TY - JOUR
T1 - Intelligent nonsingular terminal sliding-mode control using MIMO elman neural network for piezo-flexural nanopositioning stage
AU - Lin, Faa Jeng
AU - Lee, Shih Yang
AU - Chou, Po Huan
N1 - Funding Information:
manuscript received may 30, 2012; accepted september 16, 2012. The author acknowledges the financial support of the national science council of Taiwan, r.o.c., through its grant nsc 101-2221-E-008-105-my3. F.-J. lin and s.-y. lee are with the department of Electrical Engineering, national central University, chungli, Taiwan (e-mail: linfj@ ee.ncu.edu.tw). P.-H. chou is with the department of mechatronics control, Industrial Technology research Institute, Hsinchu, Taiwan. doI http://dx.doi.org/10.1109/TUFFc.2012.2513
PY - 2012
Y1 - 2012
N2 - The objective of this study is to develop an intelligent nonsingular terminal sliding-mode control (INTSMC) system using an Elman neural network (ENN) for the threedimensional motion control of a piezo-flexural nanopositioning stage (PFNS). First, the dynamic model of the PFNS is derived in detail. Then, to achieve robust, accurate trajectory-tracking performance, a nonsingular terminal sliding-mode control (NTSMC) system is proposed for the tracking of the reference contours. The steady-state response of the control system can be improved effectively because of the addition of the nonsingularity in the NTSMC. Moreover, to relax the requirements of the bounds and discard the switching function in NTSMC, an INTSMC system using a multi-input-multioutput (MIMO) ENN estimator is proposed to improve the control performance and robustness of the PFNS. The ENN estimator is proposed to estimate the hysteresis phenomenon and lumped uncertainty, including the system parameters and external disturbance of the PFNS online. Furthermore, the adaptive learning algorithms for the training of the parameters of the ENN online are derived using the Lyapunov stability theorem. In addition, two robust compensators are proposed to confront the minimum reconstructed errors in INTSMC. Finally, some experimental results for the tracking of various contours are given to demonstrate the validity of the proposed INTSMC system for PFNS.
AB - The objective of this study is to develop an intelligent nonsingular terminal sliding-mode control (INTSMC) system using an Elman neural network (ENN) for the threedimensional motion control of a piezo-flexural nanopositioning stage (PFNS). First, the dynamic model of the PFNS is derived in detail. Then, to achieve robust, accurate trajectory-tracking performance, a nonsingular terminal sliding-mode control (NTSMC) system is proposed for the tracking of the reference contours. The steady-state response of the control system can be improved effectively because of the addition of the nonsingularity in the NTSMC. Moreover, to relax the requirements of the bounds and discard the switching function in NTSMC, an INTSMC system using a multi-input-multioutput (MIMO) ENN estimator is proposed to improve the control performance and robustness of the PFNS. The ENN estimator is proposed to estimate the hysteresis phenomenon and lumped uncertainty, including the system parameters and external disturbance of the PFNS online. Furthermore, the adaptive learning algorithms for the training of the parameters of the ENN online are derived using the Lyapunov stability theorem. In addition, two robust compensators are proposed to confront the minimum reconstructed errors in INTSMC. Finally, some experimental results for the tracking of various contours are given to demonstrate the validity of the proposed INTSMC system for PFNS.
UR - http://www.scopus.com/inward/record.url?scp=84871022242&partnerID=8YFLogxK
U2 - 10.1109/TUFFC.2012.2513
DO - 10.1109/TUFFC.2012.2513
M3 - 期刊論文
C2 - 23221221
AN - SCOPUS:84871022242
VL - 59
SP - 2716
EP - 2730
JO - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
JF - IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
SN - 0885-3010
IS - 12
M1 - 6373795
ER -