In this study, an adaptive neural network (NN) command filtered control (CFC) method is proposed for a permanent magnet synchronous motor (PMSM) system with system uncertainties and external disturbance by means of a backstepping technique. At every backstepping step, a novel command filter is proposed, and the complicated virtual input and its derivative together can be approximated by this filter. The “explosion of complexity” problem in conventional backstepping design can be avoided because we do not need to calculate the derivative of the virtual input repeatedly. NNs are used to model system uncertainties and disturbances. Finally, an adaptive NN CFC is designed, and the convergence of the tracking error and the boundedness of all signals involved can be guaranteed. Finally, a simulation study is presented to verify the theoretical results.

在这项研究中，提出了一种自适应神经网络（NN）命令滤波控制（CFC）方法，该方法通过反步技术用于具有系统不确定性和外部干扰的永磁同步电动机（PMSM）系统。在每个反推步骤中，都提出了一种新颖的命令过滤器，并且该过滤器可以将复杂的虚拟输入及其派生词近似起来。因为我们不需要反复计算虚拟输入的导数，所以可以避免传统Backstepping设计中的“复杂性爆炸”问题。NN用于对系统不确定性和干扰进行建模。最后，设计了一种自适应NN CFC，可以保证跟踪误差的收敛性和所涉及的所有信号的有界性。最后，进行了仿真研究以验证理论结果。