Paper
9 June 2006 Hysteresis compensation for piezoelectric actuators in single-point diamond turning
Haifeng Wang, Dejin Hu, Daping Wan, Hongbin Liu
Author Affiliations +
Proceedings Volume 6149, 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies; 614934 (2006) https://doi.org/10.1117/12.674322
Event: 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies, 2005, Xian, China
Abstract
In recent years, interests have been growing for fast tool servo (FTS) systems to increase the capability of existing single-point diamond turning machines. Although piezoelectric actuator is the most universal base of FTS system due to its high stiffness, accuracy and bandwidth, nonlinearity in piezoceramics limits both the static and dynamic performance of piezoelectric-actuated control systems evidently. To compensate the nonlinear hysteresis behavior of piezoelectric actuators, a hybrid model coupled with Preisach model and feedforward neural network (FNN) has been described. Since the training of FNN does not require a special calibration sequence, it is possible for on-line identification and real-time implementation with general operating data of a specific piezoelectric actuator. To describe the rate dependent behavior of piezoelectric actuators, a hybrid dynamic model was developed to predict the response of piezoelectric actuators in a wider range of input frequency. Experimental results show that a maximal error of less than 3% was accomplished by this dynamic model.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haifeng Wang, Dejin Hu, Daping Wan, and Hongbin Liu "Hysteresis compensation for piezoelectric actuators in single-point diamond turning", Proc. SPIE 6149, 2nd International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies, 614934 (9 June 2006); https://doi.org/10.1117/12.674322
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KEYWORDS
Actuators

Neural networks

Fourier transforms

Single point diamond turning

Servomechanisms

Data modeling

Systems modeling

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