A fast and accurate piezoelectric actuator modeling method based on truncated least squares support vector regression

Xiangdong, Liu; Ma, Zhibiao; Mao, Xuefei; Shan, Jinjun; Wang, Yijun

doi:10.1063/1.5086491

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…To overcome the overfitting problem, the authors in [ 26 , 27 , 28 , 29 ] proposed hysteresis models based on a regression algorithm. The input of the hysteresis model was selected based on an autoregressive model with exogenous input (NARX), where the current output is dependent not only on the current inputs but also on the past inputs and past outputs.…”

Section: Introductionmentioning

confidence: 99%

Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

2023

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The nonlinear hysteresis phenomenon can occur in piezoelectric-driven nanopositioning systems and can lead to reduced positioning accuracy or result in a serious deterioration of motion control. The Preisach method is widely used for hysteresis modeling; however, for the modeling of rate-dependent hysteresis, where the output displacement of the piezoelectric actuator depends on the amplitude and frequency of the input reference signal, the desired accuracy cannot be achieved with the classical Preisach method. In this paper, the Preisach model is improved using least-squares support vector machines (LSSVMs) to deal with the rate-dependent properties. The control part is then designed and consists of an inverse Preisach model to compensate for the hysteresis nonlinearity and a two-degree-of-freedom (2-DOF) H-infinity feedback controller to enhance the overall tracking performance with robustness. The main idea of the proposed 2-DOF H-infinity feedback controller is to find two optimal controllers that properly shape the closed-loop sensitivity functions by imposing some templates in terms of weighting functions in order to achieve the desired tracking performance with robustness. The achieved results with the suggested control strategy show that both hysteresis modeling accuracy and tracking performance are significantly improved with average root-mean-square error (RMSE) values of 0.0107 μm and 0.0212 μm, respectively. In addition, the suggested methodology can achieve better performance than comparative methods in terms of generalization and precision.

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Section: Introductionmentioning

confidence: 99%

Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

2023

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“…As LSSVM only deals with one-to-one mapping, whereas hysteresis is defined as multi-valued, few studies have attempted to provide a suitable hysteresis map, often based on expanding input space into multidimensional space. For instance, the authors in [19][20][21][22] used nonlinear autoregressive exogenous (NARX) models in which the input was expanded to include current and past inputs, as well as the past outputs. Although the results showed that the outputs could be well-predicted, the output of the inverse LSSVM-NARX model that is fed to its input usually results in accumulated errors over time [23,24].…”

Section: Introductionmentioning

confidence: 99%

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

et al. 2022

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Nanopositioning technology is widely used in high-resolution applications. It often uses piezoelectric actuators due to their superior characteristics. However, piezoelectric actuators exhibit a hysteresis phenomenon that limits their positioning accuracy. To compensate for the hysteresis effect, developing an accurate hysteresis model of piezoelectric actuators is very important. This task is challenging, requiring some considerations of the multivalued mapping of hysteresis loops and the generalization capabilities of the model. This challenge can be dealt with by developing a machine learning-based model, whose inverse model can be used to efficiently design an accurate feedforward controller for hysteresis compensation. However, this approach depends on model accuracy and the type of data used to train the model. Thus, accurate prediction of the hysteresis behavior may not be guaranteed in the presence of disturbances. In this paper, a machine learning-based model is used to design a hysteresis compensator and then combined with a robust feedback controller to enhance the robustness of a nanopositioning control system. The proposed model is based on hysteresis operators, the least square support vector machine (LSSVM) method, and particle swarm optimization (PSO) algorithm. The inverse model is used to design the feedforward controller, and the RST controller is employed to develop feedback control. Our main contribution is the introduction of a hybrid controller capable of compensating for the hysteresis effect, and at the same time, eliminating remaining modeling errors and rejecting disturbances. The performance of the proposed approach is evaluated through MATLAB simulation, as well as through real-time experiments. The experimental results of our approach demonstrate superior tracking performance compared with the PID-LSSVM controller.

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A review on control strategies for compensation of hysteresis and creep on piezoelectric actuators based micro systems

Sabarianand

Karthikeyan

Muthuramalingam

2020

Mechanical Systems and Signal Processing

173

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A fast and accurate piezoelectric actuator modeling method based on truncated least squares support vector regression

Cited by 6 publications

References 20 publications

Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

Precision Motion Control of a Piezoelectric Actuator via a Modified Preisach Hysteresis Model and Two-Degree-of-Freedom H-Infinity Robust Control

Robust Tracking Control of Piezo-Actuated Nanopositioning Stage Using Improved Inverse LSSVM Hysteresis Model and RST Controller

A review on control strategies for compensation of hysteresis and creep on piezoelectric actuators based micro systems

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