Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator
Piezoelectric actuated models are promising high-performance precision positioning devices used for broad applications in the field of precision machines and nano/micro manufacturing. Piezoelectric actuators involve a nonlinear complex hysteresis that may cause degradation in performance. These hyst...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/13/12/2205 |
| _version_ | 1827637655371776000 |
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| author | Sidra Naz Muhammad Asif Zahoor Raja Ammara Mehmood Aneela Zameer Jaafery |
| author_facet | Sidra Naz Muhammad Asif Zahoor Raja Ammara Mehmood Aneela Zameer Jaafery |
| author_sort | Sidra Naz |
| collection | DOAJ |
| description | Piezoelectric actuated models are promising high-performance precision positioning devices used for broad applications in the field of precision machines and nano/micro manufacturing. Piezoelectric actuators involve a nonlinear complex hysteresis that may cause degradation in performance. These hysteresis effects of piezoelectric actuators are mathematically represented as a second-order system using the Dahl hysteresis model. In this paper, artificial intelligence-based neurocomputing feedforward and backpropagation networks of the Levenberg–Marquardt method (LMM-NNs) and Bayesian Regularization method (BRM-NNs) are exploited to examine the numerical behavior of the Dahl hysteresis model representing a piezoelectric actuator, and the Adams numerical scheme is used to create datasets for various cases. The generated datasets were used as input target values to the neural network to obtain approximated solutions and optimize the values by using backpropagation neural networks of LMM-NNs and BRM-NNs. The performance analysis of LMM-NNs and BRM-NNs of the Dahl hysteresis model of the piezoelectric actuator is validated through convergence curves and accuracy measures via mean squared error and regression analysis. |
| first_indexed | 2024-03-09T16:04:53Z |
| format | Article |
| id | doaj.art-207ced3e790c415aa96dd14a328a2089 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T16:04:53Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-207ced3e790c415aa96dd14a328a20892023-11-24T16:45:58ZengMDPI AGMicromachines2072-666X2022-12-011312220510.3390/mi13122205Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric ActuatorSidra Naz0Muhammad Asif Zahoor Raja1Ammara Mehmood2Aneela Zameer Jaafery3Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Islamabad 45650, PakistanFuture Technology Research Center, National Yunlin University of Science and Technology, Yunlin 64002, TaiwanSchool of Engineering, RMIT University, Melbourne 3001, AustraliaDepartment of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad 45650, PakistanPiezoelectric actuated models are promising high-performance precision positioning devices used for broad applications in the field of precision machines and nano/micro manufacturing. Piezoelectric actuators involve a nonlinear complex hysteresis that may cause degradation in performance. These hysteresis effects of piezoelectric actuators are mathematically represented as a second-order system using the Dahl hysteresis model. In this paper, artificial intelligence-based neurocomputing feedforward and backpropagation networks of the Levenberg–Marquardt method (LMM-NNs) and Bayesian Regularization method (BRM-NNs) are exploited to examine the numerical behavior of the Dahl hysteresis model representing a piezoelectric actuator, and the Adams numerical scheme is used to create datasets for various cases. The generated datasets were used as input target values to the neural network to obtain approximated solutions and optimize the values by using backpropagation neural networks of LMM-NNs and BRM-NNs. The performance analysis of LMM-NNs and BRM-NNs of the Dahl hysteresis model of the piezoelectric actuator is validated through convergence curves and accuracy measures via mean squared error and regression analysis.https://www.mdpi.com/2072-666X/13/12/2205piezoelectric actuatorLevenberg–MarquardtBayesian regularizationintelligent computingdahl hysteresis model |
| spellingShingle | Sidra Naz Muhammad Asif Zahoor Raja Ammara Mehmood Aneela Zameer Jaafery Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator Micromachines piezoelectric actuator Levenberg–Marquardt Bayesian regularization intelligent computing dahl hysteresis model |
| title | Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator |
| title_full | Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator |
| title_fullStr | Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator |
| title_full_unstemmed | Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator |
| title_short | Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator |
| title_sort | intelligent predictive solution dynamics for dahl hysteresis model of piezoelectric actuator |
| topic | piezoelectric actuator Levenberg–Marquardt Bayesian regularization intelligent computing dahl hysteresis model |
| url | https://www.mdpi.com/2072-666X/13/12/2205 |
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