CN104460317A - Control method for self-adaptive prediction functions in single-input and single-output chemical industry production process - Google Patents
Control method for self-adaptive prediction functions in single-input and single-output chemical industry production process Download PDFInfo
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Abstract
The invention relates to a control method for self-adaptive prediction functions in a single-input and single-output chemical industry production process. The control method mainly solves the problems that in the prior art, a chemical industry production process is hard to control, modeling is difficult, the control effect is poor, and adjustment and control cannot adapt to on-site working conditions in real time. The control method comprises the steps that production data are collected on line in the process, an established prediction model is only relevant to the production data at the current moment, and an on-line calibration self-adaptive algorithm is constructed by establishing two-degrees-of-freedom control input through linear weighting, wherein in the step of the establishing the two-degrees-of-freedom control input formed through linear weighting, firstly, future system control input formed by weighting the two primary functions is selected; secondly, according to a tracking object, control system desired performance indexes are established; thirdly, a future system predicts output derivation; fourthly, on-line self-adaptive adjustment is performed on system parameters. The technical scheme well solves the problems. The control method can be used in a series of single-input and single-output industry production devices in the fields of oil refining, chemical industry and the like.
Description
Technical field
The present invention relates to the control method of the self-adaptive prediction function of single-input single-output chemical process.
Background technology
Single-input single-output process is very general in actual industrial production, and great majority are realized by PID regulator device for the control of this class process at present.Adjusting of PID controller is normally obtained by the test of the technology such as relay feedback, it is generally after controller tuning, parameter remains unchanged, namely its parameter tuning obtains in the moment of putting into operation at controller, after process characteristic changes and is subject to external interference, controller parameter can not change thereupon.But in actual production, due to the impact of external source and external environment, usually can affect production run, thus cause the control effects of PID controller to be deteriorated.In addition, PID controller belongs to passive regulation strategy, is when detecting that real output value does not conform to set-point, just regulates.And in these adjustment processes, effectively not utilizing the inputoutput data information of actual production, this is because it does not become based on the model regulated yet always.All combined factors get up to cause based on its system effect of PID controller poor.
Predictive functional control algorithm is a member of Predictive Control System, and while maintenance forecast Control Algorithm advantage, it has the feature of self.The maximum feature of Predictive function control is, its control inputs can be considered as the weighting composition of the basis function selected in advance, so makes on-line optimization speed greatly improve; Secondly, Predictive function control does not have too much requirement for its forecast model, as long as relevant information can describe production feature, then these information all can be used as forecast model; 3rd, predictive functional control algorithm is used widely in actual industrial, and its superior interference free performance obtains the welcome of user.For this reason, notice has a large amount of production input/output informations in actual production, so very necessary model and these models of on-line correction carrying out constructing system in conjunction with these production informations, the current input of design con-trol system is carried out further by predictive functional control algorithm, very necessary, to obtain better control effects.
Hou Z. S. teaches at document " The model-free learning adaptive control of a class of SISO nonlinear systems " (Proc. of American Control Conf., New Mexico, 1997:343-344), by introducing the concept of partial differential, avoid the modeling problem of non-linear process, it is a kind of control method of good non-linear process, but it does not provide concrete systematic parameter control method, more do not utilize the future anticipation information of system.In practice because predictive control algorithm can utilize forecast model very well, the concepts such as feedback compensation have waited until better engineer applied, and wherein predictive functional control algorithm obtains the concern of more people due to its structurized control inputs form.Develop combination need not identification model and to realize simple Nonlinear Prediction Models method very necessary for this reason.
The current control for this type systematic is mainly to the correct application of Traditional PID regulator, Majhi professor is at document " Modified smith predictor and controller for processes with time delay " (IEE Proc.-Control Theory Appl. 1999, 140 (5), 359-366) PID proposed for this type systematic controls new method, follow-up have again other similar methods to occur, but the shortcoming of these class methods still shows and needs just can guarantee system stability by the regulator of more than 2 for Non-self-regulating system, result in regulating parameter in control method too much, and these regulating parameter great majority are based on the transfer function model of gained, limitation is larger.In addition, PID regulator belongs to passive regulation strategy, normally just regulable control input after interference occurs in the external world or systematic parameter perturbs, thus make corresponding control system robust performance bad, be difficult to provide good control effects, deposit in case for concrete tower Liquid level in external interference, ectocine can not be eliminated in time, cause tower level fluctuation, affect downstream and produce.
Summary of the invention
The present invention to mainly solve in prior art exist chemical process be difficult to control, modeling difficulty, control effects is not good enough, can not adapt to the problem of field working conditions adjustment in real time, provide the self-adaptive prediction function control method of single-input single-output chemical process.The method has online acquisition real-time production data and builds model, and the adaptive optimization algorithm built can the advantage of effectively anti-external interference.
