CN108107721A - Decoupling control methods of the MIMO based on the inclined form Non-Model Controllers of SISO Yu local derviation information - Google Patents

Decoupling control methods of the MIMO based on the inclined form Non-Model Controllers of SISO Yu local derviation information Download PDF

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CN108107721A
CN108107721A CN201711187045.4A CN201711187045A CN108107721A CN 108107721 A CN108107721 A CN 108107721A CN 201711187045 A CN201711187045 A CN 201711187045A CN 108107721 A CN108107721 A CN 108107721A
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siso
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CN108107721B (en
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卢建刚
李雪园
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Zhejiang University ZJU
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Abstract

The invention discloses a kind of decoupling control methods of MIMO based on the inclined form Non-Model Controllers of SISO Yu local derviation information, first according to MIMO (Multiple Input and Multiple Output, multiple-input and multiple-output) system coupling feature and tendentiousness feature, mimo system is resolved into multiple SISO (Single Input and Single Output, single-input single-output) systems to intercouple；SISO systems are controlled using the inclined form Non-Model Controllers of SISO；Based on BP neural network, using local derviation information as input, target is minimised as with the system error function value for considering all SISO systematic errors contributions, using gradient descent method, and combine the gradient information that control input respectively treats setting parameter for controller respectively, systematic error backpropagation calculating is carried out, realizes the online self-tuning of the parameters such as penalty factor, the step factor of the inclined form Non-Model Controllers of SISO, and synchronously realizes the online decoupling between multiple SISO systems.Method proposed by the present invention is the effective means for solving mimo system control problem, it can be achieved that good control effect.

Description

Decoupling controls of the MIMO based on the inclined form Non-Model Controllers of SISO Yu local derviation information Method
Technical field
The invention belongs to automation control areas, and the inclined form Non-Model Controllers of SISO are based on more particularly, to a kind of MIMO With the decoupling control method of local derviation information.
Background technology
The control problem of MIMO (Multiple Input and Multiple Output, multiple-input and multiple-output) system, One of significant challenge that control field is faced is automated all the time.Mimo system is typically characterised by coupling, That is：The variation of one input often makes multiple outputs change, and an output is often also not only by an input Influence；But meanwhile this coupling is in most cases, especially in industrial process automation control field, and understands body Reveal tendentiousness feature, that is to say, that：The variation of one input often tends to that some is made specifically to export generation significant changes And others output then being influenced smaller, an output often tends to be significantly affected by some specific input and be subject to other defeated The influence entered is then smaller.The tendentiousness feature of mimo system, to be broken down into multiple SISO (Single Input and Single Output, single-input single-output) system provides feasibility；And the coupling feature of mimo system, and imply this A little SISO systems must synchronously solve the online solution between multiple SISO systems when SISO controller is respectively adopted and is controlled Coupling problem.
SISO controller is there are many implementation method, including the inclined form Non-Model Controllers of SISO.The inclined forms of SISO without Model controller is a kind of new data drive control method, does not depend on any mathematical model information of controlled device, only according to Rely the analysis and design of the inputoutput data that is measured in real time in SISO controlled devices into line control unit, and realize concise, meter It calculates and bears small and strong robustness, unknown nonlinear time-varying SISO systems can also be controlled well, there is good answer Use prospect.The theoretical foundation of the inclined form Non-Model Controllers of SISO is collaborateed by Hou Zhong lifes with Jin Shangtai at it《Model-free is adaptive Should control-theoretical with applying》It is proposed in (Science Press, 2013, page 68), control algolithm is as follows：
Wherein, u (k) is the control input at k moment；Δ u (k)=u (k)-u (k-1)；E (k) is the systematic error at k moment；For the pseudo- gradient estimate at k moment,ForI-th of component (i=1 ..., L)；L input linear in order to control Length constant, and be the integer more than 1；λ is penalty factor；ρ1,…,ρLFor step factor.
However, the inclined form Non-Model Controllers of SISO need to rely on Heuristics before actually coming into operation punishment is previously set Factor lambda and step factor ρ1,…,ρLEtc. parameters numerical value, penalty factor λ and step-length are also not yet realized during actually come into operation Factor ρ1,…,ρLEtc. parameters online self-tuning.Parameter effectively adjusts the shortage of means, not only makes the inclined form model-free controls of SISO The use debugging process of device processed is time-consuming and laborious, and can also seriously affect the control effect of the inclined form Non-Model Controllers of SISO sometimes Fruit constrains the popularization and application of the inclined form Non-Model Controllers of SISO.That is：The inclined form Non-Model Controllers of SISO are in reality Border also needs to solve the problems, such as online self-tuning parameter during coming into operation.
