CN109782586A - The tight format non-model control method of the different factor of the MISO of parameter self-tuning - Google Patents
The tight format non-model control method of the different factor of the MISO of parameter self-tuning Download PDFInfo
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Abstract
The invention discloses a kind of tight format non-model control methods of the different factor of the MISO of parameter self-tuning, for the existing limitation using the tight format non-model control method of MISO with factor structure, namely: the penalty factor of identical numerical value and the limitation of the step factor of identical numerical value can only be used by being directed to the different control inputs in control input vector at the k moment, propose a kind of tight format non-model control method of the MISO using different factor structure, the penalty factor of different numerical value and/or the step factor of different numerical value can be used for the different control inputs in control input vector at the k moment, it is able to solve the control problem that each control channel characteristic is different present in the complex objects such as strong nonlinearity MISO system, the method of parameter self-tuning is proposed simultaneously effectively to overcome penalty factor and step factor to need Want the time-consuming and laborious problem adjusted.Compared with existing control method, the present invention has higher control precision, better stability and wider array of applicability.
Description
Technical field
The invention belongs to automation control area, more particularly, to a kind of parameter self-tuning the tight format of the different factor of MISO without
Model control method.
Background technique
Oil refining, petrochemical industry, chemical industry, pharmacy, food, papermaking, water process, thermoelectricity, metallurgy, cement, rubber, machinery, electrical etc.
The controlled device of industry, including reactor, rectifying column, machine, unit, production line, workshop, factory, wherein many controlled
Object is MISO (Multiple Input and Single Output, multiple input single output) system.It realizes to MISO system
High-precision, high stable, high applicability control, to industry energy-saving, upgrading synergy be of great significance.However, MISO
The control problem of the control problem of system, especially strong nonlinearity MISO system is automated control field institute all the time
The significant challenge faced.
It include the tight format non-model control method of MISO in the existing control method of MISO system.The tight format model-free of MISO
Control method is a kind of novel data drive control method, does not depend on any mathematical model information of controlled device, relies only on
The analysis and design of controller are carried out in the inputoutput data of MISO controlled device real-time measurement, and realize concise, calculating
Small and strong robustness is born, is had a good application prospect.The theoretical basis of the tight format non-model control method of MISO, You Houzhong
" MFA control-theory and application " (Science Press, 2013, page 95) that raw and Jin Shangtai is collaborateed at it
Middle proposition, control algolithm are as follows:
Wherein, u (k) is to control input vector, u (k)=[u at the k moment1(k),…,um(k)]T, m is control input total number
(m is the positive integer greater than 1)；E (k) is k moment error；Φ (k) is k moment MISO system puppet Jacobian matrix estimated value, | |
Φ (k) | | it is 2 norms of matrix Φ (k)；λ is penalty factor；ρ is step factor.
The above-mentioned existing tight format non-model control method of MISO uses same factor structure, that is to say, that: at the k moment,
For the different control input u in control input vector u (k)1(k),…,um(k), the penalty factor λ of identical numerical value can only be used
With the step factor ρ of identical numerical value.When existing MISO is applied to strong nonlinearity with the tight format non-model control method of the factor
When the complex objects such as MISO system, since control channel characteristic is different, it tends to be difficult to realize ideal control effect, constrain
The popularization and application of the tight format non-model control method of MISO.
For this purpose, applying bottleneck to break existing MISO with the tight format non-model control method of the factor, the present invention is mentioned
A kind of tight format non-model control method of the different factor of MISO of parameter self-tuning is gone out.
