CN106066608A - The modelling by mechanism of multivariate continuous process model merges discrimination method with Experimental modeling - Google Patents
The modelling by mechanism of multivariate continuous process model merges discrimination method with Experimental modeling Download PDFInfo
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Abstract
The modelling by mechanism that the present invention relates to a kind of multivariate continuous process model merges discrimination method with Experimental modeling, comprise the following steps: 1) under rational assumed condition sets, utilize Analysis on Mechanism modeling method to determine model structure and the parameter being identified multivariable process;2) the some groups of Identification Data being identified multivariable process are gathered;3) carry out modelling by mechanism and merge identification with Experimental modeling.Compared with prior art, the present invention has the advantage such as more accurately and reliably.
Description
Technical field
The present invention relates to the multivariate continuous process identification technique in modeling and simulating field in Control Science and Engineering subject,
The modelling by mechanism especially relating to a kind of multivariate continuous process model merges discrimination method with Experimental modeling.
Background technology
Along with socioeconomic development, modern industrial equipment increasingly maximizes, complicates, and whole production process is by numerous rings
Joint composition, and generally there is coupling and associate between these links, this coupling shows as some input of system
Variable affects multiple output variable simultaneously, or some output variable is affected by multiple input variables, such system
It is referred to as multi-variable system.Extensive all the more for the utilization of multi-variable system, for ensureing security of system and stablizing, carry in every profession and trade
High product quality, the energy consumption etc. of reducing need one the most accurately system model to be controlled the design of system.Therefore, many
The modeling problem of variable system is always control theory and the focus of industrial application research.Being modeled as of system mathematic model
Lose and affect the design of control system all the time, adjust and performance evaluation.
For a long time, mode based on Analysis on Mechanism carries out the modeling of system, to need the principle of modeling to determine accordingly
One group of (partially) differential equation and the continuous time system of some restriction relations composition, have explicit physical meaning and reliability be high
Advantage but have that computing is complicated and nonlinear model such as is difficult to resolve at the problem.So, Analysis on Mechanism modeling is considered as engineering always
Disabled modeling method in application, especially for multivariate complication system.The method of application experiment modeling carries out many
During the modeling of variable complication system, there is highly versatile and the feature pressing close to engineering actual there is also the model structure being difficult to overcome
Uncertain and the insecure problem of identification model.But, under rational assumed condition sets, can effectively simplify and be set up
Analysis on Mechanism model, and it is possible to obtain on difinite quality analysis significance can computation model.The structure of this model and spy
Property parameter value can be exactly that the model structure of multivariate complication system Experimental modeling is uncertain and the unreliable difficulty of identification model
Topic provides solution.
Summary of the invention
Defect that the purpose of the present invention is contemplated to overcome above-mentioned prior art to exist and provide one more accurately and reliably
Modelling by mechanism and the Experimental modeling of multivariate continuous process model merge discrimination method.
The purpose of the present invention can be achieved through the following technical solutions:
The modelling by mechanism of a kind of multivariate continuous process model merges discrimination method with Experimental modeling, comprises the following steps:
1) under rational assumed condition sets, Analysis on Mechanism modeling method is utilized to determine the mould being identified multivariable process
Type structure and parameter;
2) the some groups of Identification Data being identified multivariable process are gathered;
3) carry out modelling by mechanism and merge identification with Experimental modeling.
Described step 1) particularly as follows:
If being expressed as of obtained Analysis on Mechanism model available delivery functional form
In formula, Φ (s) is the transfer function model matrix being identified multivariable process;GijS () is jth input the i-th output
Branch's transfer function model;
Assume the i-th output Y of processiS () is expressed as
In formula, UjS () is the jth input being identified multivariable process;YijS () is YiS () inputs U for jthjThe sound of (s)
Answer component;
Assume GijS () is expressed as with a kind of universal model
In formula, KijIt is GijThe gain of (s);τijIt is GijDelaying of (s);qijIt is GijThe polynomial exponent number of molecule of (s);pij
It is GijThe exponent number of the denominator polynomials of (s);{βijk, k=1,2 ..., qijIt is GijThe polynomial coefficient of molecule of (s);{αijk,k
=1,2 ..., pijIt is GijThe coefficient of the denominator polynomials of (s).