For this purpose, the present invention proposes a kind of decoupling controls of MIMO based on the inclined form Non-Model Controllers of SISO Yu local derviation information Method processed, can synchronously solve the problems, such as the inclined form Non-Model Controller online self-tuning parameters of SISO and multiple SISO systems it Between the problem that decouples online, the control problem to solve mimo system provides a kind of new method.
The content of the invention
In order to solve the problems, such as present in background technology, it is inclined to be based on SISO it is an object of the present invention to provide a kind of MIMO The decoupling control method of form Non-Model Controller and local derviation information.
For this purpose, the above-mentioned purpose of the present invention is achieved through the following technical solutions, comprise the following steps：
Step (1)：For with mi input (mi be integer) more than or equal to 2 and mo output (mo be more than or Integer equal to 2) MIMO (Multiple Input and Multiple Output, multiple-input and multiple-output) system, choose An input in mi input and an output in mo output, form a SISO (Single Input and Single Output, single-input single-output) system；Repeated m time (m >=1 and m≤mi and m≤mo and m is integer), forms m SISO systems, wherein the input of one of arbitrary SISO systems is all not as the input of other SISO systems, one of arbitrary SISO systems The output of system is all not as the output of other SISO systems；The m SISO systems use the inclined form Model free controls of m SISO Device is controlled；
Step (2)：For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, determine that its control input linearizes Length constant Lj, LjTo be more than 1 integer；The inclined form Non-Model Controller parameters of j-th of SISO include penalty factor λjWith Step factorDetermine that the inclined form Non-Model Controllers of j-th of SISO treat setting parameter, described j-th The inclined form Non-Model Controllers of SISO treat setting parameter, are the part of the inclined form Non-Model Controller parameters of j-th of SISO Or all, include penalty factor λjAnd step factorOne of arbitrary or any number of combination；Determine j-th of BP god Input layer number, node in hidden layer, output layer number of nodes through network, the output layer number of nodes are no less than the jth A inclined form Non-Model Controllers of SISO treat setting parameter number；Initialize the hidden layer weight coefficient, defeated of j-th of BP neural network Go out a layer weight coefficient；Initialize the local derviation information in { local derviation information j } set；If m >=2, for other inclined lattice of m-1 SISO Formula Non-Model Controller, repeats this step；
Step (3)：The k moment will be denoted as current time；
Step (4)：Calculate { the gradient information collection } at k moment；
For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, there is step (4-1), step (4-2), step The processing of (4-3), step (4-4), step (4-5)：
The step (4-1) is：Actual value is exported with j-th of SISO system based on j-th of SISO systems output desired value, Function is calculated using j-th of SISO systematic error, j-th of SISO systematic error at k moment is calculated, is denoted as ej(k)；
The step (4-2) is：Local derviation information during { local derviation information j } is gathered, as the defeated of j-th BP neural network Enter；
The step (4-3) is：Based on the input of j-th of BP neural network described in step (4-2), j-th of BP nerve Network carries out forward calculation, and result of calculation is exported by the output layer of j-th of BP neural network, and it is inclined to obtain j-th of SISO Form Non-Model Controller treats the value of setting parameter；
The step (4-4) is：J-th of SISO systematic errors e obtained based on step (4-1)j(k), step (4- 3) the inclined form Non-Model Controllers of j-th of SISO obtained treat the value of setting parameter, using the inclined form model-free controls of SISO It is defeated in the control at k moment for controlled device that the inclined form Non-Model Controllers of j-th of SISO are calculated in the control algolithm of device processed Enter uj(k)；
The step (4-5) is：The control input u obtained based on step (4-4)j(k), the control input is calculated uj(k) it is described respectively for each gradient information for treating setting parameter at the k moment of the inclined form Non-Model Controllers of j-th of SISO The specific formula for calculation of gradient information is as follows：
When the inclined form Non-Model Controllers of j-th of SISO are treated to include penalty factor λ in setting parameterjWhen, the control System input uj(k) it is directed to the penalty factor λjIt is in the gradient information at k moment：
When the inclined form Non-Model Controllers of j-th of SISO are treated to include step factor ρ in setting parameterj,1When, the control System input uj(k) it is directed to the step factor ρj,1It is in the gradient information at k moment：
When the inclined form Non-Model Controllers of j-th of SISO are treated to include step factor ρ in setting parameterj,iAnd 2≤i≤ LjWhen, the control input uj(k) it is directed to the step factor ρj,iIt is in the gradient information at k moment：
Wherein, Δ uj(k)=uj(k)-uj(k-1),It is the inclined form Non-Model Controllers of j-th of SISO at the k moment Pseudo- gradient estimate,ForI-th of component (i=1 ..., Lj)；
