Dynamic inverse control method and device for MEMS gyroscope

Abstract

The invention discloses a dynamic inverse control method and device for an MEMS gyroscope, which are used for improving the environmental adaptability of a drive control system of the MEMS gyroscope.The method comprises the steps of converting gyroscope kinetic model into a strict feedback form; designing a neural network-based parallel estimation model to approach real dynamics, and designing acomposite adaptive law of neural network weights based on neural network prediction errors and tracking errors to realize effective dynamic estimation of unknown dynamics; designing a disturbance observer to realize effective estimation of external disturbance; and designing a dynamic inverse control system on the basis of a backstepping method to avoid differential explosion of the backstepping method and achieve drive control over the MEMS gyroscope. According to the MEMS gyroscope dynamic inverse control method based on parallel estimation, the problem that dynamic dynamics and external interference are difficult to estimate accurately can be solved, high-precision gyroscope driving control is achieved, and the MEMS gyroscope driving control performance is further improved.

Description

technical field [0001] The present application relates to the field of intelligent instrumentation, and more specifically, to a method and device for dynamic inverse control of MEMS gyroscopes. Background technique [0002] At present, in view of the advantages of small size, low power consumption, and low price, MEMS (Micro-Electro-Mechanical System, Micro-Electro-Mechanical System) gyroscopes are widely used in the fields of angular velocity measurement such as robots, consumer electronics, and wearable devices. However, because the MEMS gyroscope circuit signal is very weak, useful signals are easily affected by external environments such as temperature and air pressure, and external interference. Once the useful signal is affected by external interference or changes in the working environment, the gyro drive control performance will deteriorate. Therefore, it is very necessary to use adaptive control to improve the environmental adaptability of the drive control system. ...

AI Technical Summary

Problems solved by technology

However, since the MEMS gyroscope circuit signal is very weak, useful signals are easily affected by external environments such as temperature and air pressure, as well as external interference. Once the useful signal is affected by external interference or changes in the working environment, the gyro drive control performance will deteriorate.

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