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A data-driven adaptive adjustment method for system key performance indicators

A technology of key performance indicators and self-adaptive adjustment, applied in the field of tracking control, can solve the problems of poor tracking control performance and high tracking error, and achieve the effects of high versatility, simple process, and good tracking control performance.

Active Publication Date: 2022-04-19
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that a large number of parameters usually need to be adjusted when adjusting key performance self-adaptive indicators in the tracking control process of the current standard industrial control system for continuous stirring heating tanks, resulting in poor tracking control performance and high tracking error, the present invention provides a solution. A data-driven adaptive adjustment method for system key performance indicators

Method used

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  • A data-driven adaptive adjustment method for system key performance indicators
  • A data-driven adaptive adjustment method for system key performance indicators
  • A data-driven adaptive adjustment method for system key performance indicators

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Experimental program
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specific Embodiment approach 1

[0022] Specific implementation mode one: combine figure 1 To describe this embodiment, a data-driven self-adaptive adjustment method for system key performance indicators provided in this embodiment specifically includes the following steps:

[0023] Step 1, set the system input as a random square wave signal to collect data, and carry out the initialization of the method:

[0024] Step A1, set the time window length N, collect the input and output data of the past time window length of the controlled object under the random square wave excitation signal, and construct the input matrix U up to the last time N (k-1) and the output matrix Y at the last moment N (k-1), and calculate the generalized inverse of the input matrix up to the last moment

[0025] Step A2, use the improved partial least squares method to calculate the initial value M(k-1) of the regression model and then obtain the prediction model

[0026] Step A3. Use the expected output of the controlled object...

specific Embodiment approach 2

[0034] Specific implementation mode 2: The difference between this implementation mode and specific implementation mode 1 is that the generalized inverse of the input matrix at the last moment as described in step A1 Specifically:

[0035]

[0036] Among them, N is the length of the time window, k represents the current moment, Indicates the input matrix at the last moment; Indicates the input of the controlled object at time k, Represents an m-dimensional real vector space; Indicates the output matrix at the last moment, Indicates the output of the controlled object at time k, Indicates an n-dimensional real number vector space; the superscript "T" indicates a transpose, and the superscript "+" indicates a generalized inverse.

[0037] Other steps and parameters are the same as those in the first embodiment.

specific Embodiment approach 3

[0038] Embodiment 3: The difference between this embodiment and Embodiment 1 is that the specific calculation process of the prediction model includes:

[0039] Compute the regression model using modified partial least squares:

[0040]

[0041] Among them, M(k) represents the regression model at time k, Represents the generalized inverse of the input matrix at time k; Indicates the output matrix at time k; Indicates the output of the controlled object at time k, Represents an n-dimensional real vector space;

[0042] Given a prediction step size of n p , to compute the predictive model:

[0043]

[0044] Among them, n represents the dimension of the input data of the controlled object, and m represents the dimension of the output data of the controlled object.

[0045] Other steps and parameters are the same as those in Embodiment 1 or 2.

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Abstract

The invention provides a data-driven system key performance index self-adaptive adjustment method, which belongs to the technical field of tracking control. The present invention first sets the system input as a random square wave signal to collect data, and uses the improved partial least square method to initialize according to the collected data and the expected output of the controlled object, and obtains and stores the input of the controlled object at the current moment; The measured data of the controlled object at the current moment and the partial least squares algorithm are updated online in the method of the present invention to obtain the input of the controlled object at the next moment; the previous step is repeatedly executed until the system operation ends. The invention solves the problem that the existing data-driven system key performance index self-adaptive adjustment technology usually needs to adjust a large number of parameters, resulting in poor control performance and high tracking error. The invention can be used for self-adaptive adjustment of key performance indicators of the system.

Description

technical field [0001] The invention relates to an adaptive adjustment method for system key performance indicators, belonging to the technical field of tracking control. Background technique [0002] In recent years, with the increasing level of computer technology and informatization, industrial production processes such as chemical industry, metallurgy, machinery, etc. have higher and higher requirements for key performance indicators (such as product quality), and high-precision tracking control has been recognized by the industry and academic circles. world's attention. The existing predictive control technology generally requires the model of the controlled object to be consistent, but in practical applications, it is difficult to obtain an accurate system model due to problems such as measurement noise and complex system mechanisms. On the other hand, with the improvement of sensor and computer storage technology, there is a large amount of measurement data that is n...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 尹珅高菾佚罗浩
Owner HARBIN INST OF TECH