Prediction function control method for chemical process genetic algorithm optimization

A technology for predictive function control and chemical process, applied in program control, comprehensive factory control, electrical program control, etc., can solve problems such as model mismatch and shorten running time

Active Publication Date: 2020-05-08
HAINAN NORMAL UNIVERSITY +1
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Problems solved by technology

[0004] Aiming at the above technical problems, the present invention proposes a new type of prediction function control method for chemical process in view of interference, and establishes a new type of prediction for chemical process genetic algorithm optimization by means of given model, model conversion, prediction mechanism, optimization, etc. Function control method, which effectively solves the control problem of model mismatch caused by system disturbance and the switching problem of each stage, effectively improves the tracking performance and anti-interference performance of the batch process, and shortens the time required for each stage of the system. Run time, achieve good control effect and improve production efficiency under model mismatch caused by system disturbance

Method used

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  • Prediction function control method for chemical process genetic algorithm optimization
  • Prediction function control method for chemical process genetic algorithm optimization
  • Prediction function control method for chemical process genetic algorithm optimization

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Embodiment 1

[0140] Taking the injection molding stage and pressure holding stage in the injection molding process as an example, the injection molding process is a typical multi-stage chemical process, and the adjustment method is to control the valve opening of the proportional valve and the control of the pressure holding pressure.

[0141] The present invention is achieved through the following technical solutions:

[0142] A new type of predictive function control method optimized for chemical process genetic algorithm, including the following steps:

[0143] Step 1. For different stages in the chemical process, establish a switching system model based on the state space model of the controlled object, specifically:

[0144] 1.1 Construct a new multi-stage chemical process with disturbance system model:

[0145]

[0146] Among them, k represents the current time, x i (k)∈R n , u i (k)∈R 1 ,y i (k)∈R 1 Respectively represent the state, output and input of the batch process at...

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Abstract

A prediction function control method for chemical process genetic algorithm optimization belongs to the field of advanced control of industrial processes, and comprises the following steps: step 1, establishing a switching system model of a controlled object based on a state space model for different stages in a chemical process; 2, designing a controller of a prediction function of the controlledobject based on genetic algorithm optimization; 3, designing a switching law and carrying out robustness analysis; and 4, for the switching system model in the step 1.2.5, finding out a system stability condition and designing a switching signal. According to the invention, the control problem of model mismatching caused by system interference and the switching problem of each stage are effectively solved, the batch process tracking performance and the anti-interference performance are effectively improved, the operation time of the system in each stage is shortened, a good control effect isstill achieved under model mismatch caused by system interference, and the production efficiency is improved.

Description

technical field [0001] The invention belongs to the field of advanced control of industrial processes, and in particular relates to a predictive function control method optimized for chemical process genetic algorithm. Background technique [0002] In modern industrial production, chemical processes are widely used, especially in the food industry, pharmaceutical industry, chemical industry, etc. The study of its control theory has also made a huge breakthrough. However, it is still a challenge in the high-precision control of modern industrial processing. The main reason lies in its high-quality production level requirements, as well as complex and changeable process conditions. Therefore, the internal interference of the system increases accordingly. When the system is disturbed, the model will not match, making the system unable to run stably. Improving control performance in the presence of model mismatch remains an important issue. An iterative learning control (IL...

Claims

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Application Information

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IPC IPC(8): G05B19/418
CPCG05B19/41865G05B2219/32252Y02P90/02
Inventor 王立敏张日东罗卫平陈丽娟王心如
Owner HAINAN NORMAL UNIVERSITY
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