超声波电机转速控制模型的迭代学习辨识建模

超声波电机的转速控制模型是其运动控制研究的基础。为研究以驱动频率为调节变量的超声波电机转速控制模型，根据超声波电机驱动频率与转速之间关系特性的实测数据，建立超声波电机转速控制的二阶线性时不变模型。给出迭代学习辨识策略对电机转速控制模型的参数进行辨识。针对超声波电机的每组实测数据中数据量不同导致的参数收敛性减弱的情况，通过双范数最优理论来设计迭代学习辨识的参数学习律。将迭代学习辨识所得结果与Hammerstein模型比较。仿真和实验结果表明，二阶线性时不变模型下的迭代学习辨识可以有效地辨识超声波电机的模型参数，参数收敛速度快、收敛性较好，所建模型精度较高，建模方法有效。

Iterative learning identification for speed control model of ultrasonic motor

The speed control model of ultrasonic motor is the basis of its motion control research. In order to study the ultrasonic motor speed control model with drive frequency as the regulating variable, a second-order linear time-invariant model for ultrasonic motor speed control is established based on the measured data between ultrasonic motor drive frequency and speed. Iterative learning identification is used to recognize the parameters of the motor speed control model. The parameter learning law for iterative learning identification is designed by double-parametric optimality theory for the case of weakened parameter convergence caused by different amounts of data in each set of real measurement data. The results obtained from the iterative learning identification are compared with the Hammerstein model. Simulation and experimental results show that the iterative learning identification can effectively identify the model parameters of the ultrasonic motor. The parameters converge quickly and well, the accuracy of the constructed model is high and the modeling method is effective.