Described step 2) particularly as follows:
Assume that Identification Data length elects N as, collected and be identified the input data table of multivariable process and be shown as { uj(k),
K=1,2 ..., N;J=1,2 ... n}, collected and be identified the output tables of data of multivariable process and be shown as { yi(k), k=1,
2,…,N;I=1,2 ... m}.
Described step 3) particularly as follows:
The gain of Experimental modeling model and exponent number directly take modelling by mechanism parameter, the time class parameter of Experimental modeling model
Calculate with identification optimization and obtain, it may be assumed that
WithIt is calculated by the identification optimization according to Identification Data.
Described step 3) particularly as follows:
The model order of Experimental modeling directly takes modelling by mechanism model parameter, the gain of Experimental modeling model and time class
Parameter calculates with identification optimization and obtains, it may be assumed that
WithCalculated by the identification optimization according to Identification Data
Arrive.
Described step 3) particularly as follows:
The parameter optimization scope of Experimental modeling is according to modelling by mechanism parameter determination, it may be assumed that
WithBy the identification according to Identification Data
Optimization is calculated.
But, the identification of each parameter optimizes codomain all can be centered by modelling by mechanism model parameter, left and right 10% to 50%
For border.And for exponent number class parameter such asOrThe most desirableOr
The modeling problem of multivariate continuous process is always advanced control theory and the bottleneck problem of Technique Popularizing application, uses
Analysis on Mechanism modeling method is set up multivariate continuous process model and is had explicit physical meaning and the high advantage of reliability, but exists
Parameter difficulty to determine and equation complexity intractable problem.Set up multivariate continuous process model by the method for Experimental modeling and have logical
Strong by property and press close to the feature that engineering is actual, but there is also that model structure is uncertain and the insecure problem of model characteristics.But,
Two kinds of methods are merged and sets up multivariate continuous process model, then can learn from other's strong points to offset one's weaknesses, set up more accurately and more reliable
Model.Compared with prior art, the Mathematical Models that the present invention is directed to a kind of general transfer function form proposes three kinds of machines
Reason modeling merges discrimination method with Experimental modeling, and the essence of this fusion discrimination method is to utilize Analysis on Mechanism modeling method to obtain
Being identified model structure and the initial parameter values of multivariable process, re-using experiment modeling method is asked for being identified multivariable process
Concrete model parameter.
Detailed description of the invention
Below in conjunction with the embodiment of the present invention, technical scheme is clearly and completely described, it is clear that described enforcement
Example is a part of embodiment of the present invention rather than whole embodiment.Based on the embodiment in the present invention, ordinary skill
The every other embodiment that personnel are obtained on the premise of not making creative work, all should belong to the model of present invention protection
Enclose.
It is identified process, it is assumed that its realistic model is for what certain known two input one exported:
The model of postulated mechanism modeling gained is very accurate, just the same with realistic model.
Tested by identification, it is thus achieved that 800 point process output response { y1(k), k=1,2 ..., 800}, input data { u1
(k), k=1,2 ..., 800} and input data { u2(k), k=1,2 ..., 800}.
Directly take with the model order that Experimental modeling merges discrimination method, i.e. Experimental modeling according to the second modelling by mechanism
Modelling by mechanism parameter, the recycling available identification model of population (PSO) identification program is:
Directly take according to gain and the exponent number of the first modelling by mechanism with Experimental modeling fusion discrimination method, i.e. Experimental modeling
Using modelling by mechanism parameter, recycling the available identification model of population (PSO) identification program is:
Contrast two kinds of modellings by mechanism and merge knowable to the model parameter that identification obtains with Experimental modeling, modelling by mechanism and experiment
It is highly effective that discrimination method is merged in modeling, particularly merges discrimination method with the first modelling by mechanism with Experimental modeling.From
Above case is it can be seen that merge distinguishing of time class parameter that discrimination method obtains with the first modelling by mechanism and Experimental modeling
Know precision the highest, and the time class parameter obtained with the second modelling by mechanism and Experimental modeling fusion discrimination method exists significantly
Identification Errors.
