Control method of neural network full adjustment based on nominal controller

Abstract

The invention discloses a control method of a neural network full adjustment based on a nominal controller. The control method mainly comprises the two steps that a trace tracking controller and a neural network full adjustment compensating controller are designed based on a nominal value model, and the control output ends of the two controllers are combined to be used as the control input end of a micro-gyroscope. According to the control method of the neural network full adjustment based on the nominal controller, the advantage of a model control method is used, meanwhile, the powerful approximation capability of a neural network is used, model errors and an outside disturbance effect are estimated and compensated on line in real time, the tracking performance and the robustness of a system can be greatly improved, the adaptive algorithms of a neural network weight and the center and the sound stage width of a gaussian function are designed based on a Lyapunov stability theory, and the global stability of a closed-loop system and the boundedness of control input can be guaranteed.

Description

technical field [0001] The invention relates to a control method of a micro gyroscope, in particular to a control method based on a neural network full adjustment of a nominal controller. Background technique [0002] Micromachined gyroscope (MEMS Gyroscope) is an inertial sensor processed by microelectronics 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 process and the influence of ambient te...

AI Technical Summary

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.

[0005] This shows that above-mentioned existing gyroscope obviously still has inconvenience and defect in use, and needs to be further improved

In order to solve the problems existing in the use of existing gyroscopes, relevant manufacturers have tried their best to find a solution, but no suitable design has been developed for a long time

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