Research on performance degradation of force sensors based on improved error back propagation algorithm

Studying the performance degradation of force sensors, a core component of aircraft control stick force measurement devices, is essential. The key to investigating equipment performance degradation lies in constructing a degradation model. When dealing with degradation data from specific devices, re...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 2849; no. 1; pp. 12025 - 12030
Main Authors: Wang, Pengling, Wang, Peng, Wang, Chu, Wang, Bin, Chen, Chen, Li, Liangliang
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.09.2024
Subjects:
Aircraft control
Aircraft performance
Back propagation networks
Control equipment
Control sticks
Error analysis
Error reduction
Force measurement
Genetic algorithms
Measuring instruments
Neural networks
Performance degradation
Sensors
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:Studying the performance degradation of force sensors, a core component of aircraft control stick force measurement devices, is essential. The key to investigating equipment performance degradation lies in constructing a degradation model. When dealing with degradation data from specific devices, relying solely on fitting methods may not effectively describe the degradation of the equipment. This study introduces an error backpropagation neural network model for constructing the performance degradation model of force sensors, and optimization improvements are made by using a genetic algorithm. Experimental results demonstrate a 99% reduction in Root Mean Square Error with the proposed modeling approach.
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2849/1/012025