Summary of the invention
In order to solve the problems, such as background technique, the object of the present invention is to provide a kind of parameter self-tunings
The tight format non-model control method of the different factor of MISO, it is characterised in that:
When controlled device is MISO (Multiple Input and Single Output, multiple input single output) system
When, the tight format non-model control method of the different factor of MISO calculates i-th of the control of k moment and inputs ui(k) mathematical formulae is such as
Under:
Wherein, k is positive integer；I indicates i-th in MISO system control input total number, and i is positive integer, 1≤
I≤m, m are MISO system control input total number, and m is the positive integer greater than 1；ui(k) it is controlled for i-th of the k moment defeated
Enter；E (k) is k moment error；Φ (k) is k moment MISO system puppet Jacobian matrix estimated value, φj,iIt (k) is matrix Φ (k)
The i-th column element of jth row, | | Φ (k) | | be matrix Φ (k) 2 norms；λiFor the penalty factor of i-th of control input；ρiFor
The step factor of i-th of control input；
For MISO system, the value of i is traversed positive integer area by the tight format non-model control method of the different factor of MISO
Between all values in [1, m], can be calculated the k moment controls input vector u (k)=[u1(k),…,um(k)]T；
The tight format non-model control method of the different factor of MISO has different ratio characteristics；The different ratio characteristics are pointers
To positive integer i and x that positive integer section [1, m] interior any two are not mutually equal, in the use control method to MISO system
Control period is carried out, at least there is a moment, so that at least one inequality is set up in following two inequality:
λi≠λx；ρi≠ρx
Control period is being carried out to MISO system using the control method, is controlling input vector u (k) to the k moment is calculated
=[u1(k),…,um(k)]TMathematical formulae in setting parameter carry out parameter self-tuning；It is described to setting parameter include punish
Penalty factor λi, step factor ρiOne of any or any number of combination of (i=1 ..., m).
The parameter self-tuning is using control input vector u (k)=[u described in neural computing1(k),…,um(k)]T
Mathematical formulae in setting parameter；In hidden layer weight coefficient, the output layer weight coefficient for updating the neural network, use
Control input vector u (k)=[u1(k),…,um(k)]TRespectively in respective mathematical formulae when setting parameter in k
The gradient at quarter；Control input vector u (k)=[u1(k),…,um(k)]TIn ui(k) (i=1 ..., m) it is directed to the ui
(k) gradient to setting parameter at the k moment in mathematical formulae, by ui(k) it is directed to the u respectivelyi(k) in mathematical formulae
It is each to setting parameter the k moment partial derivative form；The ui(k) it is directed to the u respectivelyi(k) each in mathematical formulae
A partial derivative to setting parameter at the k moment, is calculated using following mathematical formulae:
As the ui(k) in mathematical formulae includes penalty factor λ to setting parameteriWhen, ui(k) it is directed to the punishment
Factor lambdaiIn the partial derivative at k moment are as follows:
As the ui(k) in mathematical formulae includes step factor ρ to setting parameteriWhen, ui(k) it is directed to the step-length
Factor ρiIn the partial derivative at k moment are as follows:
The u being calculatedi(k) it is directed to the u respectivelyi(k) each in mathematical formulae is when setting parameter in k
The partial derivative at quarter is all put into set { ui(k) gradient }；For MISO system, by the value traversal positive integer section of i [1,
M] in all values, respectively obtain set { u1(k) gradient } ..., gather { um(k) gradient }, and all it is put into set { ladder
Degree set }, the set { gradient set } is comprising all { { u1(k) gradient } ..., { um(k) gradient } } set；
The parameter self-tuning is using control input vector u (k)=[u described in neural computing1(k),…,um(k)]T
Mathematical formulae in setting parameter, the input of the neural network includes element, collection in the set { gradient set }
Close one of any or any number of combination of the element in { error set }；The set { error set } includes e's (k) and e (k)
Error function group；The error function group of the e (k) is the k moment and the accumulation of error of all moment before isThe k moment
Second order backward difference e (k) -2e (k-1)+e of single order backward difference e (k)-e (k-1) of error e (k), k moment error e (k)
(k-2), one of any or any number of combination of the high-order backward difference of k moment error e (k).
While by adopting the above technical scheme, the present invention can also be used or be combined using technology further below
Scheme:
The k moment error e (k) is calculated using error calculation function；The error calculation argument of function packet
The desired value containing output and output actual value.
The error calculation function uses e (k)=y*(k)-y (k), wherein y*(k) desired value is exported for the k moment, y (k) is
The k moment exports actual value；Or use e (k)=y*(k+1)-y (k), wherein y*(k+1) desired value is exported for the k+1 moment；Or
Using e (k)=y (k)-y*(k)；Or use e (k)=y (k)-y*(k+1)。
The neural network is BP neural network；The BP neural network uses hidden layer for the structure of single layer, that is, uses
The Three Tiered Network Architecture being made of input layer, single layer hidden layer, output layer.
