为优化永磁同步电机（permanent magnet synchronous motor, PMSM）经典滑模控制器的抗干扰能力和启动性能，设计了一种基于径向基函数（radial basis function, RBF）神经网络优化双自适应滑模控制算法。通过梯度下降法实时优化RBF神经网络的权值，动态控制滑模中自适应趋近律的二重自适应函数；结合高阶滑膜面提高新型趋近函数的收敛速度。改进自适应学习率优化算法，动态调整学习速率，进一步改善电机控制精度、响应速度和抗干扰能力，并采用滑模扰动观测器将外界扰动及时前馈至控制器。在MATLAB/Simulink上搭建模型，并与PI、SMC、NeSMC、RBNSMC、RBNSMC + SMOD控制进行对比试验。实验结果表明，该控制算法的跟踪性能更好，能够有效增强系统的抗扰动能力，并且启动性能具有明显改善。

In order to optimise the interference immunity and starting performance of the classical sliding mode controller for permanent magnet synchronous motor (PMSM), a radial basis neural network-based optimised dual adaptive sliding mode control algorithm was designed. The weights of the radial basis function(RBF) neural network were optimised in real time by the gradient descent method to dynamically control the dual adaptive function of the adaptive convergence law in the sliding mode. The convergence speed of the novel convergence function was improved by combining with the higher-order sliding film surface. The adaptive learning rate optimisation algorithm was improved to dynamically adjust the learning rate to further improve the motor control accuracy, response speed and anti-interference ability, and a sliding mode disturbance observer was used to feed forward the external disturbance to the controller in time. The model was built on MATLAB/Simulink and compared with PI, SMC, NeSMC, RBNSMC, RBNSMC + SMOD control. The experimental results show that the control algorithm has better tracking performance, can effectively enhance the system's anti-disturbance ability, and the start-up performance has significant improvement.