Iterative learning optimization-based model prediction control method for loose damping machine

A technology of model predictive control and iterative learning, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as poor stability, reduce adjustment and operation, realize rapid movement, reduce labor intensity and Determining the effect of factors influencing

Pending Publication Date: 2022-04-12
首域科技(杭州)有限公司
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  • Application Information

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Problems solved by technology

[0006] The invention provides a model predictive control method based on iterative learning optimization for a loose moisture regainer, which solves the problem that the automatic control of the loose moisture conditioner only relies on feedforward information when collecting information. The problem of intervening and correcting the export moisture line chart

Method used

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  • Iterative learning optimization-based model prediction control method for loose damping machine
  • Iterative learning optimization-based model prediction control method for loose damping machine
  • Iterative learning optimization-based model prediction control method for loose damping machine

Examples

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no. 1 example

[0052] Please refer to figure 1 ,in, figure 1It is a structural schematic diagram of the first embodiment of a model predictive control method based on iterative learning optimization for a loose moisture regainer provided by the present invention. A model predictive control method based on iterative learning optimization for a loose moisture regainer, comprising the following steps:

[0053] S1: Standardize the original data and align it with delay;

[0054] S2: Select the main parameters that are highly correlated with the outlet moisture;

[0055] S3: Mechanism model and model parameters are obtained through regression calculation;

[0056] S4: Construct different types of tobacco leaf simulation models iterative learning cycle;

[0057] S5: Feedback control based on simulation model;

[0058] S6: The difference between the model prediction control signal and the theoretical water addition amount of the system is obtained, and the correction value of the water addition...

no. 2 example

[0087] Please refer to figure 2 , image 3 , Figure 4 , Figure 5 and Figure 6 , based on the model predictive control method based on iterative learning optimization for loose moisture regainers provided in the first embodiment of the present application, the second embodiment of the present application proposes another model predictive control method based on iterative learning optimization for loose moisture regainers. The second embodiment is only a preferred mode of the first embodiment, and the implementation of the second embodiment will not affect the independent implementation of the first embodiment.

[0088] Specifically, the second embodiment of the present application provides a model predictive control method based on iterative learning optimization for a loose moisture regain machine. Loose dampening machine, comprising: device main body 1;

[0089] A smoke outlet pipe 6, the number of the smoke outlet pipes 6 is two, and the two smoke outlet pipes 6 are...

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Abstract

The invention provides an iterative learning optimization-based model prediction control method for a loose damping machine. The method comprises the following steps of performing standardization processing and delay alignment on original data; selecting main parameters which are highly related to outlet moisture; performing regression calculation to obtain a mechanism model and model parameters; constructing a simulation model iterative learning loop body of different types of tobacco leaves; feedback control based on the simulation model; the model predicts the difference between the control signal and the theoretical water adding amount of the system to obtain a water adding amount correction value and the theoretical water adding amount which jointly act on a PID control signal of a water adding valve, and then the water content of a tobacco flake outlet is controlled. According to the model prediction control method for the loosening and moisture regaining machine based on iterative learning optimization, after the loosening and moisture regaining machine is put into operation, the outlet moisture content CPK after control optimization in the loosening and moisture regaining process is obviously improved compared with a traditional adjusting mode, the production cost is reduced, the qualified rate of tobacco leaves is improved, and the labor intensity and the influence of uncertain factors are effectively reduced.

Description

technical field [0001] The invention relates to the field of tobacco processing, in particular to a model predictive control method based on iterative learning optimization for a loose moistening machine. Background technique [0002] Tobacco is a plant of the genus Nicotiana in the family Solanaceae, an annual or limited perennial herb, with glandular hairs all over the body and strong roots. [0003] Nowadays, the loose moisture conditioner needs to be used for auxiliary operation during the tobacco processing, while the traditional loose moisture conditioner control generally uses feed-forward information such as the incoming moisture, the material flow rate before loosening and moisture control, and the outlet moisture control target to control the moisture content of the exported tobacco leaves. This kind of automatic control based on feed-forward information alone has poor stability in practical applications, and requires operators to continuously intervene and correct...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 邓红伟黄金方世杰
Owner 首域科技(杭州)有限公司
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