The neural network is minimised as target with the value of system error function, carries out systematic error using gradient descent method
Backpropagation calculates, and updates hidden layer weight coefficient, the output layer weight coefficient of the neural network；The system error function from
Variable includes one of any or any number of combination of error e (k), output desired value, output actual value.
The system error function isWherein, e (k) is k moment error, Δ uiu(k)=
uiu(k)-uiu(k-1), uiuIt (k) is the u control input of k moment i-th, a and biuFor the constant more than or equal to 0, iu is positive whole
Number.
The controlled device includes reactor, rectifying column, machine, unit, production line, workshop, factory.
The hardware platform for running the control method includes industrial control computer, singlechip controller, microprocessor control
Device processed, field programmable gate array controller, Digital Signal Processing controller, embedded system controller, programmable logic control
Device processed, Distributed Control System, field bus control system, industrial Internet of Things network control system, industry internet control system are appointed
One of meaning or any number of combination.
The tight format non-model control method of the different factor of the MISO of parameter self-tuning provided by the invention, for control input to
The penalty factor of different numerical value or the step factor of different numerical value can be used in different control inputs in amount, is able to solve strong non-thread
Property the complex objects such as MISO system present in the different control problem of each control channel characteristic, while effectively overcome punishment because
Son and step factor need the time-consuming and laborious problem adjusted.Therefore, with existing MISO with the tight format model-free control of the factor
Method processed is compared, and the tight format non-model control method of the different factor of the MISO of parameter self-tuning provided by the invention has higher control
Precision, better stability and wider array of applicability processed.
Detailed description of the invention
Fig. 1 is the principle of the present invention block diagram；
Fig. 2 is i-th of BP neural network structural schematic diagram that the present invention uses；
Fig. 3 is that the single output MISO system of two inputs uses the tight format of the different factor of MISO of parameter self-tuning of the invention without mould
Control effect figure when type control method；
Fig. 4 is that the single output MISO system of two inputs uses the tight format of the different factor of MISO of parameter self-tuning of the invention without mould
Control input curve when type control method；
Fig. 5 is that the single output MISO system of two inputs uses the tight format of the different factor of MISO of parameter self-tuning of the invention without mould
Penalty factor change curve when type control method；
Fig. 6 is that the single output MISO system of two inputs uses the tight format of the different factor of MISO of parameter self-tuning of the invention without mould
Step factor change curve when type control method；
When Fig. 7 is the single output MISO systems of two inputs tight with the factor using existing MISO format non-model control method
Control effect figure；
When Fig. 8 is the single output MISO systems of two inputs tight with the factor using existing MISO format non-model control method
Control input curve.
Specific embodiment
Present invention will be further explained below with reference to the attached drawings and specific examples.
Fig. 1 gives the principle of the present invention block diagram.For the MISO with m control input (m is the positive integer greater than 1)
System is controlled using the tight format non-model control method of the different factor of MISO.U is inputted for i-th of controli(k) (i=
1 ..., m), the tight format non-model control method of the different factor of MISO is for calculating ui(k) parameter of mathematical formulae include punishment because
Sub- λi, step factor ρi；Select ui(k) in mathematical formulae to setting parameter, be ui(k) parameter of mathematical formulae
It partly or entirely, include penalty factor λi, step factor ρiOne of any or any number of combination；In the schematic diagram of Fig. 1, institute
There is control input uiIt (k) in the mathematical formulae of (i=1 ..., m) is penalty factor λ to setting parameteri, step factor ρi；ui
(k) being calculated to setting parameter using i-th of BP neural network in mathematical formulae.