The set of the above-mentioned whole gradient information is denoted as { gradient information j }, is put into set { gradient information collection }；
Gradient information in { gradient information j } set by described in is sequentially denoted as { the local derviation information j } collection of previous moment Local derviation information in conjunction, i.e.,：When the inclined form Non-Model Controllers of j-th of SISO are treated to include penalty factor λ in setting parameterj Gradient information in { gradient information j } set described in Shi ZeIt is denoted as in { the local derviation information j } set of previous moment Local derviation informationWhen the inclined form Non-Model Controllers of j-th of SISO are treated to include step factor in setting parameter ρj,iAnd 1≤i≤LjGradient information in { gradient information j } set described in Shi ZeIt is denoted as { the local derviation of previous moment Information j } set in local derviation information
If m=1, { the gradient information collection } is constant, subsequently into step (5)；
If m >=2, make the inclined form Non-Model Controllers of each SISO in the inclined form Non-Model Controllers of m SISO It is respectively provided with step (4-1), step (4-2), step (4-3), step (4-4), the processing procedure of step (4-5)；When m SISO is inclined The inclined form Non-Model Controllers of each SISO in form Non-Model Controller perform step (4-1), step completely The processing of (4-2), step (4-3), step (4-4), step (4-5), then described { gradient information collection } include all { { gradient letters Breath 1 ..., { gradient information m } } set, subsequently into step (5)；
Step (5)：For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, with the value of system error function most It is small to turn to target, using gradient descent method, with reference to { the gradient information collection } that the step (4) obtains, carry out systematic error Backpropagation calculates, and updates hidden layer weight coefficient, the output layer weight coefficient of j-th of BP neural network, as j-th of later moment in time Hidden layer weight coefficient, output layer weight coefficient during BP neural network progress forward calculation, it is at the same time synchronous if m >=2 to realize The decoupling of the inclined form Non-Model Controllers of j-th of SISO and the inclined form Non-Model Controllers of other m-1 SISO；
If m=1, enter step (6)；
If m >=2, for the inclined form Non-Model Controllers of other m-1 SISO, this step is repeated, until complete Hidden layer weight coefficient, the output layer weight coefficient of m, portion BP neural network are all updated, subsequently into step (6)；
Step (6)：The whole m control input { u1(k),…,um(k) } after acting on controlled device, controlled pair is obtained As whole m SISO systems output actual value in later moment in time, back to step (3), repeat step (3) and arrive step (6).
While using above-mentioned technical proposal, the present invention can also be used or combined using technology further below Scheme：
It, can be in step (4-1), step (4-2), step (4-3), step (4-4), step if m >=2 in step (4) Suddenly between any one step of (4-5) or any two step or before step (4-1) or step (4-5) changes the value of j afterwards, It carries out the processing procedure of the inclined form Non-Model Controllers of other SISO, can be sequentially value or nothing for the change of j numerical value Regularly value, as long as making the processing procedure for the inclined form Non-Model Controllers of each SISO, sequencing is step (4- 1), step (4-2), step (4-3), step (4-4), step (4-5), and the two of the inclined form Non-Model Controllers of each SISO Between a step or before step (4-1) or after step (4-5), the inclined form model-free controls of other SISO can be performed as needed The step of device processed (4-1), step (4-2), step (4-3), step (4-4), step (4-5) processing.
J-th of SISO systematic errors in the step (4-1) calculate argument of function and include j-th of SISO system System output desired value exports actual value with j-th of SISO system.
J-th of SISO systematic errors in the step (4-1) calculate function and use WhereinDesired value, y are exported for j-th of SISO system that the k moment setsj(k) j-th of the SISO obtained for k instance samples System exports actual value；Or it usesWhereinFor j-th of SISO at k+1 moment System exports desired value, yj(k) j-th of the SISO system obtained for k instance samples exports actual value.
The independent variable of the system error function in the step (5) includes m SISO systematic error, m SISO system System output desired value, m SISO system output actual value one of arbitrarily or any number of combination.
The system error function in the step (5) isWherein, ej(k) it is the J SISO systematic error, Δ uj(k)=uj(k)-uj(k-1), ajWith bjTo be greater than or equal to 0 constant, 1≤j≤m.
Decoupling control methods of the MIMO provided by the invention based on the inclined form Non-Model Controllers of SISO Yu local derviation information, can It solves the problems, such as to solve online between the inclined form Non-Model Controller online self-tuning parameters of SISO and multiple SISO systems with synchronous The problem of coupling, so as to fulfill the decoupling control of mimo system.
