Adaptive hysteresis compensation on an experimental nanopositioning platform
Résumé
This paper presents a novel adaptive approach for hysteresis compensation applied to a piezoelectric actuator in one axis of an STM-like lab-made micro-/nanopositioning platform. The idea is to identify a compensating static parametric model, which imitates directly the inverse model of the nonlinearity. In this way, the approach is less complex than those based on model inversion. In addition, the identification is made online, allowing to consider a simple polynomial model, and to adapt its parameters according to the actual hysteresis curve which is faced (ascending or descending path, varying input amplitude, etc.). In order to be able to track possibly fast parameter variations, an original adaptation algorithm is proposed within the Bayesian framework, and including an exponential forgetting factor with optimal data-driven tuning. Illustrative experimental results are finally presented for tracking both triangular and sinusoidal reference signals with varying amplitude.