Fig. 2 is i-th of BP neural network structural schematic diagram that the present invention uses；BP neural network can use hidden layer for
The structure of single layer can also use hidden layer for the structure of multilayer；In the schematic diagram of Fig. 2, for simplicity, BP neural network
Hidden layer is used for the structure of single layer, i.e., using the Three Tiered Network Architecture being made of input layer, single layer hidden layer, output layer；
Determine the input layer number, node in hidden layer, output layer number of nodes of i-th of BP neural network；I-th BP neural network
Input layer number is set as m × 2+3, and wherein the input of m × 2 input layer is the element gathered in { gradient set }Wherein the input of 3 input layers is in set { error set }
ElementThe output layer number of nodes of i-th of BP neural network is no less than ui(k) number
The number to setting parameter in formula is learned, is set as u in Fig. 2i(k) in mathematical formulae to setting parameter number 2, point
It Shu Chu not penalty factor λi, step factor ρi；The hidden layer weight coefficient of i-th of BP neural network, output layer weight coefficient it is updated
Journey specifically: target is minimised as with the value of system error function, with system error function e in Fig. 22(k) value is minimised as
Target carries out systematic error backpropagation calculating using gradient descent method, and the hidden layer for updating i-th of BP neural network weighs system
Number, output layer weight coefficient；Update i-th of BP neural network hidden layer weight coefficient, output layer weight coefficient when, using comprising
{u1(k) gradient } ..., { um(k) gradient } set { gradient set } in element, that is, using control input vector u
(k)=[u1(k),…,um(k)]TRespectively in respective mathematical formulae to setting parameter the k moment gradient
In conjunction with the above description of Fig. 1 and Fig. 2, the realization step of technical solution of the present invention is further described below:
The k moment will be denoted as current time；Desired value y will be exported*(k) it is missed with the difference of output actual value y (k) as the k moment
Poor e (k)；Then it will gather the element in { gradient set }With set
Element in { error set }As the input of i-th of BP neural network, i-th of BP
Neural network carries out forward calculation, and it is tight to obtain the different factor of MISO by the output layer output of i-th of BP neural network for calculated result
Format non-model control method calculates ui(k) value to setting parameter in mathematical formulae；Based on error e (k), ui(k) number
The value to setting parameter in formula is learned, i-th of k moment control is calculated using the tight format non-model control method of the different factor of MISO
Input ui(k)；By all values in the value of i traversal positive integer section [1, m], can be calculated the k moment control input to
Measure u (k)=[u1(k),…,um(k)]T；For the u in control input vector u (k)i(k), it calculates separately for ui(k) mathematics
Each partial derivative to setting parameter at the k moment in formula, is all put into set { ui(k) gradient }；The value of i is traversed
All values in positive integer section [1, m] respectively obtain set { u1(k) gradient } ..., gather { um(k) gradient }, and it is complete
Portion is put into set { gradient set }；Then, with system error function e2(k) value is minimised as target, uses set { gradient collection
Close } in gradientIt is reversed that systematic error is carried out using gradient descent method
It propagates and calculates, update hidden layer weight coefficient, the output layer weight coefficient of i-th of BP neural network；The value of i is traversed into positive integer area
Between all values in [1, m], i.e., hidden layer weight coefficient, the output layer weight coefficient of renewable whole m BP neural networks；It controls defeated
After incoming vector u (k) acts on controlled device, controlled device is obtained in the output actual value of later moment in time, then repeats this
Step described in paragraph carries out the control of later moment in time to MISO system.
The following is specific embodiments of the present invention.
The single output MISO system of two inputs that controlled device uses, the complex characteristic with strong nonlinearity belong to typical hardly possible
The MISO system of control:
System exports desired value y*(k) as follows:
y*(k)=(- 1)round((k-1)/100)
In a particular embodiment, m=2.
For above-mentioned specific embodiment, carries out five groups of tests and compare verifying.In order to more clearly compare five groups of tests
Control performance, using root-mean-square error (Root Mean Square Error, RMSE) be used as control performance evaluation index:
Wherein, e (k)=y*(k)-y (k), y*(k) desired value is exported for the k moment, y (k) is to export actual value at the k moment.
The value of RMSE (e) is smaller, shows to export actual value y (k) and exports desired value y*(k) error is smaller in general, controlling
It can be more preferable.
The hardware platform for running control method of the present invention uses industrial control computer.
