基于RBF神经网络的超声波电机参数辨识与模型参考自适应控制

超声波电机(USM)是近年发展起来的一种新型微特电机，与传统的电磁驱动型电机的工作原理截然不同。由于USM具有小型轻量、无电磁干扰、响应速度快、低速大转矩、高保持力矩、高功率密度等诸多优点，因而在光学仪器、办公自动化、汽车专用电器、智能机器人、航空航天等领域具有良好的应用前景。但USM的高度非线性、时变性和强耦合增加了它的控制难度。该文提出一种新的USM自适应控制策略。系统采用双闭环控制，内环用来补偿定子环机械谐振频率的漂移；外环利用径向基函数神经网络(RBFt~)控制器调节USM的驱动频率，实现速度的自适应控制。经实验证明，该控制系统具有响应迅速、适应性强等优点，具有较高的控制精度和较好的稳定性。

IDENTIFICATION AND MODEL REFERENCE ADAPTIVE CONTROL FOR ULTRASONIC MOTOR BASED ON RBF NEURAL NETWORK

Ultrasonic motor (USM) is a newly developed motor, and it is quite different from the traditional electromagnetic motors. USM has excellent performance and many useful features, therefore, it has been expected to be of practical use. However, because of the complicated coupling among the variables, high nonlinear characteristics and uncertainty of the parameters and so on, up to the present, no accurate mathematical model has been derived. Hence, the precise speed control of USM is generally difficult. This paper proposes a new model reference adaptive speed control scheme. Two closed loops are constructed here. The inner loop is built as a mechanical resonant frequency compensator. The frequency is regulated in the other loop by the Radial Basis Function neural network (RBFNN) controller whose parameters are adjusted on-line by the use of another RBFNN which can approximate the nonlinear input-output mappings of the motor. With the proposed method, excellent flexibility and adaptability as well as high precision and good robustness are obtained by experiments based on DSP.