مدلسازی فازی دینامیکی موتور آلتراسونیک با استفاده از الگوریتم مورچگان

The model of ultrasonic motor system is an important premise of designing motor motion controller. This paper works out fuzzy modeling method of ultrasonic motor system. Ant colony optimization and least square method are used to obtain the unknown parameters of membership functions and fuzzy rules, respectively. The two-input and one-output Takagi-Sugeno model is established, and the model can well show the nonlinear dynamic relationship of ultrasonic motor system.

Modeling of ultrasonic motor can adopt different methods. From the perspective of being suitable for control applications, control modeling method of ultrasonic motor system gets more and more attention. Usually, for the purpose of online realization and minimizing the amount of online calculation, the control model should be relatively simple and can show the main aspects of nonlinear control of ultrasonic motor system. As the limitations of theoretical modeling, control modeling usually adopts identification method based on the tested data[1-3]. According to the different methods of model identification, the forms ofmodel can be the transfer function, differential equation, neural network, etc. In recent years, fuzzy modeling method based on fuzzy reasoning is gradually arisen. Another effective way is provided for the modeling of nonlinear complex system. The fuzzy method is mostly used to realize rotating speed and position control, and is rarely used in the modeling field of motor. This paper works out the appropriate fuzzy modeling method to obtain the dynamic model of ultrasonic motor system. Ant colony optimization algorithm is used to obtain fuzzy clustering membership functions. And then least square method is used to identify the unknown parameters of the conclusions of fuzzy rules. The two-input and one-output Takagi-Sugeno model of ultrasonic motor system is established, and the model can well show the nonlinear dynamic relationship among the amplitude of driving voltage, frequency and rotating speed.

Ant colony optimization algorithm can effectively avoid local optimization. Optimization method is simple, has highly efficiency and robustness. In the paper, ant colony optimization is used to obtain the parameters of T-S dynamic fuzzy model of ultrasonic motor system. The optimization result is satisfying.