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Fast self-learning improved ADRC control method for non-linear system

A technology of nonlinear system and control method, applied in the field of fast self-learning improved ADRC control of nonlinear system, which can solve the problems of poor control effect of large time-delay system

Active Publication Date: 2019-01-04
WUHAN UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

The RBF neural network algorithm can be used to control the film thickness, but the control effect is not good for large time delay systems
In short, there are still aspects to be optimized for the biaxially stretched film thickness control system

Method used

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Embodiment Construction

[0108]The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not intended to limit the present invention.

[0109] The fast self-learning improved ADRC control method of the nonlinear system of the present embodiment comprises the steps:

[0110] Create an improved Active Disturbance Rejection Controller (ADRC): Use the additional momentum term and adaptive learning rate method to adjust the nonlinear combination part of the ADRC in real time, find the optimal control parameters, and realize the self-tuning of parameters , and apply the improved Active Disturbance Rejection Controller (ADRC) to the discretization model of the nonlinear system;

[0111] Step 1: Create Active Disturbance Rejection Controller (ADRC): Active Disturbance Rejection Control (ADRC) technology is an improvement o...

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Abstract

The invention discloses a fast self-learning improved ADRC control method for a non-linear system, which comprises the following steps: S1, creating an active disturbance rejection controller (ADRC) which includes a tracking differentiator (TD) part, an extended state observer (ESO) part, a non-linear state error feedback (NLSEF) part and a disturbance compensation part, wherein S1 includes S11, establishing a tracking differentiator (TD) control model, S12, establishing an extended state observer (ESO) control model, S13, establishing a non-linear state error feedback (NLSEF) control model, and S14, establishing a disturbance compensation control model; S2, creating a self-learning active disturbance rejection controller (SADRC): applying a self-learning method to the non-linear state error feedback (NLSEF) control model to establish a self-learning non-linear active disturbance rejection control system model; and S3, creating a fast self-learning active disturbance rejection controller (FSADRC): designing a learning rate oriented adaptive mechanism by using an additional momentum term method and establishing a fast self-learning model based on dynamic adaptive learning rate.

Description

technical field [0001] The invention relates to a control method of a nonlinear system, in particular to a fast self-learning improved ADRC control method of the nonlinear system. Background technique [0002] The uniformity of the thickness of the biaxially stretched film is one of the important criteria for its quality. If the uniformity is not good, there will be a relative deviation in a certain position of the film. If this deviation position remains unchanged, after thousands of layers of winding, the film will have bad defects such as grooves, hoops or ribs, and cause permanent deformation. This makes the measurement and control of film thickness very important, because it directly affects the mechanical properties and performance quality of film products. In the production of biaxially stretched film, there are many factors that affect the thickness of the film, such as raw material quality, extrusion pressure, die temperature, stretching speed, etc. Changes in one ...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/027G05B13/042
Inventor 廖雪超周游陈振寰邓万雄
Owner WUHAN UNIV OF SCI & TECH
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