Double-cylinder hydraulic gate oil cylinder stroke error compensation method based on artificial neural network

Abstract

The invention discloses a double-cylinder hydraulic gate oil cylinder stroke error compensation method based on an artificial neural network. The method includes the following steps that (1) key data of a gate state are defined and include a left-right oil cylinder stroke deviation value delta Hc, a gate left-right opening degree deviation value delta H, a gate water seal extrusion degree D, a gate noise level DB and a gate vibration level V; (2) the key data are collected, and an error grading standard is set; (3) the optimal error range is determined; (4) the mapping relation between the key data and an oil cylinder stroke compensation value h is established; (5) an initial BP artificial neural network model is established, and the oil cylinder stroke compensation value h is obtained; (6) the oil cylinder stroke compensation value h is output to a gate electrical synchronous deviation rectification control system; and (7) artificial neural network training is carried out, and the optimal gate running track is obtained. According to the double-cylinder hydraulic gate oil cylinder stroke error compensation method based on the artificial neural network, the practical running state of a gate is reflected comprehensively and accurately, the problem that the gate state reflected by an oil cylinder stroke detection value is inconsistent with the practical gate state is solved, and the running synchronization precision of the gate is remarkably improved.

Description

technical field [0001] The invention belongs to the technical field of gate synchronous deviation correction control technology in the technical field of gate hoist automatic control, and specifically refers to a double-cylinder hydraulic gate cylinder stroke error compensation method based on artificial neural network. Based on the judgment of the actual state of the gate operation, the mapping relationship between the detection value of the cylinder stroke and the actual state of the gate operation based on the artificial neural network is established, and the error intelligently compensates the detection value of the stroke detection value of the cylinder, and then accurately adjusts the state of the gate to ensure that the gate operates on the best track . Background technique [0002] Large gates are important facilities in water conservancy projects, and play a key role in flood control, drought resistance, water supply and other applications. Large gate hoists genera...

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Problems solved by technology

[0006] In order to solve the problem that the gate state reflected by the stroke detection value of the double-cylinder hydraulic gate cylinder is inconsistent with the actual gate state existing in the current project, the present invention provides a compensation method for the stroke error of the double-cylinder hydraulic gate cylinder based on artificial neural network

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