Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network
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 pro...
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MDPI AG
2024-10-01
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| author | Thi Thu Nguyen Luke Oduor Otieno Oyoo Michael Juma Thi Ngoc Nguyen Yong Joong Lee |
| author_facet | Thi Thu Nguyen Luke Oduor Otieno Oyoo Michael Juma Thi Ngoc Nguyen Yong Joong Lee |
| author_sort | Thi Thu Nguyen |
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| description | 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%. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-0825 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Actuators |
| spelling | doaj-art-69f2b6447c8a43dda6ecc36a1ed380212025-08-20T02:10:57ZengMDPI AGActuators2076-08252024-10-01131039110.3390/act13100391Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural NetworkThi Thu Nguyen0Luke Oduor Otieno1Oyoo Michael Juma2Thi Ngoc Nguyen3Yong Joong Lee4School of Mechanical Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of KoreaDepartment of Electrical and Electronic Engineering, Dedan Kimathi University of Technology, Private Bag-10143, Dedan Kimathi, Nyeri 10143, KenyaSchool of Mechanical Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of KoreaSchool of Mechanical Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of KoreaDepartment of Smart Mobility Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of KoreaPiezoelectric 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%.https://www.mdpi.com/2076-0825/13/10/391high-speed atomic force microscopy (HS-AFM)hysteresisnonlinear systemDenseNet-type neural network |
| spellingShingle | Thi Thu Nguyen Luke Oduor Otieno Oyoo Michael Juma Thi Ngoc Nguyen Yong Joong Lee Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network Actuators high-speed atomic force microscopy (HS-AFM) hysteresis nonlinear system DenseNet-type neural network |
| title | Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network |
| title_full | Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network |
| title_fullStr | Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network |
| title_full_unstemmed | Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network |
| title_short | Nonlinear Modeling of a Piezoelectric Actuator-Driven High-Speed Atomic Force Microscope Scanner Using a Variant DenseNet-Type Neural Network |
| title_sort | nonlinear modeling of a piezoelectric actuator driven high speed atomic force microscope scanner using a variant densenet type neural network |
| topic | high-speed atomic force microscopy (HS-AFM) hysteresis nonlinear system DenseNet-type neural network |
| url | https://www.mdpi.com/2076-0825/13/10/391 |
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