When battery of tests: the input layer number of the 1st BP neural network and the 2nd BP neural network is set as 7,
Wherein the input of 4 input layers is the element gathered in { gradient set } Wherein the input of 3 input layers is set { error
Set } in element1st BP neural network is hidden with the 2nd BP neural network
Number containing node layer is set as 6；The output layer number of nodes of 1st BP neural network and the 2nd BP neural network is set as 2,
Wherein the 1st BP neural network exports penalty factor λ respectively1With step factor ρ1, the 2nd BP neural network export punishment respectively
Factor lambda2With step factor ρ2；Then the tight format non-model control method of the different factor of MISO of parameter self-tuning of the invention is used,
The above-mentioned single output MISO system of two inputs is controlled, Fig. 3 is the control effect figure of output, and Fig. 4 is control input curve, figure
5 be penalty factor change curve, and Fig. 6 is step factor change curve；It is investigated from control performance evaluation index, it is defeated in Fig. 3
RMSE (e) out is 0.3062；It is investigated from different ratio characteristics, the penalty factor change curve of two control inputs in Fig. 5
There are the nonoverlapping phenomenon in part show when controlling the above-mentioned single output MISO system of two inputs penalty factor have it is different because
Subcharacter, in Fig. 6 the step factor change curve of two control inputs there are the nonoverlapping phenomenon in part show it is defeated to above-mentioned two
Entering step factor when single output MISO system is controlled has different ratio characteristics.
When second group of test: the input layer number of the 1st BP neural network and the 2nd BP neural network is set as 4,
The input of 4 input layers is the element gathered in { gradient set } The node in hidden layer of 1st BP neural network and the 2nd BP neural network is set as 6；1st BP mind
Output layer number of nodes through network and the 2nd BP neural network is set as 2, wherein the 1st BP neural network exports punish respectively
Penalty factor λ1With step factor ρ1, the 2nd BP neural network export penalty factor λ respectively2With step factor ρ2；Then using this
The tight format non-model control method of the different factor of the MISO of the parameter self-tuning of invention, to the above-mentioned single output MISO system of two inputs into
Row control；It is investigated from control performance evaluation index, the RMSE (e) of output is 0.3140.
When third group is tested: the input layer number of the 1st BP neural network and the 2nd BP neural network is set as 3,
The input of 3 input layers is the element gathered in { error set }1st
The node in hidden layer of BP neural network and the 2nd BP neural network is set as 6；1st BP neural network and the 2nd BP mind
Output layer number of nodes through network is set as 2, wherein the 1st BP neural network exports penalty factor λ respectively1With step factor
ρ1, the 2nd BP neural network export penalty factor λ respectively2With step factor ρ2；Then using parameter self-tuning of the invention
The tight format non-model control method of the different factor of MISO controls the above-mentioned single output MISO system of two inputs；From control performance
Evaluation index is investigated, and the RMSE (e) of output is 0.3297.
When the 4th group of test: penalty factor λ1With step factor ρ1It is fixed, only select the 2nd control defeated to setting parameter
The penalty factor λ entered2With step factor ρ2, therefore need to only use a BP neural network；The input layer number of BP neural network
3 are set as, the input of 3 input layers is the element gathered in { error set }The node in hidden layer of BP neural network is set as 6；The output layer of BP neural network
Number of nodes is set as 2, exports penalty factor λ respectively2With step factor ρ2；Then the MISO of parameter self-tuning of the invention is used
The different tight format non-model control method of the factor controls the above-mentioned single output MISO system of two inputs；It is evaluated from control performance
Index is investigated, and the RMSE (e) of output is 0.3300.
When the 5th group of test: directly adopt existing MISO with the tight format non-model control method of the factor, setting punishment because
Sub- λ=3 set step factor ρ=1, control the above-mentioned single output MISO system of two inputs, and Fig. 7 is the control effect of output
Fruit figure, Fig. 8 are control input curve；It is investigated from control performance evaluation index, the RMSE (e) exported in Fig. 8 is 0.3396.
The comparison result of five groups of controlling test Performance Evaluating Indexes is listed in table 1, using of the invention first group to the 4th group
The result of test is superior to the 5th group of test using existing MISO with the tight format non-model control method of the factor, wherein passing through
Comparison diagram 3 and Fig. 7 can be found that the improvement effect of battery of tests is especially significant, sufficiently shows parameter provided by the invention from whole
There is the tight format non-model control method of the different factor of fixed MISO higher control precision, better stability to be applicable in wider array of
Property.
