Nonlinear Black-box System Identification through Neural Networks of a Hysteretic Piezoelectric Robotic Micromanipulator

Abstract

Piezoelectric micromanipulators are used in applications with precise and high dynamics positioning. This recognition is thanks to their high resolution, bandwidth and stiffness. Its nonlinear behavior, however, complicates the design of robust control laws with respect to no or imprecise sensing. In this context, this work presents the identification of a piezoelectric micromanipulator through nonlinear black-box neural networks with data acquired in a laboratory setup. A comparison is made regarding the model complexity. The results show the accuracy of the models, their statistical validity and that they were able to capture the dynamics of the micromanipulator adequately.