Performing actuation in nanomanipulation at the necessary accuracy is largely possible thanks to many new piezoelectric
actuation systems. Although piezoelectric actuators can provide means to perform near infinitely small displacements at
extremely high resolutions, the output of the actuator motion can be quite nonlinear, especially under voltage based
In this work, we will cover some of the control issues, related especially to piezoelectric actuation in nanomanipulation
tasks. We will also take a look at some of the recent improvements made possible by methods utilizing artificial neural
networks for improving the generalization capability and the accuracy of piezoelectric hysteresis models used in inverse
modelling and control of the solid-state voltage controlled piezoelectric actuators.
We will also briefly discuss the problem areas in which the piezoelectric control method research should be especially
focused on and some of the weaknesses of the existing methods. In addition, some of the common issues related to
testing and result representations are discussed.
Micro- and nanoresolution applications are important part of functional material research, where imaging and
observation of material interaction may go down to the molecular or even atomic level. Much of the nanometer range
movement of scanning and manipulation instruments is made possible by usage of piezoelectric actuation systems.
This paper presents a software based controller implementation utilizing neural networks for high precision positioning
of a piezoelectric actuator. The controller developed can be used for controlling nanopositioning piezo actuators when
sufficiently accurate feedback information is available.
Piezo actuators exhibit complex hysteresis dynamics that need to be taken into account when designing an accurate
control system. For inverse modelling purposes of the hysteresis related phenomena, a static hysteresis operator and a
new developed dynamic creep operator are presented to be used in conjunction with a feed-forward type neural
network. The controller utilizing the neural network inverse hybrid model is implemented as a software component for
the existing Scalable Modular Control framework (SMC). Using the SMC framework and off-the-shelf components, a
measurement and control system for the nanopositioning actuator is constructed and tested using two different
capacitive sensors operating on y- and z-axes of the actuator.
Using the developed controller, piezo actuator related hysteresis phenomena were successfully reduced making the
nanometer range positioning of the actuator axes possible. Also, the effect of using a lower accuracy position sensor
with more noise to control accuracy is briefly discussed.