Inversing global SMFC (sliding mode fuzzy control) method for micro-gyroscope based on neural network

A technology of micro gyroscope and neural network, applied in the direction of adaptive control, general control system, control/adjustment system, etc., can solve problems such as inconvenience, system instability, and low robustness

Inactive Publication Date: 2015-11-11
HOHAI UNIV CHANGZHOU
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Problems solved by technology

However, the robustness of adaptive control to external disturbances is very low, and it is easy to make the system unstable.
[0004] This shows that above-mentioned existing gyroscope obviously still has inconvenience and defect in use, a

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  • Inversing global SMFC (sliding mode fuzzy control) method for micro-gyroscope based on neural network
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  • Inversing global SMFC (sliding mode fuzzy control) method for micro-gyroscope based on neural network

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[0115] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solution of the present invention more clearly, but not to limit the protection scope of the present invention.

[0116] Inverse global sliding mode fuzzy control method based on neural network for micro gyroscope, such as Figure 4 shown, including the following steps:

[0117] 1. Establish the dimensionless dynamic equation of the micro gyroscope according to Newton's law in the rotating system

[0118] A micro-vibration gyroscope generally consists of three components: a mass supported by an elastic material, an electrostatic drive device, and a sensing device. The main function of the electrostatic driving circuit is to drive and maintain the constant amplitude of the vibration of the micro-vibration gyroscope, and the sensing circuit is used to sense the position and speed of the mass. The micro gyr...

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Abstract

The invention discloses an inversing global SMFC (sliding mode fuzzy control) method for a micro-gyroscope based on a neural network. An inversing global SMFC system based on the neural network consists of an inversing global sliding mode fuzzy controller, a neural network dynamic characteristic estimator, and a fuzzy uncertainty estimator. Global sliding-mode control can iron out a defect that an arrival mode in the conventional sliding-mode control does not have robustness, speeds up system response, and enables a system to have robustness in the whole process of response. The method enables a lyapunov function of the system and the design process of a controller to be systematic and structuralized through inverse design during the inversing control. Fuzzy control is used for approximating a switching function term, and a switching term in sliding-mode control is converted into continuous fuzzy control output, thereby weakening vibration in sliding-mode control, and achieving a stronger capability of adaptive tracking. Therefore, the method improves the transient performance and robustness of a sliding-mode control system, estimates the unknown dynamic characteristics of the micro-gyroscope, and reduces the vibration in the control of a sliding-mode control structure.

Description

technical field [0001] The invention relates to the technical field of micro-gyroscope control, in particular to a neural network-based micro-gyroscope inversion global sliding mode fuzzy control method. Background technique [0002] Micro gyroscopes are fundamental measurement elements in inertial navigation and inertial guidance systems. Because of its huge advantages in size and cost, micro gyroscopes are widely used in aviation, aerospace, automotive, biomedical, military and consumer electronics fields. However, due to the existence of errors in design and manufacturing and temperature disturbances, there will be differences between the characteristics of the original and the design, which will reduce the performance of the micro gyroscope system. The micro gyroscope itself belongs to the multi-input multi-output system and the system parameters are uncertain and susceptible to the influence of the external environment. Compensating manufacturing errors and measuring ...

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Application Information

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IPC IPC(8): G05B13/04
Inventor 储云迪方韵梅费峻涛
Owner HOHAI UNIV CHANGZHOU
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