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A temperature compensation method for piezoresistive pressure sensors based on multi-strategy fusion improved dung beetle optimization algorithm
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DOI：10.1177/00202940261418933.png)
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Abstract
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To enhance the measurement accuracy of piezoresistive pressure sensors across a wide range of pressures and temperatures, this study proposes a temperature compensation model based on a Multi-strategy Fusion Improved Dung Beetle Optimization (MSDBO) algorithm. The model addresses three critical limitations of the traditional Dung Beetle Optimizer (DBO): (1) random population initialization that hinders effective environmental exploration, (2) a linear boundary convergence factor that weakens the global-local balance, and (3) susceptibility to local optima. To overcome these challenges, First, Maximin Latin Hypercube Sampling (MLHS) ensures uniform population distribution, enhancing convergence and compensation accuracy. Second, a nonlinear boundary convergence factor improves global search capability and accelerates convergence. Third, a dual-strategy combining beetle somersault foraging with adaptive Gaussian-Cauchy mutation prevents entrapment in local optima while boosting optimization capacity. The proposed MSDBO algorithm was applied to refine the weights and thresholds in a Back-Propagation Neural Network (BPNN) temperature compensation model. Practical implementation on an STM32F407VET6-based transmitter for 0-50 MPa/-20 degrees C-70 degrees C piezoresistive pressure sensors: temperature compensation reduced zero drift and sensitivity drift coefficients by one and three orders of magnitude, respectively, while improving full-scale accuracy from 6.36% to 0.041%-a two-order-of-magnitude enhancement.
Keywords:
improved dung beetle optimization algorithm
multi-strategy fusion
piezoresistive pressure sensor
temperature compensation
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