Description of the drawings
Fig. 1 is the principle of the present invention block diagram；
Fig. 2 is j-th of BP neural network structure diagram that the present invention uses；
Fig. 3 is two inputs, two output mimo system in penalty factor λjWith step factor ρj,1,ρj,2,ρj,3Self-tuning System simultaneously When the 1st SISO system control effect figure；
Fig. 4 is two inputs, two output mimo system in penalty factor λjWith step factor ρj,1,ρj,2,ρj,3Self-tuning System simultaneously When the 2nd SISO system control effect figure；
Fig. 5 is two inputs, two output mimo system in penalty factor λjWith step factor ρj,1,ρj,2,ρj,3Self-tuning System simultaneously When control input figure；
Fig. 6 is two inputs, two output mimo system in penalty factor λjWith step factor ρj,1,ρj,2,ρj,3Self-tuning System simultaneously When penalty factor λjChange curve；
Fig. 7 is two inputs, two output mimo system in penalty factor λjWith step factor ρj,1,ρj,2,ρj,3Self-tuning System simultaneously When step factor ρ1,1,ρ1,2,ρ1,3Change curve；
Fig. 8 is two inputs, two output mimo system in penalty factor λjWith step factor ρj,1,ρj,2,ρj,3Self-tuning System simultaneously When step factor ρ2,1,ρ2,2,ρ2,3Change curve；
Fig. 9 is two inputs, two output mimo system in penalty factor λjIt fixes and step factor ρj,1,ρj,2,ρj,3Self-tuning System When the 1st SISO system control effect figure；
Figure 10 is two inputs, two output mimo system in penalty factor λjIt fixes and step factor ρj,1,ρj,2,ρj,3Self-tuning System When the 2nd SISO system control effect figure；
Figure 11 is two inputs, two output mimo system in penalty factor λjIt fixes and step factor ρj,1,ρj,2,ρj,3Self-tuning System When control input figure；
Figure 12 is two inputs, two output mimo system in penalty factor λjIt fixes and step factor ρj,1,ρj,2,ρj,3Self-tuning System When step factor ρ1,1,ρ1,2,ρ1,3Change curve；
Figure 13 is two inputs, two output mimo system in penalty factor λjIt fixes and step factor ρj,1,ρj,2,ρj,3Self-tuning System When step factor ρ2,1,ρ2,2,ρ2,3Change curve.
Specific embodiment
The present invention is further described in the following with reference to the drawings and specific embodiments.
Fig. 1 gives the principle of the present invention block diagram.According to the coupling feature of mimo system and tendentiousness feature, one is chosen A input and an output, form a SISO system, Repeated m time, intercouple so as to which mimo system is resolved into m SISO systems, while the input of wherein one of arbitrary SISO systems is all not as the input of other SISO systems, it is one of arbitrary The output of SISO systems is all not as the output of other SISO systems；M SISO system uses the inclined form model-free controls of m SISO Device processed is controlled.
For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, determine that its control input linearizes length constant Lj, LjTo be more than 1 integer；The inclined form Non-Model Controller parameters of j-th of SISO include penalty factor λjAnd step factorDetermine that the inclined form Non-Model Controllers of j-th of SISO treat setting parameter, the inclined forms of j-th of SISO are without mould Type controller treats setting parameter, is the part or all of of the inclined form Non-Model Controller parameters of j-th of SISO, includes punishment Factor lambdajAnd step factorOne of arbitrary or any number of combination；In Fig. 1, the inclined forms of j-th of SISO are without mould Type controller treats that setting parameter is penalty factor λjAnd step factorDetermine the input of j-th of BP neural network Node layer number, node in hidden layer, output layer number of nodes, the output layer number of nodes are no less than the inclined forms of j-th of SISO Non-Model Controller treats setting parameter number；Initialize hidden layer weight coefficient, the output layer weight coefficient of BP neural network；Initialization Local derviation information in { local derviation information j } set.For the inclined form Non-Model Controllers of other m-1 SISO, this section is repeated Fall the work.
The k moment will be denoted as current time.
For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, it is actual that system output is obtained by sampling first Value yj(k), system is exported into desired valueWith system output actual value yj(k) j-th SISO system of the difference as the k moment Error ej(k)；Then the local derviation information during { local derviation information j } is gathered, the input as j-th of BP neural network；J-th of BP Neutral net carries out forward calculation, and result of calculation is exported by the output layer of j-th of BP neural network, obtained described j-th The inclined form Non-Model Controllers of SISO treat the value of setting parameter；Then, based on j-th of SISO systematic errors ej(k), j-th The inclined form Non-Model Controllers of SISO treat the value of setting parameter, using the control algolithm of the inclined form Non-Model Controllers of SISO, meter Calculation obtains the inclined form Non-Model Controllers of j-th of SISO and is directed to control input u of the controlled device at the k momentj(k)；Calculate control Input uj(k) each gradient information for treating setting parameter at the k moment of the inclined form Non-Model Controllers of j-th of SISO is directed to respectively, All the set of the gradient information is denoted as { gradient information j }, is put into set { gradient information collection }, while { gradient is believed by described in Breath j set in gradient information be sequentially denoted as previous moment { the local derviation information j } set in local derviation information.For it The inclined form Non-Model Controllers of his m-1 SISO, repeat the work described in this paragraph, include { the gradient information collection } All set of { { gradient information 1 } ..., { gradient information m } }.
