Nonsingular sliding model control method of default performance of MEMS (Micro-electromechanical Systems) gyroscope based on complex learning

Abstract

The invention relates to a nonsingular sliding model control method of default performance of an MEMS gyroscope based on complex learning; the method considers the MEMS gyrodynamics model in which theparameter perturbation exists, combines a parallel estimation model to build a neural network prediction error, designs a compound adaptive law of the neural network weight, corrects the weight coefficient of the neural network and realizes the effective dynamic estimation of an unknown dynamics; a performance function is introduced to limit the tracking error; the limited tracking error is converted into unlimited conversion error by the error conversion; a sliding model controller and a complex learning law based on the conversion error are designed; thus, the default performance of the MEMS gyroscope is controlled; and, the nonsingular terminal sliding model controller is designed to realize the feedforward compensation of the unknown dynamics and avoid the singular problem of the system. The method provided by the invention solves the problem that the singularity, the overshoot and the tracking error of the MEMS gyroscope system cannot be predesigned, further improves the controlprecision of the MEMS gyroscope, and improves the performance of the gyroscope.

Description

technical field [0001] The invention relates to a modal control method of a MEMS gyroscope, in particular to a non-singular sliding mode control method based on composite learning for preset performance of the MEMS gyroscope, and belongs to the field of intelligent instruments. Background technique [0002] MEMS gyroscope is a small size, low power consumption, low cost, and easy to integrate angular motion measurement sensor, which is widely used in various low-precision consumer electronics and industrial fields. In order to further improve the measurement accuracy of MEMS gyroscopes, "Slidingmode control of MEMS gyroscopes using composite learning" (Rui Zhang, TianyiShao, Wanliang Zhao, Aijun Li, Bin Xu, "Neurocomputing", 2018) proposes a MEMS gyroscope based on a parallel estimation model. Gyroscope compound learning control method. On the one hand, construct the prediction error and tracking error of the neural network, design the compound adaptive law of the weight of...

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Problems solved by technology

[0004] In order to overcome the shortcomings of the existing technology that does not consider the singularity of sliding mode control and the system overshoot and tracking error cannot be pre-designed, the present invention proposes a non-singular sliding mode control method based on composite learning for MEMS gyroscope preset performance

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