Genetic algorithm optimized multi-model predication control method

A technology of predictive control and genetic algorithm, which is applied in the field of automation, can solve the problems of reduced accuracy of system model sets, unclear dominant working areas, and different degrees of system influence, so as to reduce influence, improve accuracy and control performance.

Active Publication Date: 2017-05-31
HANGZHOU DIANZI UNIV
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Problems solved by technology

In the multi-model predictive control, the global model of the system is decomposed into a series of local models, and the control amount of the system is obtained by weighting each model in the system at different working range points, but in the global working range Due to the difference in the scope of the working interval, different working interval models have different influences on the system, resulting in different degrees of influence on the control amount in the system on the basis of the weight of the system
As a result, the accuracy of the system model set is reduced, and the dominant work area is not obvious, requiring faster calculation and more accurate model set

Method used

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  • Genetic algorithm optimized multi-model predication control method
  • Genetic algorithm optimized multi-model predication control method

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Embodiment Construction

[0067] The present invention will be further described below in conjunction with embodiment

[0068] 1 Establish the multi-model of the electric heating furnace.

[0069] 1.1 According to the working area of ​​the electric heating furnace, it is divided into i equal parts according to the temperature range, and i is the number of working areas to be divided.

[0070] 1.2 Collect the real-time step response data of the electric heating furnace in each working condition interval, and use the data to establish the fractional order transfer function model M of the controlled object i , of the form:

[0071]

[0072] Among them, M i is the i-th sub-model, α 1，i is the differential order of the i-th system, T 1,i is the corresponding coefficient, S is the Laplace transform operator, K m,i is the model proportional gain coefficient, τ m,i is the lag time constant of the model.

[0073] 1.3 The fractional order model can be numerically processed by the Oustaloup approximatio...

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Abstract

The invention discloses a genetic algorithm optimized and weighted multi-model predication function control method. The method comprises the following steps: firstly, expanding an integer-order multi-model predication control method to a fractional-order multi-model predication control method; converting a nonlinear model into a linear partial model through an established partial state space model of a controlled object; introducing a genetic algorithm into a selection of a partial model set and kernel bandwidth of a working section. Therefore, the range of a working region of the partial model of a system is enlarged, the quantity of the models which work at present is reduced, the controlled quantity occupied rate on the system of the main partial model in the system is increased, the control precision of the system is increased and the working efficiency of the device is more efficient.

Description

technical field [0001] The invention belongs to the technical field of automation, and relates to a method for predictive control of genetic algorithm optimization multi-model. Background technique [0002] In industrial control, there are usually links such as nonlinearity, large operating range, and large delay, which directly affect the level of production efficiency and product quality. In order to meet the industrial production efficiency and improve economic benefits, more accurate control effects can be obtained. , thus gaining more researchers' attention. In the multi-model predictive control, the global model of the system is decomposed into a series of local models, and the control amount of the system is obtained by weighting each model in the system at different working range points, but in the global working range In , due to the difference in the scope of the working interval, different working interval models have different influences on the system, resulting...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04G06N3/12
CPCG05B13/048G06N3/12
Inventor 张日东徐卫德
Owner HANGZHOU DIANZI UNIV
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