Temperature control method of high-temperature and high-pressure jigger based on improved RBF neural network

Abstract

The invention discloses a temperature control method of a high-temperature and high-pressure jigger based on an improved RBF neural network. The method comprises the following steps of: S1, establishing a dye liquor temperature change curve model; S2, calculating a temperature control deviation according to the actual measurement value of the dye liquor temperature and the dye liquor temperature in the dye liquor temperature change curve model corresponding to the sampling moment, and setting a threshold value; and S3, respectively adopting a PD controller, a PID controller and / or a PID controller based on the improved RBF neural network to perform sectional control on the temperature of the dye according to the relation between the temperature control deviation and the set threshold value until the numerical value of the temperature control deviation is zero. According to the method, the characteristic learning method based on the improved RBF neural network and the PID controller are combined to achieve the self-adaptive control and adjustment of the temperature control system of the jigger in a severe application environment, so that the high precision and the high efficiency of the control process are ensured, the control adjustment period is short, and the control efficiency is high.

Description

technical field [0001] The invention relates to the field of automatic control of the jigger process in the textile industry, in particular to a temperature control method for a high-temperature and high-pressure dyeing machine based on an improved RBF neural network. Background technique [0002] The jigger dyeing link is an essential link in the textile production process and is crucial to the quality control and management of textiles. During the jigger dyeing process, ensuring the precise control of the temperature of the dyeing liquor directly affects the jigger quality of the fabric, and is the core work in the design of the jigger control system. With the rapid development of automatic control technology and the continuous deepening of control algorithm research, the control algorithm based on neural network has been more and more widely used in the field of industrial control. Replacing the traditional control algorithm with the control algorithm based on neural netw...

AI Technical Summary

Problems solved by technology

[0007] Aiming at the technical problem that the control precision of the existing control method cannot meet the production demand, the present invention improves the network structure on the basis of the temperature control technology of the dyeing machine based on the BP neural network, and uses the improved radial basis function neural network (Radical Basis Function , RBF) to replace the BP network, and propose a temperature control method for high temperature and high pressure dyeing machines based on the improved RBF neural network

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