Artificial neural network based hysteresis compensation for piezoelectric tube scanner in atomic force microscopy

• Main Authors: Othman, Yahya Sheriff, Mahmood, Iskandar Al-Thani, Alang Md Rashid, Nahrul Khair, Darsivan, Fadly Jashi
• Format: Conference or Workshop Item
• Language: English
• Published: 2012
• Subjects: TJ212 Control engineering
• Online Access: http://irep.iium.edu.my/32299/1/Artificial_Neural_Network_Based_Hysteresis_Compensation_for_Piezoelectric_Tube_Scanner_in_Atomic_Force_Microscopy.pdf; http://irep.iium.edu.my/32299/; http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6412244&tag=1

Piezoelectric tube scanner is a major component that used in nanoscale imaging tools such as atomic force microscopy (AFM). This is because it can provide precise nanoscale positioning. However the precision is limited by vibration and some nonlinear drawbacks represented by creep and hysteresis. Hysteresis problem appears when positioning is needed at wide range. In this paper, a feed forward multi-layer neural network (MLNN) is trained to shape a proper control signal based on reference input and actual output signals. The experimental results show that the developed neural network scheme improves the performance of the system by significantly minimizing the effect of hysteresis.