Optimal Control System Method Based on Exemplary System

An optimization control and control system technology, applied in the direction of total factory control, total factory control, electrical program control, etc., can solve problems such as inability to ensure optimization results, complex identification algorithms, and difficulty in popularization, etc., to achieve easy mastery and popularization, The steps and algorithms are intuitive and simple, and the implementation conditions are easy to meet

Active Publication Date: 2017-09-22
INST OF AUTOMATION CHINESE ACAD OF SCI
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

[0002] The control system optimization methods commonly used in the current industrial process control are basically divided into three categories. One is the method of setting the PID control parameters for specific objects one by one by the operator based on experience. The disadvantage is that the optimization results cannot be guaranteed due to the randomness of human experience.
The other type is to first identify the model parameters of specific objects, and then use the optimization process of a certain target to determine the adjustment control parameters. The identification algorithm is generally more complicated, and the optimization process of repeated search, iteration, and approximation is required for specific objects one by one. , making it time-consuming and labor-intensive in implementation and maintenance, and there are limitations that are not easy to popularize
There is also a variety of algorithms that use online self-adaptive adjustment of controller parameters (there are also disadvantages such as complex algorithms, only applicable to certain objects, and even poor robustness, requiring senior technicians to implement, and high application costs)

Method used

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  • Optimal Control System Method Based on Exemplary System
  • Optimal Control System Method Based on Exemplary System
  • Optimal Control System Method Based on Exemplary System

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Embodiment approach

[0035] According to an embodiment of the present invention, the control system includes a filter Gf, a controller Gc, and a controlled object Gp, wherein the mathematical model specifying the controlled object Gp is:

[0036]

[0037] (K 2 ≥0;K 1 >0;K 0 ≥0;τ>0)

[0038] This model basically covers the common first-order (K2=0), second-order, integral (K0=0), non-integral and time-delay (τ>0) objects that account for the majority of industrial processes, where , S is the Laplace operator, K 0 is the integral factor, K 1 is the scaling factor, K 2 Be differential factor, τ represents time lag value; The mathematical model of specifying described controller Gc is:

[0039]

[0040]

[0041] Ti=K 1 / K 0

[0042] Td=K 2 / K 1

[0043]

[0044] In the formula, Kp, Ti, Td and Tf are controller parameters, Kp is proportional coefficient, Ti is integral coefficient, Td is differential coefficient, Tf is filter coefficient;

[0045] Specify the mathematical model...

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Abstract

The invention discloses an optimization control system method based on an exemplary system, which obtains a pair of optimization parameters of such objects by optimizing a selected target; The controller parameters and filter parameters of the actual system are conveniently calculated to realize optimal control; and the tuning method of online control parameters is designed based on the aforementioned template to maintain the long-term optimal control effect.

Description

technical field [0001] The invention belongs to the field of automatic control, and in particular relates to an optimal design of an industrial process control system, a parameter identification of a controlled object, and a method for realizing a complete set of an optimal control system for online setting of controller parameters. Background technique [0002] The control system optimization methods commonly used in the current industrial process control are basically divided into three categories. One is the method of setting the PID control parameters for specific objects one by one by the operator based on experience. The disadvantage is that the optimization results cannot be guaranteed due to the randomness of human experience. . The other type is to first identify the model parameters of specific objects, and then use the optimization process of a certain target to determine the adjustment control parameters. The identification algorithm is generally more complicated...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B19/418
CPCY02P90/02
Inventor 邱育东张文生
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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