Multi-model prediction control method based on gap metric weighting function

A weighting function and predictive control technology, which is applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve the problem of increasing the number, etc., and achieve the effect of improving control performance, reducing the amount of online calculation, and setting convenient and fast

Inactive Publication Date: 2018-12-28
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0004] Although traditional weighting functions, such as trapezoidal weighting functions and Gaussian weighting functions, are simple in form and can be calculated offline, the number of tuning parameters increases with the number of sub-models, which brings great challenges to parameter tuning

Method used

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  • Multi-model prediction control method based on gap metric weighting function
  • Multi-model prediction control method based on gap metric weighting function
  • Multi-model prediction control method based on gap metric weighting function

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Embodiment 1

[0034] In Example 1, a continuous stirred reactor system (CSTR) is simulated and analyzed.

[0035] CSTR system:

[0036]

[0037] where x 1 and x 2 is the dimensionless reaction rate and the temperature in the reactor, and the input variable u is the dimensionless coolant temperature. The constant D in the equation a =0.072, γ=20, B=8 and β=0.3. This system is highly nonlinear, and a single linear controller cannot meet the requirements.

[0038] Utilize the multi-model predictive control method based on the gap metric weighting function provided by the present invention to control the CSTR system, the steps are as follows:

[0039] S1. The change of the output y of the CSTR system directly reflects the degree of nonlinearity of the system, so y is selected as the scheduling variable; the operating space of the system is {y|y∈[0,6]}.

[0040] S2. Choose 3 linear models to approximate the CSTR system, respectively

[0041] S3. Based on submodel P 1 , P 2 , P 3...

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Abstract

The invention discloses a multi-model prediction control method based on a gap metric weighting function. A systematic and effective multi-model weighting algorithm is provided through gap metric; submodel prediction controllers are synthesized to form a multi-model prediction controller; and optimization control is performed on a nonlinear system. Compared with the traditional weighting function,such as a trapezoidal weighting function, the weighting function only has a single setting parameter; the workload of parameter setting is greatly reduced; a weight number can be calculated off lineand is stored in a query table for future use; the on-line calculation load is greatly reduced; the algorithm reserves the advantages of the traditional weighting algorithm, overcomes the disadvantages of the traditional weighting algorithm and is an effective multi-model weighting algorithm; and the algorithm has the great benefits for improving performance of the multi-model prediction controller.

Description

technical field [0001] The invention relates to a multi-model predictive control method based on a gap metric weighting function, and belongs to the technical field of nonlinear system multi-model control. Background technique [0002] In recent years, multi-model predictive control methods are increasingly used for the control of nonlinear systems with wide operating spaces. The multi-model predictive control method based on the decomposition-synthesis principle combines the advantages of multi-model methods and predictive control. On the one hand, the multi-model predictive control method converts complex nonlinear control problems into a series of simple linear control problems; on the other hand, the constraints in the control system can be directly included in the objective function. In addition, the closed-loop control performance of the multi-model predictive control is further improved due to the rolling optimization mechanism. [0003] In the multi-model predictiv...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 杜静静陈俊风
Owner HOHAI UNIV CHANGZHOU
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