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Modified Nonlinear Hysteresis Approach for a Tactile Sensor

Abdul-Hussain, Gasak; Holderbaum, William; Theodoridis, Theodoros; Wei, Guowu

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Authors

Gasak Abdul-Hussain



Abstract

Soft tactile sensors based on piezoresistive materials have large-area sensing applications. However, their accuracy is often affected by hysteresis which poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fiber-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units were designed. These sensor units underwent force sequences to collect corresponding output resistance. A backpropagation network was trained using these sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network’s parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor’s full-scale output to 13.5%. This improvement established the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for the measurement and control of force, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Though the complete elimination of hysteresis in tactile sensors may not be feasible, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

Citation

Abdul-Hussain, G., Holderbaum, W., Theodoridis, T., & Wei, G. (in press). Modified Nonlinear Hysteresis Approach for a Tactile Sensor. Sensors, 23(16), 7293. https://doi.org/10.3390/s23167293

Journal Article Type Article
Acceptance Date Aug 17, 2023
Online Publication Date Aug 21, 2023
Deposit Date Sep 19, 2023
Publicly Available Date Sep 19, 2023
Journal Sensors
Print ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 23
Issue 16
Pages 7293
DOI https://doi.org/10.3390/s23167293
Keywords Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry

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