A temperature compensation method for piezoresistive pressure sensors based on multi-strategy fusion improved dung beetle optimization algorithm

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°C−70°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.