CN110095982A - The control method of load characteristic automatic identification method, state-space model and power supply - Google Patents
The control method of load characteristic automatic identification method, state-space model and power supply Download PDFInfo
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Abstract
The present invention relates to identification field, the control methods of specially a kind of load characteristic automatic identification method, state-space model and power supply, wherein embedded load characteristic automatic identification method includes: using the unit module with load characteristic as being identified object；Identification by being identified object exports yID, identification input uIDConstruct adaptive state-space model；The adaptive state-space model is suitable for inputting its identification according to the variation adjustment for being identified object.It realizes and constructs adaptive state-space model automatically according to the input-output characteristic for being identified object.
Description
Technical field
The present invention relates to identification field, specially a kind of load characteristic automatic identification method, state-space model and power supplys
Control method.
Background technique
Nowadays, it requires to be set according to the different sampling periods in many fields, it is automatic to recognize external control target
Input-output characteristic, and its state-space model is exported automatically, to complete the detection demand of the control object in different industries.
Based on above-mentioned technical problem, need to design a kind of new load characteristic automatic identification method, state-space model and
The control method of power supply.
Summary of the invention
The object of the present invention is to provide the controlling parties of a kind of load characteristic automatic identification method, state-space model and power supply
Method.
In order to solve the above-mentioned technical problems, the present invention provides a kind of embedded load characteristic automatic identification methods, comprising:
Using the unit module with load characteristic as being identified object；
Identification by being identified object exports yID, identification input uIDConstruct adaptive state-space model；
The adaptive state-space model is suitable for inputting its identification according to the variation adjustment for being identified object.
Further, the embedded load characteristic automatic identification method further include:
Identification is inputted into uIDY is exported with corresponding identificationIDIt is recorded as a pair of the adaptive state-space model
I/O data pair；And
U is inputted by identificationIDY is exported with identificationIDThe method for constructing adaptive state-space model includes:
Adaptive state-space model order i is estimated according to the number s of I/O data pair calculating, when constructing Hankel matrix
Used matrix columns j, and the value of used matrix line number N when Hankel matrix is constructed, to construct Hankel matrix,
Wherein
Adaptive state-space model is set with the input of m dimension and l dimension output, and corresponding identification inputsSetting identification output
[0, s-1] k ∈ in formula, s are greater than 5 times of m × i, and m is the dimension of input signal；L is the dimension of output signal；K is
Discrete-time variable；For real number field.
Further, identification input u is constructed according to the value of i, j and NIDY is exported with identificationIDHankel matrix, i.e.,
In above-mentioned Hankel matrix, U0,i,NFor the input Hankel matrix before the i-th moment；Ui,j,NFor at i-th
It is carved into the input Hankel matrix at jth moment；Y0,i,NFor the output Hankel matrix before the i-th moment；Yi,j,NFor i-th
The output Hankel matrix at moment to jth moment.
Further, by U0,i,N、Ui,j,N、Y0,i,NAnd Yi,j,NLQ decomposition is carried out, makees rectangular projection to be obtained from adaptive state space
The considerable matrix Γ of model extensionj, i.e.,
In formula, Wherein
L11~L44Reflect U0,i,NRow vector at space, Ui,j,NRow vector arrive Y respectively at space0,i,NRow
Vector is at space, Yi,j,NRow vector at space projection components.
Further, to L42And L43Matrix singular value decomposition is carried out, i.e.,
In formula, U is [L42 L43] the premultiplication matrix that is obtained through matrix singular value decomposition of matrix；
V is [L42 L43] matrix through the right side that matrix singular value decomposition obtains multiplies matrix；
Σ is the eigenvalue matrix of adaptive state-space model；
T is transposition；
Σ1For the adaptive state-space model nonzero eigenvalue for being identified object；
Σ2For null matrix；
U1,U2For according to Σ1Number to U carry out piecemeal, U1Columns be equal to Σ1Line number, U2Columns be equal to Σ2's
Line number；And
V1 T,For according to Σ1Number to VTCarry out piecemeal, V1 TLine number be equal to Σ1Columns,Line number be equal to
Σ2Columns.
Further, U is taken1N, left side column vector be denoted as Un, UnUpper l (j-1) row be denoted as Up, lower l (j-1) is capable to be denoted as Uq,
Then adaptive state-space model matrixC is UnUpper l row；N is the practical order obtained by i.
