Abstract
Hybrid Temperature Compensation Model of MEMS Gyroscope Based on Genetic Particle Swarm Optimization Variational Modal Decomposition and Improved Backpropagation
Highlights
The variational modal decomposition (VMD) algorithm optimized by genetic particle swarm optimization (GPSO) is used for signal decomposition to obtain a series of intrinsic mode functions (IMFs), and the multiscale permutation entropy (MPE) is used to judge the complexity of the time series and divide the components into noise items, mixed items, and temperature drift items.[27]. The noise items are removed directly because the information they contain is useless, and the noise items are denoised by using a forward linear prediction (FLP) filtering algorithm
We proposed a hybrid parallel processing model based on GPSO-VMD and improved BP to reduce the MEMS gyroscope temperature error
Experimental results showed that the temperature has a strong influence on the output of the gyroscope, and this processing model can effectively extract the features of the gyroscope signal and reduce the noise interference and temperature drift in the output signal
Summary
With the rapid development of MEMS technology, research on inertial devices such as accelerometers and gyroscopes has become a hotspot.[1,2,3,4,5] MEMS gyroscopes are widely used in aviation, aerospace, and other high-precision measurement and control fields due to their low cost, low power consumption, robustness, and excellent performance.[6,7] the. To improve the accuracy and speed of optimization, Ma et al proposed an immune particle swarm optimization (IPSO) algorithm.[24] In this paper, we propose the use of genetic particle swarm optimization (GPSO) to optimize VMD, and the best decomposition effect is achieved through iterative optimization Another key step is to establish an appropriate temperature compensation model. The VMD algorithm optimized by GPSO is used for signal decomposition to obtain a series of intrinsic mode functions (IMFs), and the multiscale permutation entropy (MPE) is used to judge the complexity of the time series and divide the components into noise items, mixed items, and temperature drift items.[27] The noise items are removed directly because the information they contain is useless, and the noise items are denoised by using a forward linear prediction (FLP) filtering algorithm. Experiments show that this method has excellent performance for the signal processing of gyroscopes
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