Target is minimised as with the value of system error function, is contributed in Fig. 1 with considering whole m SISO systematic errors System error functionValue be minimised as target, using gradient descent method, with reference to { the gradient information collection }, into The backpropagation of row systematic error calculates, and hidden layer weight coefficient, the output layer weight coefficient of j-th of BP neural network is updated, as rear Hidden layer weight coefficient, output layer weight coefficient during one j-th of moment BP neural network progress forward calculation, and synchronously realize jth The decoupling of a inclined form Non-Model Controllers of SISO and the inclined form Non-Model Controllers of other m-1 SISO.For other m-1 The inclined form Non-Model Controllers of SISO, repeat the work described in this paragraph, until the hidden layer of whole m BP neural network Weight coefficient, output layer weight coefficient are all updated.
The whole m control input { u1(k),…,um(k) } after acting on controlled device, controlled device is obtained latter Whole m SISO systems output actual value at moment, back to foregoing【The k moment will be denoted as current time】Paragraph, after starting Decoupling control processes of the MIMO at one moment based on the inclined form Non-Model Controllers of SISO Yu local derviation information.
Fig. 2 gives j-th of (1≤j≤m) BP neural network structure diagram that the present invention uses.J-th of BP nerve net The structure that hidden layer is individual layer may be employed in network, can also use structure of the hidden layer for multilayer.In the schematic diagram of Fig. 2, it is For the sake of simplicity, j-th of BP neural network employs the structure that hidden layer is individual layer, that is, uses by input layer, individual layer hidden layer, defeated Go out the Three Tiered Network Architecture of layer composition, input layer number is set to treat that setting parameter number (treats that setting parameter number is L in Fig. 2j + 1), node in hidden layer 6, output layer number of nodes is set to treat that setting parameter number (treats that setting parameter number is L in Fig. 2j+ 1).The node of input layer, the local derviation information in gathering with { local derviation information j } It corresponds to respectively.The node of output layer, the penalty factor λ with the inclined form Non-Model Controllers of j-th of SISOjWith Step factorIt corresponds to respectively.The update of the hidden layer weight coefficient, output layer weight coefficient of j-th of BP neural network Process is specially：Target is minimised as with the value of system error function, to consider whole m SISO systematic errors in Fig. 2 The system error function of contributionValue be minimised as target, using gradient descent method, with reference to { the gradient information Collection }, systematic error backpropagation calculating is carried out, updates hidden layer weight coefficient, the output layer weight coefficient of j-th of BP neural network, Hidden layer weight coefficient, output layer weight coefficient during as j-th of BP neural network progress forward calculation of later moment in time, and it is synchronous real The decoupling of the existing inclined form Non-Model Controllers of j-th of SISO and the inclined form Non-Model Controllers of other m-1 SISO.
It is the specific embodiment of the present invention below.
Controlled device exports mimo system for two inputs two of typical non linear：
y1(k)=x11(k)
y2(k)=x21(k)
Wherein, a (k)=1+0.1sin (2k π/1500), b (k)=1+0.1cos (2k π/1500).
System output desired value y*(k) it is as follows：
In this embodiment, according to the coupling feature of the mimo system and tendentiousness feature, by mimo system point Solution is into the 2 SISO systems (m=2) to intercouple：The input of 1st SISO system is u1(k), export as y1(k)；2nd The input of SISO systems is u2(k), export as y2(k)。
2 SISO systems are controlled using the inclined form Non-Model Controllers of 2 SISO.The inclined form Model free controls of SISO The control input linearisation length constant L of devicejComplexity and actual control effect of the numerical value generally according to controlled device It is set, too small to influence control effect generally between 1 to 10, conference excessively causes computationally intensive, so generally often taking 3 Or 5.In this embodiment, the control input linearisation length constant L of the inclined form Non-Model Controllers of 2 SISOjAll take For 3, i.e. L1=L2=3.
1st BP neural network and the 2nd BP neural network are used to be made of input layer, individual layer hidden layer, output layer Three Tiered Network Architecture, what input layer number was set to respective controller treats setting parameter number, and node in hidden layer is all provided with For 6, what output layer number of nodes was set to respective controller treats setting parameter number.
For above-mentioned specific embodiment, two groups of verification experimental verifications have been carried out altogether.
