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Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator
Faa-Jeng Lin1, Hsin-Jang Shieh, Po-Kai Huang
1Department of Electrical Engineering, National Dong Hwa University, Hualien 974, Taiwan. linfj@mail.ndhu.edu.tw
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
|September 13, 2006
Summary
This study introduces an adaptive control strategy using a recurrent fuzzy neural network (RFNN) to compensate for piezoactuator hysteresis. The novel approach enhances control performance and robustness in piezoactuator systems.
Area of Science:
- Control Engineering
- Mechatronics
- Materials Science
Background:
- Piezoactuators exhibit performance degradation due to inherent hysteresis.
- Accurate control of piezoactuators is crucial for precision applications.
Purpose of the Study:
- To develop an adaptive control method for piezoactuators that effectively estimates and compensates for hysteresis.
- To improve the dynamic characteristics and robustness of piezoactuator control systems.
Main Methods:
- A modified hysteresis friction model was parameterized and integrated into the piezoactuator's mechanical dynamics.
- A recurrent fuzzy neural network (RFNN) was employed as an online uncertainty observer for adaptive control.
- The adaptive controller estimates and compensates for lumped uncertainty (E) in real-time.
Main Results:
- The proposed adaptive control strategy significantly improved piezoactuator control performance.
- The RFNN effectively observed and compensated for system uncertainties and parameter variations.
- Experimental results demonstrated high-performance dynamic characteristics and robustness.
Conclusions:
- The RFNN-based adaptive control offers an effective solution for hysteresis compensation in piezoactuators.
- The proposed method enhances the reliability and precision of piezoactuator systems under varying conditions.
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