Further, by least square solution overdetermined equation, with obtained from adaptive state spatial model matrix A, B, C, D and
Original state x0, and construct the adaptive state-space model for being identified object, i.e.,
The overdetermined equation are as follows:
In formula: Y0,s,1For by yIDNumerical value write as the form of single-row vector；ΓsSystem to be identified object extends considerable
Matrix；Φ, Ψ, Ω, Ξ are intermediate variable；
Y0,s,1For the column tandem of all identification output datas；
For Kronecker product；
IlUnit matrix is tieed up for l；
Vec () is by the rectangular array tandem in bracket；τ is cumulative fortune
Subfix used in calculation；
The adaptive state-space model are as follows:
xk+1=Axk+Buk
yk=Cxk+Duk；
In formula, xk+1For the state variable forecast for being identified object of the subsequent time at current k moment；xkTo be identified pair
As the state variable estimate at k moment；ukFor the input signal for being identified the object k moment；ykTo be identified the defeated of object k moment
Signal prediction out.
Further, the embedded load characteristic automatic identification method further include: verifying adaptive state-space model is
It is no accurate, i.e.,
It is identified object and is receiving verifying identification input utestVerifying identification output y is generated afterwardstest, and using adaptively
State-space model recognizes input u according to verifyingtestIt obtains output signal and forecasts ypre, work as ytsetAnd ypreJudge when identical adaptive
Answer state-space model accurate.
Another aspect, the present invention also provides a kind of adaptive state-space model recognized automatically for load characteristic, packets
It includes:
xk+1=Axk+Buk
yk=Cxk+Duk；
In formula, xk+1The state variable forecast of object is identified for the subsequent time at current k moment；xkTo be identified object k
The state variable estimate at moment；ukFor the input signal for being identified the object k moment；ykFor the output letter for being identified the object k moment
Number forecast；A, B, C, D are adaptive state-space model matrix.
Further, by least square solution overdetermined equation, with obtained from adaptive state spatial model matrix A, B, C, D and
Original state x0, and construct the adaptive state-space model for being identified object, i.e.,
The overdetermined equation are as follows:
In formula: Y0,s,1For by yIDNumerical value write as the form of single-row vector；ΓsSystem to be identified object extends considerable
Matrix；Φ, Ψ, Ω, Ξ are intermediate variable；
Y0,s,1For the column tandem of all identification output datas；
For Kronecker product；
IlUnit matrix is tieed up for l；
Vec () is by the rectangular array tandem in bracket；τ is cumulative fortune
Subfix used in calculation；
The adaptive state-space model matrixC is UnUpper l row；
The UnTo take U1N, left side column vector；UnUpper l (j-1) row be denoted as Up, lower l (j-1) is capable to be denoted as Uq, n is logical
Cross the practical order of i acquisition；U1Columns be equal to Σ1Line number；Σ1For the adaptive state-space model spy for being identified object
Value indicative；Σ2For null matrix；I is to estimate adaptive state-space model order.
The third aspect, the present invention also provides a kind of control methods of switching mode digital power, comprising:
Power major loop and digital control board for controlling power major loop output voltage；
The digital control board is suitable for through above-mentioned embedded load characteristic automatic identification method according to load characteristic
Variation, which is adjusted, stablizes the output voltage.
The invention has the advantages that the present invention is using the unit module with load characteristic as being identified object；Pass through
It is identified the identification output y of objectID, identification input uIDConstruct adaptive state-space model；The adaptive state space mould
Type is suitable for inputting its identification according to the variation adjustment for being identified object, defeated automatically according to the input for being identified object to realize
Characteristic constructs adaptive state-space model out.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is the flow chart of embedded load characteristic automatic identification method of the present invention；
Fig. 2 (a) is the discrete figure of prediction error of the adaptive state-space model of resistive load of the present invention；
Fig. 2 (b) is the discrete figure of prediction error of resistance sense loaded self-adaptive state-space model of the present invention.
Specific embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with
Illustration illustrates basic structure of the invention, therefore it only shows the composition relevant to the invention.
Embodiment 1
Fig. 1 is the flow chart of embedded load characteristic automatic identification method of the present invention.
As shown in Figure 1, present embodiments providing a kind of embedded load characteristic automatic identification method, comprising: will have negative
Carry the unit module of characteristic as be identified object (it is described be identified object and can be, but not limited to be transmitter, filter unit and
Load)；Identification by being identified object exports yID, identification input uIDConstruct adaptive state-space model；It is described adaptive
State-space model is suitable for inputting its identification according to the variation adjustment for being identified object, realizes automatically according to being identified pair
The input-output characteristic of elephant constructs adaptive state-space model.
