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

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 ...

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

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