Dynamic modeling and performance evaluation of piezoelectric impact drive system based on neural network

Abstract

In metrology and industrial design, the evaluation of measurement uncertainty and error is crucial to the measurement process. The Guide to the Expression of Uncertainty in Measurement and its supplementary documents have established a unified framework and standard for evaluating measurement uncertainty. However, a reasonable method for evaluating dynamic measurement uncertainty has not yet been proposed. By analyzing the dynamic measurement system, and using the long short-term memory time neural network to model the nonlinear dynamics represented by a piezoelectric drive platform, this paper evaluates the system’s dynamic measurement uncertainty through deep integration methods. Bayesian theory is used to propagate probability densities, and experimental results demonstrate the effectiveness of this method for assessing dynamic measurement uncertainty.