During first group of verification experimental verification, the input layer number of BP neural network is set to 4 with output layer number of nodes in Fig. 2 It is a, to the penalty factor λ of the inclined form Non-Model Controllers of 2 SISOjWith step factor ρj,1,ρj,2,ρj,3It carries out simultaneously from whole Fixed, Fig. 3 is the control effect figure of the 1st SISO system, and Fig. 4 is the control effect figure of the 2nd SISO system, and Fig. 5 is defeated in order to control Enter figure, Fig. 6 is penalty factor λjChange curve, Fig. 7 are step factor ρ1,1,ρ1,2,ρ1,3Change curve, Fig. 8 are step factor ρ2,1,ρ2,2,ρ2,3Change curve.The result shows that method of the invention by design the inclined form Non-Model Controllers of 2 SISO, and With reference to BP neural network to the penalty factor λ of the inclined form Non-Model Controllers of each SISOjWith step factor ρj,1,ρj,2,ρj,3Into Row while Self-tuning System, can realize good control effect, and realize the decoupling control of mimo system.
During second group of verification experimental verification, the input layer number of BP neural network is set to 3 with output layer number of nodes in Fig. 2 It is a, first by 2 penalty factor λjPenalty factor λ when value is first group of verification experimental verification is fixed respectivelyjAverage value (j=1, 2), then to the step factor ρ of the inclined form Non-Model Controllers of 2 SISOj,1,ρj,2,ρj,3Self-tuning System is carried out, Fig. 9 is the 1st The control effect figure of SISO systems, Figure 10 are the control effect figure of the 2nd SISO system, and Figure 11 inputs figure, Tu12Wei in order to control Step factor ρ1,1,ρ1,2,ρ1,3Change curve, Figure 13 are step factor ρ2,1,ρ2,2,ρ2,3Change curve.The result shows that the present invention Method in penalty factor λjBy designing the inclined form Non-Model Controllers of 2 SISO when fixed, and combine BP neural network pair The step factor ρ of the inclined form Non-Model Controllers of each SISOj,1,ρj,2,ρj,3Self-tuning System is carried out, can realize good control Effect, and realize the decoupling control of mimo system.
J-th of (1≤j≤m) SISO systems output should it is expected it is emphasized that in above-mentioned specific embodiment ValueWith j-th of SISO systems output actual value yj(k) difference is as j-th of SISO systematic errors ej(k), that is, Only described j-th of SISO systematic errors calculate a kind of method in function；It can also be by k+1 J-th of SISO systems output desired value of momentWith j-th of SISO systems output actual value y of k momentj(k) difference conduct J-th of SISO systematic errors ej(k), that is,J-th of SISO systematic errors calculate Function can also use independent variable include j-th SISO system export desired value and j-th of SISO system export actual value its Its computational methods, for example,To the controlled device of above-mentioned specific embodiment and Speech calculates function using above-mentioned different systematic error, can realize good control effect.
More mesh should be minimised as with the value of system error function it is emphasized that in above-mentioned specific embodiment When marking hidden layer weight coefficient, the output layer weight coefficient to update j-th of BP neural network, the system error function employs comprehensive Close the system error function for considering whole m SISO systematic error contributionsOne in only described system error function Kind function；The system error function can also use independent variable to include m SISO systematic error, m SISO systems output phase Prestige value, m SISO system output actual value one of arbitrary or any number of combination other functions, for example, the system Error function usesOrNamely useAnother letter Number form formula；Again for example, the system error function usesWherein, ej(k) it is j-th SISO systematic errors, Δ uj(k)=uj(k)-uj(k-1), ajWith bjTo be greater than or equal to 0 constant, 1≤j≤m；Obviously, b is worked asj During equal to 0, the system error function only accounts forContribution, show minimize target be systematic error minimum, Exactly pursue precision height；And work as bjDuring more than 0, the system error function considers simultaneouslyContribution andTribute It offers, shows the target minimized while pursuit systematic error is small, also the variation of pursuit control input is small, that is, both pursues essence The high pursuit again of degree manipulates steady.For the controlled device of above-mentioned specific embodiment, using above-mentioned different system error function, all It can realize good control effect；Only consider with system error functionControl effect during contribution is compared, and is missed in system Difference function considers simultaneouslyContribution andContribution when its control accuracy slightly reduce and its manipulate stationarity then have It improves.
It in addition should be it is emphasized that the step (4-1) can both be adopted to the execution method of the step (4-5) After the inclined form Non-Model Controllers of a SISO is waited to be finished from the step (4-1) to the step (4-5), then open Begin to perform method of the inclined form Non-Model Controllers of another SISO from the step (4-1) to the step (4-5)；It can also The steps (4-1) are performed to all from whole using waiting more than one and being less than a inclined form Non-Model Controllers of SISO of m After performing the step (4-5), then start to perform the inclined form Non-Model Controllers of other SISO from the step (4-1) to The method of the step (4-5)；The inclined form Non-Model Controllers of m SISO of whole can also be used from all execution steps (4-1) arrives the methods for all performing the steps (4-5).
