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Compound Learning Control Method for Mems Gyroscope Based on Parallel Estimation Model

A technology for estimating models and learning control, applied in adaptive control, general control systems, control/regulation systems, etc., can solve problems such as poor practicability

Active Publication Date: 2019-08-13
NORTHWESTERN POLYTECHNICAL UNIV +2
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Problems solved by technology

[0005] In order to overcome the shortcomings of poor practicability of existing MEMS gyroscope modal control methods, the present invention provides a MEMS gyroscope compound learning control method based on parallel estimation model

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  • Compound Learning Control Method for Mems Gyroscope Based on Parallel Estimation Model
  • Compound Learning Control Method for Mems Gyroscope Based on Parallel Estimation Model
  • Compound Learning Control Method for Mems Gyroscope Based on Parallel Estimation Model

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Embodiment Construction

[0061] refer to figure 1 . The specific steps of the MEMS gyroscope compound learning control method based on the parallel estimation model of the present invention are as follows:

[0062] (a) The dynamic model of the MEMS gyroscope considering the quadrature error is:

[0063]

[0064] Among them, m is the mass of proof mass; Ω z Input the angular velocity for the gyro; is the electrostatic driving force; x * are the acceleration, velocity and displacement of the MEMS gyroscope proof mass along the drive axis; the y * are the acceleration, velocity and displacement of the proof mass along the detection axis; d xx , d yy is the damping coefficient; k xx , k yy is the stiffness coefficient; d xy is the damping coupling coefficient, k xy is the stiffness coupling coefficient.

[0065] In order to improve the accuracy of mechanism analysis, the MEMS gyroscope dynamic model is dimensionless. Take the dimensionless time t * = ω o t, and then divide both sid...

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Abstract

The invention discloses an MEMS (micro-electromechanical system) gyroscope compound learning control method based on a parallel estimation model, and aims to solve the technical problem of poor practicability of an existing MEMS gyroscope mode control method. The technical scheme includes that neural network prediction errors are built according to the parallel estimation model and a dynamics model, compound adaptive laws of neural network weights are designed according to tracking errors, weight coefficients of a neural network are corrected, effective dynamic estimation of unknown dynamics is achieved, a system is indeterminate for parameters and insensitive to external interference in a sliding mode, and feed forward compensation of the unknown dynamics is achieved by the aid of a sliding mode controller. According to the method, the compound adaptive laws of neural network weights are designed, the weight coefficients of the neural network are corrected, effective dynamic estimation of the unknown dynamics is achieved, feed forward compensation of the MEMS gyroscope unknown dynamics is achieved according to sliding mode control theories, control accuracy of an MEMS gyroscope isfurther improved, and the method is good in practicability.

Description

technical field [0001] The invention relates to a MEMS gyroscope mode control method, in particular to a MEMS gyroscope compound learning control method based on a parallel estimation model. Background technique [0002] MEMS gyroscopes are widely used in various angular motion measurement fields due to their small size, low power consumption, low cost, and easy integration with processing circuits. In order to ensure its measurement accuracy, it is required that the proof mass of the MEMS gyroscope must vibrate at a constant amplitude along the driving direction at the natural frequency of the driving shaft. However, due to changes in environmental factors and gyroscope manufacturing defects, conventional PID control cannot achieve high-precision control of MEMS gyroscopes, resulting in serious drift of gyroscopes. [0003] With the development of nonlinear control technology, Park S et al. introduced the nonlinear control theory into MEMS gyroscope control, weakened the b...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
Inventor 许斌张睿张安龙刘瑞鑫吴枫成宇翔邵添羿赵万良谷丛林建华刘洋慕容欣刘美霞应俊
Owner NORTHWESTERN POLYTECHNICAL UNIV
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