用RBF神经网络的万能逼近特性，对电机运行过程中的不确定因素进行补偿，使其不过于依赖反步控制器所选取的参数，同时引入高斯基函数和自适应律，能够较好地对其中的非线性项进行逼近。利用神经网络与反步控制方法的结合，有效提高了两相混合式步进电机控制的位置跟踪精度和稳态性能。

The two-phase hybrid stepping motor is a nonlinear, multi-variable and strongly coupled system. Aiming at the problems of simple open-loop control structure and low positioning accuracy of two-phase hybrid stepping motor, a backstepping control method based on radial basis function (RBF) neural network is proposed. The control method overcomes the shortcomings of traditional backstepping control for nonlinear control, uses the universal approximation property of RBF neural network to compensate for the uncertainties in motor operation. Meanwhile, Gaussian basis function and adaptive law are introduced, which can make the RBF neural network better compensate for the uncertainties in motor operation. The combination of neural network and backstepping control methods effectively improves the position tracking accuracy and steady-state performance of the two-phase hybrid stepping motor control.