Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

Abstract

A modeling and parameter identification method for rate dependent hysteresis of piezoelectric actuated nano-stage is presented in this work. A system level quasi-static hysteresis model is employed to construct a neural network. To better describe the rate dependent behavior of hysteresis in piezoelectric actuated stage, a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) based dynamic model is incorporated with the quasi-static hysteresis model, where the weights of specifically designed neural network corresponds to the model parameters. To handle the multivalued problem of hysteresis, generalized input gradient is proposed to convert multivalued mapping of hysteresis into one-toone mapping. The parameters of the nonlinear rate dependent hysteresis in piezoelectric actuated stage is identified by neural network training, taking advantage of their universal function approximation capabilities. The proposed scheme is also compared with conventional black box and particle swarm optimization identification (PSO) based methods, where simulation and experimental results demonstrate significant performance improvement with the proposed method.

Neural network based predictive control with optimized search space for dynamic tracking of a piezo-actuated nano stage

“…The dynamic behavior of the piezo-actuated nano stage is described by NARMAX-based dynamic sub-model 3(b). For the gray box neural network-based static sub-model the displacement d H ( k ) is given as (Ahmed and Yan, 2021):…”

“…Neural network based model of piezo-actuated nano stage: (a) static sub model of piezo-actuated nano stage (Ahmed and Yan, 2021) and (b) dynamic sub-model of piezo-actuated nano stage.…”

“…Although the conventional neural network based modeling and control methods are proven to be effective but training time is too long which increases computation burden. Gray box neural network based model presented in Ahmed and Yan (2021) can describe the behavior hysteresis behavior of piezo-actuated nano stage with high precision and low computation burden. Due to its high performance and constraints handling abilities, Model Predictive Control (MPC) is widely used in industrial applications (Gong et al, 2016; Zhou et al, 2021).…”

Computing Frequency-Dependent Hysteresis Loops and Dynamic Energy Losses in Soft Magnetic Alloys via Artificial Neural Networks

“…The principal scope of this research is to give a contribution to the development of the dynamic models of hysteresis for innovative soft ferromagnetic materials, exploiting artificial neural networks (ANNs). Until now, ANNs have been successfully applied in the development of both scalar [23][24][25] and vector models of static hysteresis [26][27][28], but fewer studies also take the rate dependence into account [29][30][31][32]. The main advantages of neural network-based models are related to their cheap memory allocation and high computational speed, especially when implemented at a low level of abstraction.…”

“…This is also a common approach for rate-dependent models based on artificial neural networks. For instance, in [31], the authors developed a dynamic model of a piezoelectric actuator exploiting an artificial neural network coupled with a Nonlinear Autoregressive Moving Average Model with Exogeneous Inputs (NARMAX). In [29], the authors proposed modeling of the frequency-dependent hysteresis loops via an array of feedforward neural networks, each one working on a specific interval of the H field axis, whilst in [30], the fully connected cascade architecture was explored.…”