Authors:
Tien-Chi Chen
;
Tsai-Jiun Ren
and
Yi-Wei Lou
Affiliation:
Kun Shan University, Taiwan
Keyword(s):
Traveling-wave ultrasonic motor, TWUSM, Recurrent fuzzy neural network, RFNN, Back-propagation algorithm, Lyapunov theorem, General regression neural network, GRNN, Dead-zone.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Complex Artificial Neural Network Based Systems and Dynamics
;
Computational Intelligence
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Enterprise Information Systems
;
Fuzzy Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial, Financial and Medical Applications
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Stability and Instability in Artificial Neural Networks
;
Theory and Methods
Abstract:
The traveling-wave ultrasonic motor (TWUSM) has significant features such as high holding torque at low speed range, high precision, fast dynamics, simple structure, no electromagnetic interference. The TWUSM has been used in many practical areas such as industrial, medical, robotic, and automotive applications. However, the dynamic model of the TWUSM motor has the nonlinear characteristic and dead-zone problem which varies with many driving conditions. This paper presents a novel control scheme, recurrent fuzzy neural network (RFNN) and general regression neural network (GRNN) controller, for a TWUSM control. The RFNN provides a real-time control such that the TWUSM output can track the reference command. The back-propagation algorithm is applied in the RFNN to automatically adjust the parameters on-line. The adaptive laws of the RFNN are derived by Lyapunov theorem such that the stability of the system can be absolute. The GRNN controller is appended to the RFNN controller to compe
nsate the dead-zone of the TWUSM system using a predefined set. The experimental results are provided to demonstrate the effectiveness of the proposed controller.
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