An adaptive internal model control system of a piezo-ceramic actuator with two RBF neural networks

Liu, Dongbo; Fujii, Fumitake

doi:10.1109/icma.2014.6885697

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…The support vector machine (SVM) is superior to the artificial neural network (ANN) in terms of global optimization and generalization ability for the hysteresis loop to model. Therefore, the SVM is widely employed to estimate nonlinear systems accurately [37], especially that involving hysteresis loops [38].…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Han,

Xu,

et al. 2023

Preprint

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It is widely known that inchworm piezo motors are being used in ultra-precision detection platforms where high clamping force and nano-precision are required. This paper examines and proposes a dynamic model of the inchworm piezo motor to improve the performance and controller. Traditional identification strategies have not been adopted for calculating parameters for the dynamic model exhibiting hysteresis nonlinearity. In addition, hysteresis deteriorates the accuracy of positioning of inchworm piezo motors. It has been proposed to solve these problems using a feedforward controller, a hysteresis compensator based on a stochastic configuration networks algorithm (SCNs). Using the SCNs algorithm, both voltage and displacement inputs can be used to describe the nonlinear memory of hysteresis, and the kernel function provides a flexible and accurate representation of this behavior. This method enables the dynamic model parameters of the inchworm piezo motor to be identified using frequency sweeps. A proportional-integral (PI) controller is used to verify the positioning accuracy, and experimental results show that an inchworm piezo motor has a positioning accuracy of about 1nm when the laser interference is low with a 10M Hz adoption rate and 0.1nm resolution.

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Section: Introductionmentioning

confidence: 99%

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Han,

Xu,

et al. 2023

Preprint

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show abstract

“…Adaptive control is a dynamic and pivotal field of research in industrial applications. Over the past decade, importance of several self-adaptation-related application areas and technologies has increased [22][23][24][25][26]. It has also been applied in PEA systems by a number of researchers.…”

Section: Introductionmentioning

confidence: 99%

Adaptive feed-forward controller of piezoelectric actuator for micro/nano-positioning

et al. 2018

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In this paper, design and implementation of an adaptive feed-forward controller for micro/nanopositioning control of piezoelectric actuator (PEA) is described. Discrete-time Dahl hysteresis-based mathematical model of the PEA is developed and the values of the model parameters are estimated through an autoregressive with exogenous terms (ARX) model identification technique using experimental input-output data. A recursive least-square estimator (RLSE)-based adaptive feed-forward (FF) controller is proposed, which takes into account the parameter uncertainty. The FF controller has also been implemented in a DsPIC30F4011 microcontroller. The established PEA model and the controller are validated by simulation and experimental results including parameter variation.

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“…Its main characteristic is the simple structure and design, excellent control performance and disturbance rejection capability, especially for a nonlinear large-delay system. Therefore, many scholars have had a strong interest in internal model control and also have achieved many research results [25][26][27][28]. However, there is the thorny problem of finding a process model that represents the actual nonlinear system in the internal model control structure.…”

Section: Introductionmentioning

confidence: 99%

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

Zhao

Cao

et al. 2017

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Multivariable coupling, nonlinear and large time delays exist in the coil outlet temperature (COT) control system of the ethylene cracking furnace, which make it hard to achieve accurate control over the COT of the furnace in actual production. To solve these problems, an inverse controller based on an interval type-2 fuzzy model control strategy is introduced. In this paper, the proposed control scheme is divided into two parts: one is the approach structure part of the interval type-2 fuzzy model (IT2-FM), which is utilized to approach the process output. The other is the interval type-2 fuzzy model inverse controller (IT2-FMIC) part, which is utilized to control the output process to achieve the target value. In addition, on the cyber-physical system platform, the actual industrial data are used to test and obtain the mathematical model of the COT control system of the ethylene cracking furnace. Finally, the proposed inverse controller based on the IT2-FM control scheme has been implemented on the COT control system of the ethylene cracking furnace, and the simulation results show that the proposed method is feasible.

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An adaptive internal model control system of a piezo-ceramic actuator with two RBF neural networks

Cited by 5 publications

References 9 publications

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

An Identification Method for Dynamic Systems with SCNs Hysteresis Compensator: Application to the Inchworm Piezo Motor

Adaptive feed-forward controller of piezoelectric actuator for micro/nano-positioning

Interval Type-2 Fuzzy Model Based on Inverse Controller Design for the Outlet Temperature Control System of Ethylene Cracking Furnace

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