For solving the problems of the technologies described above, the technical solution used in the present invention is as follows: a kind of control method of self-adaptive prediction function of single-input single-output chemical process, it is characterized in that the feature according to actual production process, online acquisition real-time production data, by building two freedom mechanisms weighting input, build on-line correction adaptive algorithm, the self-adaptive prediction function realizing single-input single-output chemical process controls, and described control method comprises the following steps:
(1) two freedom mechanisms input is set up: select system in future control inputs to have two basis functions
with
weighting forms, that is:
;
(2) according to tracking target, control system expected performance index is set up;
(3) derivation of system in future prediction output;
(4) the online adaptive adjustment of systematic parameter;
Wherein
for system will expect input future,
,
for control inputs weighting coefficient,
for the sampling time.
In technique scheme, preferred technical scheme, single-input single-output production run can be described as:
Wherein
be Generalized Lipschitz operators, represent the mapping relations in the past between production information and the output of process; For
the production system described, necessarily exists
, then when
time,
, now model prediction will export and can be expressed as future
According to process production requirement, determine that system in future expected performance index is as follows:
Wherein
,
the retrospective order of process input and output respectively,
,
the input and output value that process is produced respectively,
mapping function between expression process constrained input,
that model exports,
be
estimated value,
for the weighting coefficient of controlled quentity controlled variable,
for optimizing time domain,
(
)
Control time domain,
be that error surveyed by model, choose
;
reference locus, wherein
,
for expecting system response time,
for the reference mark that system is expected, for constant value setting point tracking
.
In technique scheme, preferred technical scheme, according to the weighting control inputs of definition, following desired output of deriving, adopt adaptive filter method to obtain the on-line tuning of system model parameter, utility index optimization obtains control inputs, specific as follows:
(1) current
moment control inputs meets
(2) future anticipation exports
(3) model parameter is assembled, order
, then
(
)
(4) model parameter adaptive filter method
(
)
(5) current time control inputs, by
,
?
In formula:
(
)，
,(
)，
。
The outstanding advantages of the self-adaptive prediction function control method of a kind of single-input single-output chemical process that the present invention proposes is:
(1) before Control System Design, modeling process is without the need to the structure of clear and definite controlled system;
(2) by Real-time Collection production data, utilize online adaptive control algolithm to correct institute's established model, thus ensure the feature of model energy Accurate Prediction object;
(3) adopt predictive functional control algorithm, adopt special two-freedom structure control input and employing economics kind to assemble concept further and on-line optimization speed is promoted greatly, decrease calculated amount, memory space;
(4) control method designed by is applied widely, requires low, adapts to wide, effectively can be applied to systems stabilisation, time-dependent system, a large class object of integrating system.
Accompanying drawing explanation
Fig. 1 is System control structures block diagram.
Fig. 2 control system tracing preset value curve of output.
Fig. 3 control system follows the tracks of set-point curve of output after procedure parameter changes.
In Fig. 1
for single-input single-output non-linear process object, PFC is designed prediction function controller,
for the process model that identification obtains,
for the model parameter that online adaptive identification obtains,
for the output of process,
for model exports,
for model and the output of process error,
for the deviation between the output of process and desired output,
for active procedure input.
In Fig. 2,1 is controlling curve of the present invention, and 2 is the controlling curve of A-H PI method, and 3 is the controlling curve of SIMC-PID method, and 4 is the controlling curve of SIMC-PI method.
In Fig. 3,1 is controlling curve of the present invention, and 2 is the controlling curve of A-H PI method, and 3 is the controlling curve of SIMC-PID method, and 4 is the controlling curve of SIMC-PI method.
Below by embodiment, the invention will be further elaborated.
Embodiment
[embodiment 1]
Consider that production run can be described as:
Wherein
and
.For the superiority of the design's method is described, PI setting method (A-H the PI) [Automatica 1991 of consideration and Astrom-Hagglund ' s, 27], Sigurd Skogestad ' s IMC-PI and IMC-PID (SIMC-PI, SIMC-PID) carry out control performance between method [Journal of Process Control, 2003] to compare.
The specific embodiment of the invention:
(1) according to process feature, certainty annuity characteristic parameter
and the sampling time
s.
(2) (initialization) is in the time
moment, given according to process characteristic
initial value
, given assembling parameter
it is the positive number between 0 to 1.
(3) suitable match point is chosen
,
, the closed loop response time
s, setup control time domain
and weighting coefficient
.Note, if controlled device contains time lag, then
with
selection be greater than the discrete value of time lag.
(4) exist
moment, gatherer process inputoutput data, application self-adapting learning algorithm
Carry out On-line Estimation
.To simultaneously
value bring step 3 into, if these conditions all can not meet, return step 3, otherwise continue step 5.
(5) calculate
(
)
Wherein future reference track input
，
(6) ask
optimum solution, order
,
obtain current time control law
And the control inputs of current time is applied to object.
(7)
, repeat step 4-6.
?
moment adds the load disturbance that amplitude is 0.2, and system step response as shown in Figure 2.As can be seen from the simulation curve of Fig. 2, no matter tracing preset value aspect or the anti-loading interference aspect of control system, the inventive method is obviously better than A-H PI, SIMC-PI, SIMC-PID method, shows the superiority of institute's inventive method.