1 control performance of table compares
Further, it should also particularly point out at following 6 points:
(1) oil refining, petrochemical industry, chemical industry, pharmacy, food, papermaking, water process, thermoelectricity, metallurgy, cement, rubber, machinery, electrical
Etc. industries controlled device, including reactor, rectifying column, machine, unit, production line, workshop, factory, wherein much quilts
Controlling object is MISO system, and the complex characteristic with strong nonlinearity, is typical difficult control object；For example, for example, oil refining,
The common continuous-stirring reactor CSTR of the industries such as petrochemical industry, chemical industry, pharmacy is exactly the common single output MISO system of two inputs,
Two control inputs are feed rate and cooling water flow respectively, and output is reaction temperature；When chemical reaction is with strongly exothermic
When effect, the MISO system of continuous-stirring reactor CSTR just has the complex characteristic of strong nonlinearity, is typical difficult control object.
In the above specific embodiment, the single output MISO system of two inputs that controlled device uses, it may have the complexity of strong nonlinearity is special
Sign belongs to the MISO system for being particularly difficult to control；The present invention can be realized high-precision, high stable, Gao Shiyong to the controlled device
The control of property, illustrates that control method of the invention also can be to reactor, rectifying column, machine, unit, production line, vehicle
Between, the complicated MISO system such as factory realize the control of high-precision, high stable, high applicability.
(2) in the above specific embodiment, the hardware platform for running control method of the present invention is industrial control computer；?
When practical application, singlechip controller, microprocessor controller, field-programmable gate array can also be selected as the case may be
Column controller, embedded system controller, programmable logic controller (PLC), Distributed Control System, shows Digital Signal Processing controller
One of any or any number of group of cooperation of field bus control system, industrial Internet of Things network control system, industry internet control system
For the hardware platform for running control method of the present invention.
(3) in the above specific embodiment, desired value y will be exported*(k) and the difference of output actual value y (k) is as the k moment
Error e (k), that is, e (k)=y*(k)-y (k), one of only described error calculation function method；It can also be by k+1
Moment exports desired value y*(k+1) and the k moment exports the difference of y (k) as error e (k), that is, e (k)=y*(k+1)-y(k)；
The error calculation function can also include output desired value and other calculation methods for exporting actual value, citing using independent variable
For,For the controlled device of above-mentioned specific embodiment, using above-mentioned different
Error calculation function can realize good control effect.
(4) input of BP neural network includes the element in set { gradient set }, the element in set { error set }
One of any or any number of combination；When the input of BP neural network includes the element in set { gradient set }, above-mentioned tool
Body embodiment has selected the gradient at k-1 moment, i.e.,Actually answering
Used time can also further increase the gradient at more moment as the case may be, for example, the gradient at k-2 moment can be increased,
I.e.When the input of BP neural network includes set { error collection
Close } in element when, above-mentioned specific embodiment selectsMay be used also in practical application
As the case may be, to increase more error function groups in set { error set }, for example, the two of e (k) can be increased
Rank backward difference, that is by { e (k) -2e (k-1)+e (k-2) } also as the input of BP neural network；Further, BP
The input of neural network is including but not limited to the element in set { gradient set }, the element in set { error set }, citing
For, { u can be increased1(k-1),u2(k-1) } also as the input of BP neural network；To the controlled device of above-mentioned specific embodiment
For, when the input layer number of BP neural network is continuously increased, it can realize good control effect, in most cases
It can also slightly improve, but also increase computation burden simultaneously, so the input layer number of BP neural network is in practical application
Reasonable number can be set as the case may be.
