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Angular rate error compensation of MEMS based gyroscope using artificial neural network

机译:基于人工神经网络的基于MEMS的陀螺仪的角速率误差补偿

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Micro Electro Mechanical Systems based gyroscopes have been most significant inertial sensors in previous few years because of its low cost and small size. However these gyroscopes have limited usage due to its non idealities like angular rate error, temperature drift, random drift, drift periodic error. In this paper MEMS gyroscope's angular rate error, have been studied and suitable artificial neural networks based modeling and compensation scheme is proposed. Reference angular rates and output data of MG31-300 under different input angular rates were obtained and also analyzed from literature. As the angular rate error has nonlinear behavior and randomcharacteristics, the ANN based model isdeveloped, which takes gyroscope's output voltage with error as the inputand gives output voltage with compensation the effect of angular rate error. The model has been analyzed for number of neurons as well as mean square error. Result shows that with three number of neurons mean square error of 1.72 e-4 can be achieved which is acceptable and suitable for implementation.
机译:基于微电机械系统的陀螺仪在前几年中最重要的惯性传感器,因为其成本低,尺寸小。然而,由于角速率误差,温度漂移,随机漂移,漂移周期误差等非理想,这些陀螺仪具有有限的使用。在本文中,已经研究了陀螺仪的角度速率误差,提出了合适的基于人工神经网络的建模和补偿方案。获得了不同输入角速率下Mg31-300的参考角度和输出数据,并从文献中分析。随着角速率误差具有非线性行为和随机特征,基于ANN的型号是开发的,它采用陀螺仪的输出电压随着输入和提供了角速率误差的效果而导致输出电压。已经分析了该模型的神经元数以及均方误差。结果表明,通过三个神经元的均值,可以实现1.72 E-4的平方误差,这是可接受的,适合实现。

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