Following consideration Parameter Perturbation situation, namely
,
.Now system step response curve as shown in Figure 3, easily find that SIMC-PI and SIMC-PID method follows the tracks of set-point after perturbation occurs systematic parameter and the vibration of anti-interference curve is very serious from diagram, and the interference free performance of A-H method neither be fine, but the inventive method can provide level and smooth aircraft pursuit course and acceptable interference free performance.Trace it to its cause, should sum up in the point that the inventive method applies and the online adaptive of system architecture model is estimated, thus define and control result preferably.
Content is that the inventive method is applied to the outstanding tracing preset value of exemplary production process and superior anti-external interference effect as explained above.In actual industrial, there is its process feature of many production runes and be difficult to express by concrete model form, result in other control methods and be difficult to application.Because the inventive method only make use of real-time input and output production data, and have employed predictive control theory and adaptive approach, this inventive method is made to have very strong interference free performance, (object is stablized for single-input single-output system, Object with Time Delay, unstable plant, Integrating etc.) control be a very superior method.
Claims (3)
1. the control method of the self-adaptive prediction function of a single-input single-output chemical process, it is characterized in that the feature according to actual production process, online acquisition real-time production data, by building two freedom mechanisms weighting input, build on-line correction adaptive algorithm, the self-adaptive prediction function realizing single-input single-output chemical process controls, and described control method comprises the following steps:
(1) two freedom mechanisms input is set up: select system in future control inputs to have two basis functions
with
weighting forms, that is:
;
(2) according to tracking target, control system expected performance index is set up;
(3) derivation of system in future prediction output;
(4) the online adaptive adjustment of systematic parameter;
Wherein
for system will expect input future,
,
for control inputs weighting coefficient,
for the sampling time.
2. the control method of the self-adaptive prediction function of single-input single-output chemical process according to claim 1, is characterized in that, single-input single-output production run can be described as:
Wherein
be Generalized Lipschitz operators, represent the mapping relations in the past between production information and the output of process; For
the production system described, necessarily exists
, then when
time,
, now model prediction will export and can be expressed as future
According to process production requirement, determine that system in future expected performance index is as follows:
Wherein
,
the retrospective order of process input and output respectively,
,
the input and output value that process is produced respectively,
mapping function between expression process constrained input,
that model exports,
be
estimated value,
for the weighting coefficient of controlled quentity controlled variable,
for optimizing time domain,
(
)
Control time domain,
be that error surveyed by model, choose
;
reference locus, wherein
,
for expecting system response time,
for the reference mark that system is expected, for constant value setting point tracking
.
3. the control method of the self-adaptive prediction function of single-input single-output chemical process according to claim 1, it is characterized in that the weighting control inputs according to definition, to derive following desired output, adaptive filter method is adopted to obtain the on-line tuning of system model parameter, utility index optimization obtains control inputs, specific as follows:
(1) current
moment control inputs meets
(2) future anticipation exports
(3) model parameter is assembled, order
, then
(
)
(4) model parameter adaptive filter method
(
)
(5) current time control inputs, by
,
?
In formula:
(
)，
,(
)，
。
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107615184A (en) * | 2015-06-05 | 2018-01-19 | 国际壳牌研究有限公司 | For the system and method for the backstage element switching that the model in application program is estimated and controlled for model prediction |
| CN108089435A (en) * | 2017-12-17 | 2018-05-29 | 北京世纪隆博科技有限责任公司 | A kind of model of mind collection PID controller design method |
| CN111913391A (en) * | 2020-08-12 | 2020-11-10 | 深圳职业技术学院 | Method for stabilizing self-adaptive control discrete time non-minimum phase system |
| CN112130453A (en) * | 2020-07-30 | 2020-12-25 | 浙江中控技术股份有限公司 | Control method and system for improving MCS production stability based on machine learning |
-
2013
- 2013-09-24 CN CN201310434758.1A patent/CN104460317A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107615184A (en) * | 2015-06-05 | 2018-01-19 | 国际壳牌研究有限公司 | For the system and method for the backstage element switching that the model in application program is estimated and controlled for model prediction |
| CN108089435A (en) * | 2017-12-17 | 2018-05-29 | 北京世纪隆博科技有限责任公司 | A kind of model of mind collection PID controller design method |
| CN108089435B (en) * | 2017-12-17 | 2021-02-05 | 北京世纪隆博科技有限责任公司 | Design method of intelligent model set PID controller |
| CN112130453A (en) * | 2020-07-30 | 2020-12-25 | 浙江中控技术股份有限公司 | Control method and system for improving MCS production stability based on machine learning |
| CN111913391A (en) * | 2020-08-12 | 2020-11-10 | 深圳职业技术学院 | Method for stabilizing self-adaptive control discrete time non-minimum phase system |
| CN111913391B (en) * | 2020-08-12 | 2022-05-24 | 深圳职业技术学院 | Method for stabilizing self-adaptive control discrete time non-minimum phase system |
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