Adaptive neural network controller of bridge crane and design method thereof

Abstract

The invention discloses an adaptive neural network controller of a bridge crane. A dynamic model of a bridge crane is established; linear processing is carried out on the dynamic model of the bridge crane; an external disturbance factor compensation item d is introduced to obtain a linear model of the bridge crane; and an adaptive neural network controller including a trolley controller and a loadcontroller is obtained based on a RBF neural network design. According to the invention, on the basis of the neural network adaptive method, the double-feedback adaptive controller is designed for trolley positioning and load oscillation prevention of the crane; and on the basis of the self-learning method, random approximation of non-linear parts like the model error and the external interference during the crane modeling process is realized, so that stability control is realized.

Description

technical field [0001] The invention relates to a neural network adaptive controller of an overhead crane and a design method thereof. Background technique [0002] With the rapid development of my country's transportation industry and intelligent manufacturing industry, bridge cranes (also known as bridge cranes) have become indispensable key equipment in modern industrial automation production. The characteristics of high efficiency and simple structure make the application range of overhead cranes more and more extensive. At present, in order to meet the needs of global trade integration, overhead cranes are developing towards faster and faster running speeds and higher and higher lifting heights, and are widely used in various sectors of national economic development such as docks and construction sites and field. [0003] The control difficulties of bridge cranes mainly lie in the operation positioning control of the trolley and the swing suppression control of the loa...

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Problems solved by technology

[0003] The control difficulties of bridge cranes mainly lie in the operation positioning control of the trolley and the swing suppression control of the load. Most of the existing technologies are located in the anti-swing control research of the load, and the methods used have many deficiencies, such as the input setting method to eliminate the swing. The swing angle changes greatly; the closed-loop PID control method has poor anti-interference ability to the outside world; the fuzzy adaptive method has static error problems and has a large amount of calculation and slow response speed; most of the other general methods have a variety of constraints. Insufficient practicability; some technologies use the neural network method, but only limited to the load anti-swing control of the crane

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