(5) in the above specific embodiment, target is minimised as in the value with system error function to update BP nerve net
When the hidden layer weight coefficient of network, output layer weight coefficient, the system error function uses e2(k), the only described systematic error letter
One of number function；The system error function can also include error, output desired value, output actual value using independent variable
Other one of any or any number of combination functions, for example, the system error function uses (y*(k)-y(k))2Or
(y*(k+1)-y(k))2, that is, use e2(k) another functional form；Again for example, the system error function is adopted
WithWherein, e (k) is k moment error, Δ uiu(k)=uiu(k)-uiu(k-1), uiu(k) be k when
Carve i-th u control input, a and biuFor the constant more than or equal to 0, iu is positive integer；Obviously, work as biuWhen being 0, the system
System error function only accounts for e2(k) contribution shows that the target minimized is systematic error minimum, that is, pursues precision
It is high；And work as biuWhen greater than 0, the system error function considers e simultaneously2(k) contribution andContribution, show to minimize
Target pursue systematic error it is small while, it is small also to pursue control input variation, that is, not only pursued high pursue again of precision and grasped
It is vertical steady；For the controlled device of above-mentioned specific embodiment, using above-mentioned different system error function, it can realize good
Control effect；E is only considered with system error function2(k) control effect when contributing is compared, and is examined simultaneously in system error function
Consider e2(k) contribution andContribution when its control precision slightly reduce and its manipulate stationarity be then improved.
(6) the tight format non-model control method of the different factor of the MISO of the parameter self-tuning includes punishment to setting parameter
Factor lambdai, step factor ρiOne of any or any number of combination of (i=1 ..., m)；In the above specific embodiment, it arrives for first group
When the verification experimental verification of third group, to penalty factor λ1,λ2With step factor ρ1,ρ2All realize parameter self-tuning, and the 4th group
Penalty factor λ is then fixed when test1With step factor ρ1, and only to the penalty factor λ of the 2nd control input2With step factor ρ2
Realize parameter self-tuning；In practical application, any number of combination to setting parameter can also be selected as the case may be；This
Outside, described to setting parameter includes but is not limited to penalty factor λi, step factor ρi(i=1's ..., m) is one of any or any
Kind combination；For example, as the case may be, it is described to setting parameter can also include calculate MISO system puppet Jacobian matrix
Parameter needed for estimated value Φ (k).
Above-mentioned specific embodiment is used to illustrate the present invention, is merely a preferred embodiment of the present invention, rather than to this
Invention is limited, and within the spirit of the invention and the scope of protection of the claims, to any modification of the invention made, is equal
Replacement, improvement etc., both fall within protection scope of the present invention.
Claims (9)
1. the tight format non-model control method of the different factor of the MISO of parameter self-tuning, it is characterised in that:
When controlled device is MISO (Multiple Input and Single Output, multiple input single output) system, institute
It states the tight format non-model control method of the different factor of MISO and calculates i-th of control of k moment input ui(k) mathematical formulae is as follows:
Wherein, k is positive integer；I indicates i-th in MISO system control input total number, and i is positive integer, 1≤i≤
M, m are MISO system control input total number, and m is the positive integer greater than 1；ui(k) it is inputted for i-th of the control of k moment；e
It (k) is k moment error；Φ (k) is k moment MISO system puppet Jacobian matrix estimated value, φj,i(k) jth for being matrix Φ (k)
The i-th column element of row, | | Φ (k) | | it is 2 norms of matrix Φ (k)；λiFor the penalty factor of i-th of control input；ρiIt is i-th
Control the step factor of input；
For MISO system, the tight format non-model control method of the different factor of MISO by the value of i traversal positive integer section [1,
M] in all values, can be calculated the k moment controls input vector u (k)=[u1(k),…,um(k)]T；
The tight format non-model control method of the different factor of MISO has different ratio characteristics；The different ratio characteristics refer to for just
The positive integer i and x that integer range [1, m] interior any two are not mutually equal are carrying out MISO system using the control method
At least there is a moment in control period, so that at least one inequality is set up in following two inequality:
λi≠λx；ρi≠ρx
Control period is being carried out to MISO system using the control method, is controlling input vector u (k)=[u to the k moment is calculated1
(k),…,um(k)]TMathematical formulae in setting parameter carry out parameter self-tuning；It is described to setting parameter include punishment because
Sub- λi, step factor ρiOne of any or any number of combination of (i=1 ..., m).
2. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 1, feature exist
In: the parameter self-tuning is using control input vector u (k)=[u described in neural computing1(k),…,um(k)]TMathematics
In formula to setting parameter；In hidden layer weight coefficient, the output layer weight coefficient for updating the neural network, the control is used
Input vector u (k) processed=[u1(k),…,um(k)]TRespectively in respective mathematical formulae to setting parameter the k moment ladder
Degree；Control input vector u (k)=[u1(k),…,um(k)]TIn ui(k) (i=1 ..., m) it is directed to the ui(k) number
The gradient to setting parameter at the k moment in formula is learned, by ui(k) it is directed to the u respectivelyi(k) in mathematical formulae it is each to
Partial derivative of the setting parameter at the k moment forms；The ui(k) it is directed to the u respectivelyi(k) each wait adjust in mathematical formulae
Partial derivative of the parameter at the k moment, is calculated using following mathematical formulae:
As the ui(k) in mathematical formulae includes penalty factor λ to setting parameteriWhen, ui(k) it is directed to the penalty factor λi
In the partial derivative at k moment are as follows:
As the ui(k) in mathematical formulae includes step factor ρ to setting parameteriWhen, ui(k) it is directed to the step factor ρi
In the partial derivative at k moment are as follows:
The u being calculatedi(k) it is directed to the u respectivelyi(k) each in mathematical formulae is to setting parameter at the k moment
Partial derivative is all put into set { ui(k) gradient }；It, will be in value traversal positive integer section [1, m] of i for MISO system
All values, respectively obtain set { u1(k) gradient } ..., gather { um(k) gradient }, and all it is put into set { gradient collection
Close, the set { gradient set } is comprising all { { u1(k) gradient } ..., { um(k) gradient } } set；
The parameter self-tuning is using control input vector u (k)=[u described in neural computing1(k),…,um(k)]TNumber
Learn in formula to setting parameter, the input of the neural network include element in the set { gradient set }, set { accidentally
Difference set } in element one of any or any number of combination；The set { error set } includes the error of e (k) and e (k)
Group of functions；The error function group of the e (k) is the k moment and the accumulation of error of all moment before isK moment error e
(k) second order backward difference e (k) -2e (k-1)+e (k-2), the k of single order backward difference e (k)-e (k-1), k moment error e (k)
One of any or any number of combination of the high-order backward difference of moment error e (k).
3. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 1, feature exist
In: the k moment error e (k) is calculated using error calculation function；The error calculation argument of function includes output
Desired value and output actual value.
4. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 3, feature exist
In: the error calculation function uses e (k)=y*(k)-y (k), wherein y*(k) desired value is exported for the k moment, y (k) is the k moment
Export actual value；Or use e (k)=y*(k+1)-y (k), wherein y*(k+1) desired value is exported for the k+1 moment；Or use e
(k)=y (k)-y*(k)；Or use e (k)=y (k)-y*(k+1)。
5. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 2, feature exist
In: the neural network is BP neural network；The BP neural network uses hidden layer for the structure of single layer, i.e., using by inputting
The Three Tiered Network Architecture of layer, single layer hidden layer, output layer composition.
6. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 2, feature exist
In: the neural network is minimised as target with the value of system error function, and it is reversed to carry out systematic error using gradient descent method
It propagates and calculates, update hidden layer weight coefficient, the output layer weight coefficient of the neural network；The independent variable of the system error function
One of any or any number of combination comprising error e (k), output desired value, output actual value.
7. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 6, feature exist
In: the system error function isWherein, e (k) is k moment error, Δ uiu(k)=uiu
(k)-uiu(k-1), uiuIt (k) is the u control input of k moment i-th, a and biuFor the constant more than or equal to 0, iu is positive integer.
8. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 1, feature exist
In: the controlled device includes reactor, rectifying column, machine, unit, production line, workshop, factory.
9. the tight format non-model control method of the different factor of the MISO of parameter self-tuning according to claim 1, feature exist
In: run the control method hardware platform include industrial control computer, singlechip controller, microprocessor controller,
Field programmable gate array controller, Digital Signal Processing controller, embedded system controller, programmable logic controller (PLC),
Distributed Control System, field bus control system, industrial Internet of Things network control system, industry internet control system it is one of any
Or any number of combination.
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