Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization

Abstract

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/normals, 0.00412°/normals, and 0.00328°/normals to 0.00065°/normals, 0.00072°/normals and 0.00061°/normals, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.

Parameter Optimization on FNN/PID Compound Controller for a Three-Axis Inertially Stabilized Platform for Aerial Remote Sensing Applications

“…In the running process of the CPSO algorithm, the control parameters should be dynamically reduced or increased on the basis of the convergence of the population, which can reduce the structural damage to the population and help population escape from local optimizations. In engineering applications and academic studies of CPSO, the value range of the chaotic control parameter μ is usually from 0 to 4 [40]. The chaotic motion is very sensitive to the initial value selection, and the different initial values will be different.…”

A MEMS Gyroscope Noise Suppressing Method Using Neural Architecture Search Neural Network

“…Firstly, inspired by the time sequence processing in data science community, Allan variance (AV) was employed to analyze the MEMS IMU error components, and then ARMA models are employed for modeling and representing the noise [22][23][24][25][26][27][28][29][30][31]. After this, some machine learning methods are also employed in this application, for instance, neural networks and support-vector machine (SVM).…”

“…After this, some machine learning methods are also employed in this application, for instance, neural networks and support-vector machine (SVM). With the rapid development of the semiconductor technology and computing capacity, recently, deep learning (DL) gained a boom in data science community [22][23][24][25][26][27][28][29][30][31]. Artificial intelligence (AI) methods were employed in sequence data processing and obtained great advances while compared with the conventional machine learning methods [22][23][24][25][26][27][28][29][30][31].…”

“…In this paper, inspired by the excellent capability of deep neural networks in sequence data processing, the advanced Neural Architecture Search Recurrent Neural Network (NAS-RNN) was employed and tested for reducing the MEMS gyroscope noise (only the gyroscope was tested using the proposed method, and the accelerometers processing was similar to that of gyroscope). A commercial MEMS IMU STIM300 was employed in the experiments [22][23][24][25][26][27][28][29][30][31]. e sampling frequency was 125 Hz, and the testing time was approximately 800 seconds.…”

Bidirectional Carouseling Method Based on Phase Difference for MEMS Gyro North Finder

“…(2) Owing to its small size, low cost, low power, and environment adaptability, (3,4) a MEMS gyro north finder has become a research focus in the north finder field. However, limited by its mechanical precision, a MEMS gyro is one or two orders of magnitude worse than a high-precision gyro; (5) thus, effective methods must be developed to improve its measurement accuracy. In order to realize a MEMS gyro north finder, there are two main traditional methods: a static method (maytagging) and a dynamic method (carouseling).…”