Design method of micro-gyroscope double-feedback fuzzy neural network super-twist sliding mode control system

Abstract

The invention discloses a design method for a micro-gyroscope double-feedback fuzzy neural network super-distortion sliding mode control system, a control system reference model, a sliding mode surface, an adaptive law, a double-feedback fuzzy neural network approximator, a super-distortion fuzzy sliding mode controller and micro Gyro system. Aiming at problems such as the unknown model of the actual micro-gyroscope system and the uncertainty of its parameters, the invention proposes an adaptive super-twist sliding mode control method for the micro-gyroscope system based on a double-feedback fuzzy neural network. Compared with the traditional neural network control, the double closed-loop fuzzy neural network designed by the present invention has the advantage of full adjustment, and the initial value of the center vector and base width can be set arbitrarily, and the center vector, base width value and the weight of the neural network will all be As the designed adaptive algorithm adapts to the optimal value according to different inputs, the adaptive algorithm is obtained through the Lyapunov stability theory, and the dynamic double feedback neural network can store more information due to the addition of the signal regression loop. The accuracy of the approximation of the unknown model of the micro-gyroscope system is higher. At the same time, combined with the superiority of the high-order super-warp algorithm, it can effectively suppress the control input chattering of the system, ensure that the system converges within a limited time, and track the reference trajectory quickly and accurately, thereby improving the control system. performance, and the superiority of the algorithm was verified experimentally by using MATLAB.

Description

technical field [0001] The invention relates to a self-adaptive super-distortion sliding mode control method for a micro-gyro system based on a double-feedback fuzzy neural network, and belongs to the technical field of micro-gyroscope control. Background technique [0002] Gyroscopes are the basic measurement elements of inertial navigation and inertial guidance systems. Because of its huge advantages in cost, volume, and structure, micro gyroscopes are widely used in civil and military fields such as navigation, aerospace, aviation, and oil field survey and development, and land vehicle navigation and positioning. Because of the influence of errors and temperature in design and manufacturing, it will lead to differences between the characteristics of the original and the design, resulting in a decrease in the sensitivity and accuracy of the gyroscope system. The main problem of micro-gyroscope control is to compensate for manufacturing errors and measure angular velocity. ...

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Problems solved by technology

[0005] In order to improve the performance of the micro-gyroscope system, improve its robustness, and solve the existing defects of the micro-gyroscope and the shortcomings of traditional control methods, the present invention proposes a design method for the micro-gyroscope double-feedback fuzzy neural network super-distortion sliding mode control system, making full use of Advantages of dual-feedback fuzzy neural network control, adaptive control and super-twist sliding mode control

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