In the present embodiment, realize that pseudo-random sequence generator (PRNG) issues a series of configurable width by FPGA on piece
Value, the pseudo-random sequence of length and update cycle, the sequence are to input u as the identification for being identified objectID, it is identified object and exists
uIDEffect is lower to generate a series of responses, and being converted to control unit by spot sensor and ADC, (the processor control unit can
With but be not limited to using FPGA as control core, integrated SOPC soft-core processor) digital signal that is capable of handling recognizes output
yID；During identification process, control unit sending is synchronously written signal, and (memory can be, but not limited to use memory
FIFO type) the synchronous storage u under the signal functionIDAnd yIDValue, corresponded composition input/output (I/O) data pair
And wait the reading signal of on-chip bus；The strong randomness signal of long repetition period may be implemented in the introducing of PRNG, and data are more
New period (DUC), amplitude, length can be specified by identification command parameter；And the signal has big bandwidth, wide spectrum and intensity can
The characteristics of tune, the method recognized compared to system noise is used can achieve better identification effect；uIDAccording to default
Data length update when finishing, as identification process finish time；Hereafter on-chip processor (i.e. SOPC soft-core processor) exists
It is read under the instruction of processing out of chip device (the processing out of chip device can be, but not limited to using PC etc.) or automatically by on-chip bus
I/O data pair in memory, and deposit in memory, if can be configured as making there are the outer RAM of the FPGA piece of larger capacity on plate
Use the RAM as the memory of on-chip processor；After data enter memory, SOPC soft-core processor is suitable for reading I/O data pair, and
The adaptive state-space model of object is identified to building according to I/O data.
In the present embodiment, the embedded load characteristic automatic identification method further include: identification is inputted into uIDWith with its
Corresponding identification exports yIDIt is recorded as a pair of of I/O data pair of the adaptive state-space model；And pass through identification input
uIDY is exported with identificationIDThe method for constructing adaptive state-space model includes: number s (the identification order according to I/O data pair
Parameter) calculate that estimate adaptive state-space model order i (described to estimate adaptive state-space model order i by being identified
The load characteristic of object requires to determine with identification, greater than the Main Patterns number for being identified object, one ordered as identification
Parameter can be, but not limited to be appointed as slightly larger 10 rank of value), used matrix columns j when Hankel matrix is constructed, with
And the value of used matrix line number N when Hankel matrix is constructed, to construct Hankel matrix, wherein
Adaptive state-space model is set with the input of m dimension and l dimension output, and corresponding identification inputsSetting identification output
[0, s-1] k ∈ in formula, s are greater than 5 times of m × i, and m is the dimension of input signal；L is the dimension of output signal；K is
Discrete-time variable；For real number field.
In the present embodiment, identification input u is constructed according to the value of i, j and NIDY is exported with identificationIDHankel matrix, i.e.,
In above-mentioned Hankel matrix, U0,i,NFor the input of before the i-th moment (i.e. initial time to the i-th moment)
Hankel matrix is (by uIDValue arrange to obtain)；Ui,j,NIt is the input Hankel matrix at the i-th moment to jth moment (by uID's
Value arrangement obtains)；Y0,i,NFor before the i-th moment (i.e. initial time to the i-th moment) output Hankel matrix (by yID's
Value arrangement obtains)；Yi,j,NIt is the output Hankel matrix at the i-th moment to jth moment (by yIDValue arrange to obtain)；J > i,
The value of j is equal to the value of matrix columns j, and the value of i is equal to the value for estimating adaptive state-space model order i.
In the present embodiment, by U0,i,N、Ui,j,N、Y0,i,NAnd Yi,j,NLQ decomposition is carried out, makees rectangular projection to be obtained from and adapts to shape
The considerable matrix Γ of state space model extensionj, i.e.,
In formula, Wherein
L11~L44Reflect U0,i,NRow vector at space, Ui,j,NRow vector arrive Y respectively at space0,i,NRow
Vector is at space, Yi,j,NRow vector at space projection components；Q1~Q4To U0,i,N、Ui,j,N、Y0,i,NAnd Yi,j,NIt carries out
The Q matrix that LQ is decomposed is obtained by piecemeal.
