TY - JOUR
T1 - Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator
AU - Lin, Faa Jeng
AU - Shieh, Hsin Jang
AU - Huang, Po Kai
AU - Teng, Li Tao
PY - 2006/9
Y1 - 2006/9
N2 - Because the control performance of a piezo-actuator is always severely deteriorated due to hysteresis effect, an adaptive control with hysteresis estimation and compensation using recurrent fuzzy neural network (RFNN) is proposed in this study to improve the control performance of the piezo-actuator. A new hysteresis model by modifying and parameterizing the hysteresis friction model is proposed. Then, the overall dynamics of the piezo-actuator is completed by integrating the parameterized hysteresis model into a mechanical motion dynamics. Based on this developed dynamics, an adaptive control with hysteresis estimation and compensation is proposed. However, in the designed adaptive controller, the lumped uncertainty E is difficult to obtain in practical application. Therefore, a RPNN is adopted as an uncertainty observer in order to adapt the value of the lumped uncertainty Ê on line. And, some experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust to the variations of system parameters and external load.
AB - Because the control performance of a piezo-actuator is always severely deteriorated due to hysteresis effect, an adaptive control with hysteresis estimation and compensation using recurrent fuzzy neural network (RFNN) is proposed in this study to improve the control performance of the piezo-actuator. A new hysteresis model by modifying and parameterizing the hysteresis friction model is proposed. Then, the overall dynamics of the piezo-actuator is completed by integrating the parameterized hysteresis model into a mechanical motion dynamics. Based on this developed dynamics, an adaptive control with hysteresis estimation and compensation is proposed. However, in the designed adaptive controller, the lumped uncertainty E is difficult to obtain in practical application. Therefore, a RPNN is adopted as an uncertainty observer in order to adapt the value of the lumped uncertainty Ê on line. And, some experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust to the variations of system parameters and external load.
UR - http://www.scopus.com/inward/record.url?scp=33748362217&partnerID=8YFLogxK
U2 - 10.1109/TUFFC.2006.1678193
DO - 10.1109/TUFFC.2006.1678193
M3 - 期刊論文
C2 - 16964915
AN - SCOPUS:33748362217
SN - 0885-3010
VL - 53
SP - 1649
EP - 1660
JO - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
JF - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
IS - 9
M1 - 1678193
ER -