From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

An adaptive weighted least square support vector regression for hysteresis in piezoelectric actuators

© 2017 Elsevier B.V. To overcome the low positioning accuracy of piezoelectric actuators (PZAs) caused by the hysteresis nonlinearity, this paper proposes an adaptive weighted least squares support vector regression (AWLSSVR) to model the rate-dependent hysteresis of PZA. Firstly, the AWLSSVR hyperparameters are optimized by using particle swarm optimization. Then an adaptive weighting strategy is proposed to eliminate the effects of noises in the training dataset and reduce the sample size at the same time. Finally, the proposed approach is applied to predict the hysteresis of PZA. The results show that the proposed method is more accurate than other versions of least squares support vector regression for training samples with noises, and meanwhile reduces the sample size and speeds up calculation

Frequency domain approach for dynamics identification of the actuator with asymmetric hysteresis

postprin

Modeling and Compensation of Hysteresis In Piezoelectric Actuators: A Physical Approach

A study in the polarization domain is conducted by probing the impedance of the piezoelectric actuator as it moves along its trajectory. A sensing signal is overlaid over a driving signal that is used to vary the position of the device. The electric polarisation is extracted from the capacitance measurement calculated from the impedance. These polarisation curves are then modelled using the Jiles-Atherton model and compensated for using the inverse model. These measurements give insight into the ferroelectric processes within the piezoelectric actuator, which operate on the polarisation state. In addition, research has been conducted on the topic of parameter estimation of hysteresis models. This dissertation proposes a Monte Carlo study on a novel normalised Jiles-Atherton model to generate a statistical set of model solutions to compare area and remnant displacement characteristics for different parameter selections. Two parameters were found to be the most responsible for changes in these characteristics, and solutions near the desired values of the measured hysteresis curves were found to be densely distributed in certain areas of the parameter space. Different parameter estimation techniques are proposed for the Prandtl-Ishlinskii model. For this model, the parameters have geometrical significance in the slope of certain points of the hysteresis curve. A novel rescaling procedure is developed to scale a Prandtl-Ishlinskii model hysteresis curve area to a new value without requiring a refitting of the coefficients and a frequency-dependent Prandtl-Ishlinskii model is developed. Finally, a temperature-dependent, asymmetric Prandtl-Ishlinskii (TAPI) model is developed to account for the changes in hysteresis due to the external temperature. These effects are modelled in the charge domain as an extra bound charge that appears as a result of domain reorientation effects. The temperature effectively changes the amount of energy required to break pinning sites in the actuator which changes the shape of the curve. The TAPI model is then implemented on a Fabry-Perot interferometer system consisting of three piezoelectric actuators controlling the placement of a mirror forming the etalon. A decoupled inverse TAPI model is shown to effectively linearise the output of this system at different temperatures

Modeling and Compensation of Hysteresis In Piezoelectric Actuators: A Physical Approach

A study in the polarization domain is conducted by probing the impedance of the piezoelectric actuator as it moves along its trajectory. A sensing signal is overlaid over a driving signal that is used to vary the position of the device. The electric polarisation is extracted from the capacitance measurement calculated from the impedance. These polarisation curves are then modelled using the Jiles-Atherton model and compensated for using the inverse model. These measurements give insight into the ferroelectric processes within the piezoelectric actuator, which operate on the polarisation state. In addition, research has been conducted on the topic of parameter estimation of hysteresis models. This dissertation proposes a Monte Carlo study on a novel normalised Jiles-Atherton model to generate a statistical set of model solutions to compare area and remnant displacement characteristics for different parameter selections. Two parameters were found to be the most responsible for changes in these characteristics, and solutions near the desired values of the measured hysteresis curves were found to be densely distributed in certain areas of the parameter space. Different parameter estimation techniques are proposed for the Prandtl-Ishlinskii model. For this model, the parameters have geometrical significance in the slope of certain points of the hysteresis curve. A novel rescaling procedure is developed to scale a Prandtl-Ishlinskii model hysteresis curve area to a new value without requiring a refitting of the coefficients and a frequency-dependent Prandtl-Ishlinskii model is developed. Finally, a temperature-dependent, asymmetric Prandtl-Ishlinskii (TAPI) model is developed to account for the changes in hysteresis due to the external temperature. These effects are modelled in the charge domain as an extra bound charge that appears as a result of domain reorientation effects. The temperature effectively changes the amount of energy required to break pinning sites in the actuator which changes the shape of the curve. The TAPI model is then implemented on a Fabry-Perot interferometer system consisting of three piezoelectric actuators controlling the placement of a mirror forming the etalon. A decoupled inverse TAPI model is shown to effectively linearise the output of this system at different temperatures

An Asymmetric Hysteresis Model and Parameter Identification Method for Piezoelectric Actuator

Hysteresis behaviour degrades the positioning accuracy of PZT actuator for ultrahigh-precision positioning applications. In this paper, a corrected hysteresis model based on Bouc-Wen model for modelling the asymmetric hysteresis behaviour of PZT actuator is established by introducing an input bias φ and an asymmetric factor ΔΦ into the standard Bouc-Wen hysteresis model. A modified particle swarm optimization (MPSO) algorithm is established and realized to identify and optimize the model parameters. Feasibility and effectiveness of MPSO are proved by experiment and numerical simulation. The research results show that the corrected hysteresis model can represent the asymmetric hysteresis behaviour of the PZT actuator more accurately than the noncorrected hysteresis model based on the Bouc-Wen model. The MPSO parameter identification method can effectively identify the parameters of the corrected and noncorrected hysteresis models. Some cases demonstrate the corrected hysteresis model and the MPSO parameter identification method can be used to model smart materials and structure systems with the asymmetric hysteresis behaviour

Hysteresis Characterization andCompensation of Smart Material-BasedActuators via a New Modified Bouc-WenModel

芝浦工業大学2016年

Modeling the Vibrational Dynamics of Piezoelectric Actuator by System Identification Technique

Actuators based on smart materials such as piezoelectric actuators (PEAs) are widely used in many applications to transform electrical signal to mechanical signal and vice versa. However, the major drawbacks for these smart actuators are hysteresis nonlinear, creep and residual vibration. In this paper, PEAs are used for active vibration application. Therefore, a model of PEA must be established to control the vibration that occurs in the system. The frequencies of 1 Hz, 20 Hz and 50 Hz were tested on the PEAs. The results obtained from the experimental were used to develop transfer function model by employing system identification technique. Meanwhile, the model validation was based on level of models fitness to estimation data, mean squared error (MSE), final prediction error (FPE) and correlation test. The experimental result showed that the displacement of the actuator is inversely proportional to the frequency. The following consequences caused the time response criteria at 50 Hz achieved smallest overshoot and fastest response of rise time and settling time