The above, the only detailed description of the invention of the present invention, but protection scope of the present invention is not limited thereto, and any
Those familiar with the art, in the technical scope that the invention discloses, can readily occur in the amendment of various equivalence or replace
Changing, these amendments or replacement all should be contained within protection scope of the present invention.Therefore, protection scope of the present invention should be with right
The protection domain required is as the criterion.
Claims (6)
1. the modelling by mechanism of a multivariate continuous process model merges discrimination method with Experimental modeling, it is characterised in that include
Following steps:
1) under rational assumed condition sets, Analysis on Mechanism modeling method is utilized to determine the model knot being identified multivariable process
Structure and parameter;
2) the some groups of Identification Data being identified multivariable process are gathered;
3) carry out modelling by mechanism and merge identification with Experimental modeling.
The modelling by mechanism of a kind of multivariate continuous process model the most according to claim 1 merges identification side with Experimental modeling
Method, it is characterised in that described step 1) particularly as follows:
If being expressed as of obtained Analysis on Mechanism model available delivery functional form
In formula, Φ (s) is the transfer function model matrix being identified multivariable process;GijS () is dividing of jth input the i-th output
Prop up transfer function model;
Assume the i-th output Y of processiS () is expressed as
In formula, UjS () is the jth input being identified multivariable process;YijS () is YiS () inputs U for jthjS the response of () divides
Amount;
Assume GijS () is expressed as with a kind of universal model
In formula, KijIt is GijThe gain of (s);τijIt is GijDelaying of (s);qijIt is GijThe polynomial exponent number of molecule of (s);pijIt is Gij
The exponent number of the denominator polynomials of (s);{βijk, k=1,2 ..., qijIt is GijThe polynomial coefficient of molecule of (s);{αijk, k=1,
2,…,pijIt is GijThe coefficient of the denominator polynomials of (s).
The modelling by mechanism of a kind of multivariate continuous process model the most according to claim 2 merges identification side with Experimental modeling
Method, it is characterised in that described step 2) particularly as follows:
Assume that Identification Data length elects N as, collected and be identified the input data table of multivariable process and be shown as { uj(k), k=1,
2,…,N;J=1,2 ... n}, collected and be identified the output tables of data of multivariable process and be shown as { yi(k), k=1,2 ...,
N;I=1,2 ... m}.
The modelling by mechanism of a kind of multivariate continuous process model the most according to claim 3 merges identification side with Experimental modeling
Method, it is characterised in that described step 3) particularly as follows:
The gain of Experimental modeling model and exponent number directly take modelling by mechanism parameter, and the time class parameter of Experimental modeling model is with distinguishing
Know to optimize to calculate and obtain, it may be assumed that
WithIt is calculated by the identification optimization according to Identification Data.
The modelling by mechanism of a kind of multivariate continuous process model the most according to claim 3 merges identification side with Experimental modeling
Method, it is characterised in that described step 3) particularly as follows:
The model order of Experimental modeling directly takes modelling by mechanism model parameter, the gain of Experimental modeling model and time class parameter
Calculate with identification optimization and obtain, it may be assumed that
WithIt is calculated by the identification optimization according to Identification Data.
The modelling by mechanism of a kind of multivariate continuous process model the most according to claim 3 merges identification side with Experimental modeling
Method, it is characterised in that described step 3) particularly as follows:
The parameter optimization scope of Experimental modeling is according to modelling by mechanism parameter determination, it may be assumed that
WithBy the identification optimization meter according to Identification Data
Obtain.
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CN106773702A (en) * | 2017-01-03 | 2017-05-31 | 上海电力学院 | The multiple excitation discrimination method of Multivariable Linear continuous system |
CN110989349A (en) * | 2019-12-05 | 2020-04-10 | 上海电力大学 | Multivariate system model identification method |
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