A Temperature Error Parallel Processing Model for MEMS Gyroscope based on a Novel Fusion Algorithm

Abstract

To deal with the influence of temperature drift for a Micro-Electro-Mechanical System (MEMS) gyroscope, this paper proposes a new temperature error parallel processing method based on a novel fusion algorithm. Firstly, immune based particle swarm optimization (IPSO) is employed for optimal parameters search for Variational Modal Decomposition (VMD). Then, we can get the optimal decomposition parameters, wherein permutation entropy (PE) is employed as the fitness function of the particles. Then, the improved VMD is performed on the output signal of the gyro to obtain intrinsic mode functions (IMFs). After judging by sample entropy (SE), the IMFs are divided into three categories: noise term, mixed term and feature term, which are processed differently. Filter the mixed term and compensate the feature term at the same time. Finally, reconstruct them and get the result. Compared with other optimization algorithms, IPSO has a stronger global search ability and faster convergence speed. After Back propagation neural network (BP) is enhanced by Adaptive boosting (Adaboost), it becomes a strong learner and a better model, which can approach the real value with higher precision. The experimental result shows that the novel parallel method proposed in this paper can effectively solve the problem of temperature errors.Electronics 2020, 9, 499 2 of 21 circuit design structure to reduce the damping coefficient, which is convenient and economical [15]. Yang et al. adopted a new concept whereby the on-chip temperature compensation of the MEMS gyroscope is realized by the on-chip sensor. In order to make the on-chip temperature of the micro gyroscope well controlled, the newly integrated serpentine micro heater is used to achieve this function [16]. Cui et al. analyzed the working principle of the gyroscope and adopted the vibration compensation of the drive mode, which has a good effect [17]. However, the hardware compensation cost is high and it is too difficult to implement.The second is through software compensation, it includes the removal of noise and temperature compensation. The software compensation method mainly establishes a model for the drift part and the noise part of the MEMS gyroscope to remove noise and compensate drift. For the establishment of the temperature drift model, the reference temperature and the corresponding temperature drift output obtained in advance are used as a set of training set data. The model is established after the learning and training of the algorithm. When the temperature value is input, the model can predict the output value as compensation. There are two main methods. The first one is serial processing, which first uses a filter to perform noise reduction on the signal and then uses a model to eliminate drift. The other one is parallel processing, which refers to extracting and processing noise components and drift components separately. Dominik uses a combination of experiments and numerical values to provide accurate ceramic acceleration at higher temperature [18]. Yang uses a sim...

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