Improved VMD-ELM Algorithm for MEMS Gyroscope of Temperature Compensation Model Based on CNN-LSTM and PSO-SVM

• Published in: Micromachines (Basel) Vol. 13; no. 12; p. 2056
• Main Authors: Wang, Xinwang, Cao, Huiliang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.11.2022; MDPI
• Subjects: Accuracy; Algorithms; Angular velocity; Applications programs; Artificial neural networks; Bias; Calibration; Consumer electronics; convolutional neural networks—long short-term memory (CNN-LSTM); Deep learning; Design; Genetic algorithms; Gyroscopes; Inertia; Machine learning; Mathematical optimization; MEMS gyroscope; Microelectromechanical systems; Mobile computing; Navigation systems; Neural networks; Noise reduction; Particle swarm optimization; particle swarm optimization—support vector machines (PSO-SVM); Random walk; Sensors; Support vector machines; Temperature compensation; Temperature effects; variational modal decomposition (VMD); Velocity; Wavelet transforms; United Kingdom; MEMS gyroscope; convolutional neural networks—long short-term memory (CNN-LSTM); temperature compensation; variational modal decomposition (VMD); particle swarm optimization—support vector machines (PSO-SVM)
• ISSN: 2072-666X; 2072-666X
• DOI: 10.3390/mi13122056

The micro-electro-mechanical system (MEMS) gyroscope is a micro-mechanical gyroscope with low cost, small volume, and good reliability. The working principle of the MEMS gyroscope, which is achieved through Coriolis, is different from traditional gyroscopes. The MEMS gyroscope has been widely used in the fields of micro-inertia navigation systems, military, automotive, consumer electronics, mobile applications, robots, industrial, medical, and other fields in micro-inertia navigation systems because of its advantages of small volume, good performance, and low price. The material characteristics of the MEMS gyroscope is very significant for its data output, and the temperature determines its accuracy and limits its further application. In order to eliminate the effect of temperature, the MEMS gyroscope needs to be compensated to improve its accuracy. This study proposed an improved variational modal decomposition—extreme learning machine (VMD-ELM) algorithm based on convolutional neural networks—long short-term memory (CNN-LSTM) and particle swarm optimization—support vector machines (PSO-SVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and the gyro output signal with better accuracy is obtained. The VMD algorithm separates the gyro output signal and divides the gyro output signal into low-frequency signals, mid-frequency signals, and high-frequency signals according to the different signal frequencies. Once again, the PSO-SVM model is constructed by the mid-frequency temperature signal to find the temperature error. Finally, the signal is reconstructed through the ELM neural network algorithm, and then, the gyro output signal after noise is obtained. Experimental results show that, by using the improved method, the output of the MEMS gyroscope ranging from −40 to 60 °C reduced, and the temperature drift dramatically declined. For example, the factor of quantization noise (Q) reduced from 1.2419 × 10−4 to 1.0533 × 10−6, the factor of bias instability (B) reduced from 0.0087 to 1.8772 × 10−4, and the factor of random walk of angular velocity (N) reduced from 2.0978 × 10−5 to 1.4985 × 10−6. Furthermore, the output of the MEMS gyroscope ranging from 60 to −40 °C reduced. The factor of Q reduced from 2.9808 × 10−4 to 2.4430 × 10−6, the factor of B reduced from 0.0145 to 7.2426 × 10−4, and the factor of N reduced from 4.5072 × 10−5 to 1.0523 × 10−5. The improved algorithm can be adopted to denoise the output signal of the MEMS gyroscope to improve its accuracy.