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Method for controlling micro gyro based on radial basis function (RBF) neural network sliding mode

A technology of micro gyroscope and neural network, which is applied in the field of self-adaptive RBF neural network sliding mode control micro gyroscope, which can solve problems such as vibration

Inactive Publication Date: 2012-08-15
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0003] The purpose of the present invention is to overcome the prior art when the sliding mode variable structure is used alone to control the micro gyroscope. The two sides of the die face go back and forth, resulting in the problem of vibration

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  • Method for controlling micro gyro based on radial basis function (RBF) neural network sliding mode
  • Method for controlling micro gyro based on radial basis function (RBF) neural network sliding mode
  • Method for controlling micro gyro based on radial basis function (RBF) neural network sliding mode

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Embodiment Construction

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Such as figure 1 As shown, in the specific embodiment of the present invention, the micro-gyroscope system and the control system are composed of two parts. The control system mainly consists of two parts to form a sliding mode controller and an RBF neural network. r is the motion track of the micro-gyroscope reference, and u is the control controller, that is, the sliding mode controller, that is, y is the actual trajectory of the micro gyroscope. The present invention proposes an adaptive sliding mode control system strategy of RBF neural network for micro gyroscopes. A key property of this scheme is that the switching function is used as the input of the RBF neural network, and the sliding mode controller is used as the output of the RBF network. The learning function of the network can realize the neural sliding mode control of one input and one output, and...

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Abstract

The invention discloses a method for controlling a micro gyro based on a radial basis function (RBF) neural network sliding mode. Single-input single-output neural sliding mode control can be realized by using a switching function as the input of an RBF neural network, using a sliding mode controller as the output of the RBF network and using the learning function of the neural network; and a control effect can be achieved by integrating the advantages of sliding change structure control, an adaptive algorithm and the RBF neural network. The adaptive algorithm is used for adjusting the link weight of the RBF neural network in real time on line according to accessible conditions, so that a system finally achieves a sliding mode surface, completes tracking, and can adapt to sliding mode control strategies and timely correct and estimate all rigid errors, damping and the like; and the stability of a provided adaptive sliding mode controller exists according to the Lyapunov stability theorem, the system has good robustness, and digital simulation of the three-dimensional micro gyro proves that the method for controlling the micro gyro is valid.

Description

technical field [0001] The invention relates to the technical field of control systems, in particular to an adaptive method for controlling a micro-gyroscope based on an RBF neural network sliding mode. Background technique [0002] Gyroscopes are the most commonly used sensors for measuring angular velocity in many applications, such as navigation, guidance, and control stability. Gyroscopes are devices that use the Coriolis force (ie, the deflection force of the Earth's rotation) to transfer energy from one axis to another. The conventional mode of operation lacks the sense mode that drives the gyroscope from a mode to a known pendulum motion, and the detected Coriolis acceleration is coupled to vibrations that are perpendicular to the drive mode. The response of the vibration sensing mode provides information about the practical angular velocity. Structural imperfections are usually the result of cross-sectional stiffness exponents and orthogonal damping effects, and th...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 费峻涛丁红菲
Owner HOHAI UNIV CHANGZHOU
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