Artificial neural network based hysteresis compensation for piezoelectric tube scanner in atomic force microscopy

Othman, Y. S.; Mahmood, Iskandar Al-Thani; Rashid, Nahrul Khair Alang Md; Darsivan, Fadly Jashi

doi:10.1109/tencon.2012.6412244

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The proposed method has a better performance than the backpropagation neural network with 0.01 least mean square error (MSE). Othman et al [22] have used an ANN for the compensation of hysteresis of a piezoelectric tube scanner. Patra et al [23] have designed the smart pressure sensor using ANN and compensate non-linearity, offset, and temperature dependency.…”

Section: Introductionmentioning

confidence: 99%

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ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

Lata

Mandal

2020

IET Science, Measurement &Amp; Technology

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The proposed liquid level transmitter using a force resistive sensor as a secondary sensor is simple, cost-effective, and applicable for all types of liquid. The force resistive sensor is located at the bottom of a liquid measuring tank. The pressure exerted by the liquid height on the measuring tank changes the force resistive sensor's resistance. In the long term application, due to the creeping behaviour of force resistive sensor, the proposed level transmitter has low repeatability, hysteresis, and nonlinearity. In this study, the artificial neural network (ANN) has been used to eliminate the hysteresis and non-linearity present in the proposed liquid level transmitter. The performance of the proposed technique has been experimentally verified. The hysteresis and non-linearity error has been decreased from 7 to 0.89% of full-scale observation (FSO) and 9 to 0.14%, respectively.

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Section: Introductionmentioning

confidence: 99%

“…Othman et al . [22] have used an ANN for the compensation of hysteresis of a piezoelectric tube scanner. Patra et al .…”

Section: Introductionmentioning

confidence: 99%

ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

Lata

Mandal

2020

IET Science, Measurement &Amp; Technology

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Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network

Nguyen,

Otieno,

Juma

et al. 2024

Actuators

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Piezoelectric actuators (PEAs) are extensively used for scanning and positioning in scanning probe microscopy (SPM) due to their high precision, simple construction, and fast response. However, there are significant challenges for instrument designers due to their nonlinear properties. Nonlinear properties make precise and accurate control difficult in cases where position feedback sensors cannot be employed. However, the performance of PEA-driven scanners can be significantly improved without position feedback sensors if an accurate mathematical model with low computational costs is applied to reduce hysteresis and other nonlinear effects. Various methods have been proposed for modeling PEAs, but most of them have limitations in terms of their accuracy and computational efficiencies. In this research, we propose a variant DenseNet-type neural network (NN) model for modeling PEAs in an AFM scanner where position feedback sensors are not available. To improve the performance of this model, the mapping of the forward and backward directions is carried out separately. The experimental results successfully demonstrate the efficacy of the proposed model by reducing the relative root-mean-square (RMS) error to less than 0.1%.

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Artificial neural network based hysteresis compensation for piezoelectric tube scanner in atomic force microscopy

Cited by 2 publications

References 11 publications

ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

ANN-based liquid level transmitter using force resistive sensor for minimisation of hysteresis and non-linearity error

Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network

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