MEMS gyroscope robust self-adaptation control method based on neural network upper bound learning

Abstract

The invention discloses an MEMS gyroscope robust self-adaptation control method based on neural network upper bound learning. The method includes the following steps that an ideal kinetic model and an MEMS gyroscope kinetic model are established, a sliding mode function is designed, a control law is obtained based on the sliding mode function, and an RBF neural network upper bound estimated value is used as a gain of a robust item on the basis of the control law together with a feedback item and the robust item; a parameter self-adaptation law and a network weight self-adaptation law are designed based on a Lyapunov method. According to the MEMS gyroscope robust self-adaptation control method based on neural network upper bound learning, the feedback item is added in the control law, the two-shaft vibration trajectory tracking speed and the parameter estimation speed of an MEMS gyroscope are greatly increased, and the vibration amplitude is decreased; the robust item based on RBF neural network upper bound learning is added in the control law, the buffeting problem caused by large external disturbance and fluctuation and the problem that the dynamic characteristics are changed worse are solved, the uncertainty of a structural formula and the uncertainty of a non-structured formula are eliminated, and therefore the robustness of the system is further improved.

Description

technical field [0001] The invention relates to a micro-gyroscope robust self-adaptive control method based on neural network upper bound learning, and belongs to the technical field of micro-gyroscope control. Background technique [0002] Micromachined gyroscope (MEMS Gyroscope) is an inertial sensor processed by microelectronics technology and micromachining technology to sense angular velocity. It detects angular velocity through a vibrating micromechanical component made of silicon, so the micromechanical gyroscope is very easy to miniaturize and mass-produce, and has the characteristics of low cost and small size. In recent years, micromachined gyroscopes have been paid close attention to in many applications, for example, gyroscopes combined with micromachined acceleration sensors for inertial navigation, image stabilization in digital cameras, wireless inertial mice for computers, and so on. However, due to the inevitable processing errors in the manufacturing proce...

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Problems solved by technology

[0006] The purpose of the present invention is to overcome the defects of the existing micro gyroscope control method, especially to improve the micro gyroscope system in the presence of model uncertainty, parameter perturbation, and chattering caused by large external disturbances and fluctuations. In the case of interference, the tracking performance of the ideal trajectory and the robustness of the entire system provide a robust adaptive control method for micro-gyroscopes based on neural network upper bound learning

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