It finally should be it is emphasized that the inclined form Non-Model Controllers of (1≤j≤m) SISO j-th described be waited to adjust ginseng Number, includes penalty factor λjAnd step factorOne of arbitrary or any number of combination；In above-mentioned specific embodiment In, penalty factor λ during first group of verification experimental verificationjWith step factor ρj,1,ρj,2,ρj,3It realizes while Self-tuning System, second group of experiment Penalty factor λ during verificationjIt fixes and step factor ρj,1,ρj,2,ρj,3Realize Self-tuning System；It in practical application, can also basis Any number of combination of setting parameter is treated in concrete condition, selection, for example, step factor ρj,1,ρj,2It fixes and penalty factor λj With step factor ρj,3Realize Self-tuning System；In addition, the inclined form Non-Model Controllers of SISO treat setting parameter, include but not limited to punish Penalty factor λjAnd step factorFor example, as the case may be, pseudo- gradient estimate can also be includedEtc. parameters.
Above-mentioned specific embodiment is used for illustrating the present invention, is merely a preferred embodiment of the present invention rather than to this Invention is limited, and in the protection domain of spirit and claims of the present invention, to any modification of the invention made, is equal Replace, improve etc., both fall within protection scope of the present invention.

Claims (5)

  1. Decoupling control methods of the 1.MIMO based on the inclined form Non-Model Controllers of SISO Yu local derviation information, it is characterised in that including with Lower step：
    Step (1)：For with mi input (mi is the integer more than or equal to 2), (mo is more than or equal to 2 with mo output Integer) MIMO (Multiple Input and Multiple Output, multiple-input and multiple-output) system, it is defeated to choose mi The input entered and an output in mo output, form a SISO (Single Input and Single Output, single-input single-output) system；Repeated m time (m >=1 and m≤mi and m≤mo and m is integer), forms m SISO system System, wherein the input of one of arbitrary SISO systems is all not as the input of other SISO systems, one of arbitrary SISO systems it is defeated Go out all not as the output of other SISO systems；The m SISO systems are carried out using the inclined form Non-Model Controllers of m SISO Control；
    Step (2)：For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, determine that its control input linearizes length Constant Lj, LjTo be more than 1 integer；The inclined form Non-Model Controller parameters of j-th of SISO include penalty factor λjAnd step-length The factorDetermine that the inclined form Non-Model Controllers of j-th of SISO treat setting parameter, j-th of SISO is inclined Form Non-Model Controller treats setting parameter, is the part or all of of the inclined form Non-Model Controller parameters of j-th of SISO, Include penalty factor λjAnd step factorOne of arbitrary or any number of combination；Determine j-th of BP neural network Input layer number, node in hidden layer, output layer number of nodes, the output layer number of nodes is no less than j-th of SISO Inclined form Non-Model Controller treats setting parameter number；Initialize hidden layer weight coefficient, the output layer power of j-th of BP neural network Coefficient；Initialize the local derviation information in { local derviation information j } set；If m >=2, for other inclined forms of m-1 SISO without mould Type controller repeats this step；
    Step (3)：The k moment will be denoted as current time；
    Step (4)：Calculate { the gradient information collection } at k moment；
    For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, there is step (4-1), step (4-2), step (4- 3), the processing of step (4-4), step (4-5)：
    The step (4-1) is：Actual value is exported with j-th of SISO system based on j-th of SISO systems output desired value, is used J-th of SISO systematic error calculates function, and j-th of SISO systematic error at k moment is calculated, is denoted as ej(k)；
    The step (4-2) is：Local derviation information during { local derviation information j } is gathered, the input as j-th of BP neural network；
    The step (4-3) is：Based on the input of j-th of BP neural network described in step (4-2), j-th of BP neural network Forward calculation is carried out, result of calculation is exported by the output layer of j-th of BP neural network, obtains the inclined forms of j-th of SISO Non-Model Controller treats the value of setting parameter；
    The step (4-4) is：J-th of SISO systematic errors e obtained based on step (4-1)j(k), step (4-3) obtains To the inclined form Non-Model Controllers of j-th of SISO treat the value of setting parameter, using the inclined form Non-Model Controllers of SISO Control algolithm, be calculated the inclined form Non-Model Controllers of j-th of SISO for controlled device the k moment control input uj (k)；
    The step (4-5) is：The control input u obtained based on step (4-4)j(k), the control input u is calculatedj(k) Respectively for each gradient information for treating setting parameter at the k moment of the inclined form Non-Model Controllers of j-th of SISO, the gradient letter The specific formula for calculation of breath is as follows：
    When the inclined form Non-Model Controllers of j-th of SISO are treated to include penalty factor λ in setting parameterjWhen, the control input uj(k) it is directed to the penalty factor λjIt is in the gradient information at k moment：
