Adaptive Inverse Control of Piezoelectric Actuators Based on Segment Similarity

Liu, Xiangdong; Huang, Mengqi; Xiong, Rui; Shan, Jinjun; Mao, Xuefei

doi:10.1109/tie.2018.2868011

Cited by 23 publications

(7 citation statements)

References 27 publications

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“…The ILC method is similar to the MRC method and a suitable self-learning method can improve its control accuracy. On the basis of the segment similarity model, Liu et al [23] proposed an adaptive inverse control to restrain the hysteresis of PEAs. This method relies on the segment similarity model to restrict the hysteresis accuracy of PEAs.…”

Section: Literature Reviewmentioning

confidence: 99%

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Fractional order sliding mode control based on single parameter adaptive law for nano‐positioning of piezoelectric actuators

Zhang

Wang

et al. 2021

IET Control Theory & Appl

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A fractional order sliding mode control (FOSMC) based on single parameter adaptive law for nano-positioning of Piezoelectric Actuators (PEAs) is proposed. First, the Bouc-Wen (B-W) model is used to describe the hysteresis of the nano-position platform based on PEAs, which provides a mathematical model for the subsequent controller design. Then, theoretical support is provided to design the FOSMC based on adaptive law of different parameters, which are proposed for the displacement tracking problem of PEAs, and the position error convergence is also proved. Moreover, the core parameters of FOSMC based on single parameter adaptive law are identified by hybrid differential evolution (HDE) and adaptive differential evolution (ADE), which require considering the relationship between the scaling factor and the cross-probability factor. Finally, experiments have been conducted with the displacement signals mixed with multiple frequencies and multiple amplitudes and the results obtained from them show that the proposed control scheme can produce a faster response and smaller tracking errors in PEAs system as compared to traditional control algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Literature Reviewmentioning

confidence: 99%

“…On the basis of the segment similarity model, Liu et al. [23] proposed an adaptive inverse control to restrain the hysteresis of PEAs. This method relies on the segment similarity model to restrict the hysteresis accuracy of PEAs.…”

Section: Introductionmentioning

confidence: 99%

Fractional order sliding mode control based on single parameter adaptive law for nano‐positioning of piezoelectric actuators

Zhang

Wang

et al. 2021

IET Control Theory & Appl

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“…However, in order to establish a more accurate kinetic model, the nonlinear factors are usually modeled, and the system parameters are obtained by system model identification (Chen et al, 2012a, b;. In the process of model identification and controller design of a high-precision servo system, nonlinear factors are the main reason for the decline of system dynamic performance, which affects the design, simulation, and debugging of the controller (Merit et al, 2009;Liu et al, 2018;Consolatina et al, 2019;Zhu et al, 2019;Nie et al, 2022). Wavre et al (1995) proposed that the selfinductance of the coil of the closed voice coil motor is not affected by the position, and the linearity is better.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear characteristics of the driving model of the coaxial integrated macro–micro composite actuator

Wang

Xiao

et al. 2022

Mech. Sci.

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Abstract. Nonlinearity is one of the important factors affecting the positioning accuracy of the macro–micro composite actuator. To improve the positioning accuracy of the driving model of the macro–micro composite actuator, this paper combines the research phenomenon of the nonlinear characteristics of the voice coil motor to model the nonlinear factors that affect the macro-moving part of the macro–micro composite actuator. Firstly, based on analyzing its structure and working principle, the variation law of the magnetic field intensity at the working air gap of the macro-motion part is analyzed by the finite element method, and the driving force model of the macro-motion part is established. Secondly, through the magnetic field simulation analysis, there is a magnetization phenomenon in the mover part, and the static friction model is established. Then, the experimental data are acquired and processed by building the experimental test platform of the actuator, and the variation model of the electromechanical time constant with the macro-motion displacement is established. Then, combined with the Stribeck model and the static friction model, the kinetic model of the macro-motion part is established. Finally, using the least square method identify the parameter model, the results are compared with the experiment. The results show that the magnetic field distribution at the working air gap of the macro-motion part of the macro–micro composite actuator is relatively uniform, but it is related to the macro-motion displacement and the macro-motion coil current. When the macro-motion part of the macro-micro composite actuator starts, the friction model can approximately reflect the change of friction force, the kinetic model of the macro-motion part can reflect the dynamic characteristics of the macro-motion part, and the matching degree is 92.97 %. The research results lay a theoretical and technical foundation for the development of a high-speed and large-stroke positioning controller of the macro-motion micro composite actuator.

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“…For instance, based on a modified Bouc-Wen model, a feedforward compensation approach is proposed, 8 an iterative learning control and a direct inverse hysteresis compensation have been designed to achieve high-precision reference tracking, 9 and an adaptive inverse control is also proposed to address the hysteresis. 10 Those approaches are based on accurate hysteresis (inverse) models. 11 However, modeling is time-consuming and the established model cannot describe the hysteresis accurately.…”

Section: Introductionmentioning

confidence: 99%

Active disturbance rejection control for a piezoelectric nano-positioning system: A U-model approach

Wei

Duan

Zuo

et al. 2021

Measurement and Control

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Both speed and accuracy are key issues in nano-positioning. However, hysteresis existing in piezoelectric actuators severely reduces the positioning speed and accuracy. In order to address the hysteresis, a U-model based active disturbance rejection control is proposed. Based on the linear active disturbance rejection control, a controlled plant is dynamically transformed to be pure integrators. Then, according to the U-model control, a common inversion is obtained and the controlled plant is converted to be “1.” By integrating advantages of both linear active disturbance rejection control and U-model control, the U-model based active disturbance rejection control does promote the reference tracking speed and accuracy. Stability and steady-state error of the close-loop system have been analyzed. Phase lag between the system output and the control input has been effectively eliminated, and the phase-leading advantage of the U-model based active disturbance rejection control has been confirmed. Experimental results show that the U-model based active disturbance rejection control is capable of achieving faster and more accurate positioning. Remarkable improvements and practical realization make the U-model based active disturbance rejection control more promising in nano-positioning.

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Adaptive Inverse Control of Piezoelectric Actuators Based on Segment Similarity

Cited by 23 publications

References 27 publications

Fractional order sliding mode control based on single parameter adaptive law for nano‐positioning of piezoelectric actuators

Fractional order sliding mode control based on single parameter adaptive law for nano‐positioning of piezoelectric actuators

Nonlinear characteristics of the driving model of the coaxial integrated macro–micro composite actuator

Active disturbance rejection control for a piezoelectric nano-positioning system: A U-model approach

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