Micro-gyroscope fractional order self-adaptive fuzzy neural inversion terminal sliding mode control method

Abstract

The invention discloses a micro-gyroscope fractional order self-adaptive fuzzy neural inversion terminal sliding mode control method. The method includes the steps of establishing a dimensionless kinetic equation mathematical model and a reference trajectory model of a micro-gyroscope system, and establishing a fractional-order-based inversion terminal sliding mode controller. By means of the method, a micro-gyroscope can track a target in real time, so that errors are converged to zero within limited time; the robustness of the system is enhanced, and good performance is still kept under thecondition that external interference exists; a fractional order adaptive law is designed according to a fractional order terminal sliding mode surface, a self-adaptive identification method is designed on the basis of a Lyapunov stability criterion, and various unknown system parameters of the micro-gyroscope are estimated online in real time; compared with an integer order, adjustable items are added, so that the control effect and the parameter estimation effect are improved.

Description

technical field [0001] The invention relates to the technical field of micro-gyroscope control, in particular to a fractional-order self-adaptive fuzzy neural inversion terminal sliding mode control method for a micro-gyroscope. Background technique [0002] Micro-gyro is a sensor for measuring the angular velocity of inertial navigation and inertial guidance systems. Because it can navigate autonomously in any environment, it has attracted widespread attention since its appearance, and has been widely used in aerospace, navigation, aviation and military fields. application. However, there are errors in the production and manufacturing process and are susceptible to temperature effects, resulting in differences between the characteristics of the components and the design, resulting in a decrease in the performance of the microgyroscope. In addition, the micro gyroscope is a multi-input multi-output system and the system parameters are uncertain and easily affected by the ex...

AI Technical Summary

Problems solved by technology

However, there are errors in the production and manufacturing process and are susceptible to temperature, resulting in differences between the characteristics of the components and the design, resulting in a decrease in the performance of the micro gyroscope

In addition, the micro-gyroscope is a multi-input and multi-output system, and the system parameters are uncertain and easily affected by the external environment, which makes the gyro tracking effect unsatisfactory.

In the traditional sliding mode control method, the design of the sliding mode surface is a linear combination of error ratio, differential or integral, where the order of differential or integral is an integer, the tracking effect of the micro-gyroscope is relatively poor, and the system parameters and angular velocity estimation The effect is also relatively poor, and it is easy to cause chattering

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