    When the inclined form Non-Model Controllers of j-th of SISO are treated to include step factor ρ in setting parameterj,1When, the control is defeated Enter uj(k) it is directed to the step factor ρj,1It is in the gradient information at k moment：
    When the inclined form Non-Model Controllers of j-th of SISO are treated to include step factor ρ in setting parameterj,iAnd 2≤i≤LjWhen, The control input uj(k) it is directed to the step factor ρj,iIt is in the gradient information at k moment：
    Wherein, Δ uj(k)=uj(k)-uj(k-1),It is terraced in the puppet at k moment for the inclined form Non-Model Controllers of j-th of SISO Spend estimate,I-th of component (i=1 ..., Lj)；
    The set of the above-mentioned whole gradient information is denoted as { gradient information j }, is put into set { gradient information collection }；
    Gradient information in { gradient information j } set by described in is sequentially denoted as in { the local derviation information j } set of previous moment Local derviation information, i.e.,：When the inclined form Non-Model Controllers of j-th of SISO are treated to include penalty factor λ in setting parameterjShi Ze Gradient information in { the gradient information j } setIt is denoted as inclined in { the local derviation information j } set of previous moment Lead informationWhen the inclined form Non-Model Controllers of j-th of SISO are treated to include step factor ρ in setting parameterj,i And 1≤i≤LjGradient information in { gradient information j } set described in Shi ZeIt is denoted as { the local derviation letter of previous moment Cease j set in local derviation information
    If m=1, { the gradient information collection } is constant, subsequently into step (5)；
    If m >=2, the inclined form Non-Model Controllers of each SISO in the inclined form Non-Model Controllers of m SISO is made to have There are step (4-1), step (4-2), step (4-3), step (4-4), the processing procedure of step (4-5)；When the inclined forms of m SISO The inclined form Non-Model Controllers of each SISO in Non-Model Controller perform step (4-1), step (4- completely 2), the processing of step (4-3), step (4-4), step (4-5), then described { gradient information collection } include all { { gradient informations 1 } ..., { gradient information m } } set, subsequently into step (5)；
    Step (5)：For j-th of inclined form Non-Model Controller of (1≤j≤m) SISO, minimized with the value of system error function For target, using gradient descent method, with reference to { the gradient information collection } that the step (4) obtains, it is reversed to carry out systematic error It propagates and calculates, update hidden layer weight coefficient, the output layer weight coefficient of j-th of BP neural network, as j-th of BP god of later moment in time Hidden layer weight coefficient, output layer weight coefficient during through network progress forward calculation, it is at the same time synchronous if m >=2 to realize j-th The decoupling of the inclined form Non-Model Controllers of SISO and the inclined form Non-Model Controllers of other m-1 SISO；
    If m=1, enter step (6)；
    If m >=2, for the inclined form Non-Model Controllers of other m-1 SISO, this step is repeated, until whole m Hidden layer weight coefficient, the output layer weight coefficient of BP neural network are all updated, subsequently into step (6)；
    Step (6)：The whole m control input { u1(k),…,um(k) } after acting on controlled device, obtain controlled device and exist Whole m SISO systems output actual value of later moment in time, back to step (3), repeats step (3) and arrives step (6).
  2. 2. decoupling control sides of the MIMO according to claim 1 based on the inclined form Non-Model Controllers of SISO Yu local derviation information Method, which is characterized in that j-th of SISO systematic errors in the step (4-1) calculate argument of function and include j-th SISO systems export desired value and export actual value with j-th of SISO system.
  3. 3. decoupling controls of the MIMO according to claim 1 or 2 based on the inclined form Non-Model Controllers of SISO Yu local derviation information Method processed, which is characterized in that j-th of SISO systematic errors in the step (4-1) calculate function and useWhereinDesired value, y are exported for j-th of SISO system that the k moment setsj(k) it is the k moment Sample obtained j-th of SISO system output actual value；Or it usesWhereinFor J-th of SISO system output desired value at k+1 moment, yj(k) j-th of the SISO system output reality obtained for k instance samples Value.
  4. 4. decoupling control sides of the MIMO according to claim 1 based on the inclined form Non-Model Controllers of SISO Yu local derviation information Method, which is characterized in that the independent variable of the system error function in the step (5) includes m SISO systematic error, m SISO systems output desired value, m SISO system output actual value one of arbitrarily or any number of combination.
  5. 5. decoupling controls of the MIMO according to claim 1 or 4 based on the inclined form Non-Model Controllers of SISO Yu local derviation information Method processed, which is characterized in that the system error function in the step (5) isIts In, ej(k) it is j-th of SISO systematic error, Δ uj(k)=uj(k)-uj(k-1), ajWith bjTo be greater than or equal to 0 constant, 1 ≤j≤m。
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