Unmanned driving anti-swing positioning method and system based on self-adaptive neural fuzzy control

A neuro-fuzzy, positioning method technology, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problems of difficult industrial field application, complex relationship, etc., achieve simple algorithm, simplify neural network, and solve the problem of oscillation. Effect

Active Publication Date: 2020-08-14
SOUTHEAST UNIV +2
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Problems solved by technology

However, after the learning is completed, the relationship derived from the input and output data is too complex to be applied in the industrial field

Method used

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  • Unmanned driving anti-swing positioning method and system based on self-adaptive neural fuzzy control
  • Unmanned driving anti-swing positioning method and system based on self-adaptive neural fuzzy control
  • Unmanned driving anti-swing positioning method and system based on self-adaptive neural fuzzy control

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Embodiment Construction

[0025] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0026] refer to figure 1 , the self-adaptive neuro-fuzzy control based anti-sway positioning method for unmanned vehicles proposed in this embodiment is based on a multi-layer forward neural network and a first-order Sugeno fuzzy model, a fuzzy reasoning system is established through adaptive modeling, and the neural network is used to The technology realizes fast anti-shake and precise positioning of unmanned vehicles by learning a large amount of known data. In order to cooperate with signal acquisition and realize the control of the trolley, in the embodiment, SATEC STAD-2000 angle measuring instrument is used to measure the angle of loading, SICK DL-100 laser rangefinder is used to measure the position of the trolley, and SCIYON NT6000V3A controller is used to run the control program , SCIYON KD-200 inverter is u...

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Abstract

The invention provides an unmanned driving anti-swing positioning method and system based on self-adaptive neural fuzzy control. The method is based on a multi-layer forward neural network and a first-order Sugeno fuzzy model, a fuzzy inference system is established through adaptive modeling, and the neural network technology is used for learning a large number of known data to achieve rapid anti-swing and accurate positioning of unmanned driving. The fuzzy inference system is established by utilizing the self-learning capability of the neural network, the membership function is adjusted and the fuzzy rule is automatically generated according to a data set generated by the optimal control algorithm, and problems of randomness of determination of the membership function of the fuzzy systemand difficulty in extraction of the fuzzy rule are solved. Under the condition that the rope length changes within a small range, the unmanned driving anti-swing positioning method and system based onself-adaptive neural fuzzy control have the advantages of being good in robustness, simple in algorithm, high in anti-swing positioning precision and the like.

Description

technical field [0001] The invention belongs to the field of unmanned vehicle anti-sway positioning control, and in particular relates to an unmanned vehicle anti-sway positioning method and system based on adaptive neuro-fuzzy control. Background technique [0002] The load of the bridge crane will inevitably sway during the hoisting process. The research on eliminating the sway of the load when the bridge crane is hoisting and realizing accurate positioning is of great importance for improving the operation efficiency, handling accuracy and industrial control automation of industrial sites. significance. At present, the traditional anti-sway technology can no longer meet the daily industrial needs due to factors such as expensive mechanical equipment, unstable manual operation, and poor positioning effect. Therefore, unmanned vehicles equipped with anti-sway positioning systems have been promoted in industrial sites. Algorithms such as input shaping, PID control and slidi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/02G05B13/04B66C13/18
CPCG05B13/0285G05B13/029G05B13/0295G05B13/042B66C13/18
Inventor 牛丹陈有成李奇陈夕松李世华刘进波
Owner SOUTHEAST UNIV
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