NDLI: Identification of Ultrasonic Motor’s Nonlinear Hammerstein Model

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Identification of Ultrasonic Motor’s Nonlinear Hammerstein Model

Content Provider	Springer Nature Link
Author	Juanping, Zhao Lei, Zhang Jingzhuo, Shi Meiyu, Xu Juwei, Zhang Jingtao, Huang
Copyright Year	2014
Abstract	Ultrasonic motor (USM) has heavy nonlinearity, and its nonlinearity is easily changed along with the change of driving condition. Therefore, it is hard to acquire an accurate model of USM. At present, in the design of USM’s control strategy, an identification method is usually used to establish the model of USM. But the established models are always linear. Because the nonlinearity of USM system is obvious and proper nonlinear model is the foundation of excellent servo control. So in this paper, a kind of modelling method to obtain the nonlinear Hammerstein model of USM’s drive system is proposed. The structure of Hammerstein model consists of a nonlinear static element and a linear dynamic element, which can describe the nonlinearity of USM in a relatively simple form. The modelling method is on the basis of the complexity of USM itself. The parameters of the tested data are identified by using the particle swarm optimization (PSO) method which has good searching ability under complex cases. The proposed model can accurately represent USM model. That is, this PSO method makes it easier and more accurate to produce the model. Simulation results show that the data obtained from the model are close to the actual data; it indicates that the modelling method is reasonable and the established model is valid.
Starting Page	537
Ending Page	546
Page Count	10
File Format	PDF
ISSN	21953880
Journal	Journal of Control, Automation and Electrical Systems
Volume Number	25
Issue Number	5
e-ISSN	21953899
Language	English
Publisher	Springer US
Publisher Date	2014-06-03
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ultrasonic motor Control Hammerstein model Control, Robotics, Mechatronics Nonlinear model Particle swarm optimization Electrical Engineering Robotics and Automation
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Energy Engineering and Power Technology Electrical and Electronic Engineering Computer Science Applications

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