In the present embodiment, to L42And L43Matrix singular value decomposition is carried out, i.e.,In formula, U is [L42 L43] matrix is through matrix singular value decomposition
Obtained premultiplication matrix；V is [L42 L43] matrix through the right side that matrix singular value decomposition obtains multiplies matrix；Σ is adaptive state
The eigenvalue matrix of spatial model；T is transposition；Σ1For the adaptive state-space model nonzero eigenvalue for being identified object；Σ2
For null matrix；U1,U2For according to Σ1Number to U carry out piecemeal, U1Columns be equal to Σ1Line number, U2Columns be equal to Σ2
Line number；And V1 T,For according to Σ1Number to VTCarry out piecemeal, V1 TLine number be equal to Σ1Columns,Line number etc.
In Σ2Columns.
In the present embodiment, U is taken1N, left side column vector be denoted as Un, UnUpper l (j-1) row be denoted as Up, lower l (j-1) row
It is denoted as Uq, then adaptive state-space model matrixC is UnUpper l row；N is the practical order obtained by i,
I.e. the value of i is reduced to be identified the practical order n that object shows under current sample frequency in calculating automatically.
In the present embodiment, by least square solution overdetermined equation, with obtained from adaptive state spatial model matrix A,
B, C, D and original state x0, and construct the adaptive state-space model for being identified object, i.e.,
The overdetermined equation are as follows:
In formula: Y0,s,1For by yIDNumerical value write as the form of single-row vector；ΓsSystem to be identified object extends considerable
Matrix；Φ, Ψ, Ω, Ξ are intermediate variable；
Y0,s,1For the column tandem of all identification output datas；
For Kronecker product；
IlUnit matrix is tieed up for l；
Vec () is by the rectangular array tandem in bracket；τ is cumulative fortune
Subfix used in calculation；
The adaptive state-space model are as follows:
xk+1=Axk+Buk
yk=Cxk+Duk；
In formula, xk+1For the state variable forecast for being identified object of the subsequent time at current k moment；xkTo be identified pair
As the state variable estimate at k moment；ukFor the input signal for being identified the object k moment；ykTo be identified the defeated of object k moment
Signal prediction out.
In the present embodiment, the embedded load characteristic automatic identification method further include: verify adaptive state space
Whether model is accurate, i.e.,
It is identified object and is receiving verifying identification input utestVerifying identification output y is generated afterwardstest, and using adaptively
State-space model recognizes input u according to verifyingtestIt obtains output signal and forecasts ypre, work as ytsetAnd ypreJudge when identical adaptive
Answer state-space model accurate；To guarantee to verify the accuracy of adaptive state-space model, uIDWith utestShould have it is biggish not
Correlation, adaptive state-space model will by with uIDVisibly different utestCalculate the output number for meeting I/O data pair
According to；The accuracy of adaptive state-space model can be verified under the conditions of most stringent of as test input using random number.
In the present embodiment, the embedded load characteristic automatic identification method can be, but not limited to be programmed in by C++
In soft-core processor (i.e. SOPC soft-core processor).
Embodiment 2
On the basis of embodiment 1, the present embodiment also provides a kind of adaptive state recognized automatically for load characteristic
Spatial model, comprising:
xk+1=Axk+Buk
yk=Cxk+Duk；
In formula, xk+1The state variable forecast of object is identified for the subsequent time at current k moment；xkTo be identified object k
The state variable estimate at moment；ukFor the input signal for being identified the object k moment；ykFor the output letter for being identified the object k moment
Number forecast；A, B, C, D are adaptive state-space model matrix.
In the present embodiment, by least square solution overdetermined equation, with obtained from adaptive state spatial model matrix A,
B, C, D and original state x0, and construct the adaptive state-space model for being identified object, i.e.,
The overdetermined equation are as follows:
In formula: Y0,s,1For by yIDNumerical value write as the form of single-row vector；ΓsSystem to be identified object extends considerable
Matrix；Φ, Ψ, Ω, Ξ are intermediate variable；
Y0,s,1For the column tandem of all identification output datas；
For Kronecker product；
IlUnit matrix is tieed up for l；
Vec () is by the rectangular array tandem in bracket；τ is cumulative fortune
Subfix used in calculation；
The adaptive state-space model matrixC is UnUpper l row；
The UnTo take U1N, left side column vector；UnUpper l (j-1) row be denoted as Up, lower l (j-1) is capable to be denoted as Uq, n is logical
Cross the practical order of i acquisition；U1Columns be equal to Σ1Line number；Σ1For the adaptive state-space model spy for being identified object
Value indicative；Σ2For null matrix；I is to estimate adaptive state-space model order.
Embodiment 3
The present embodiment also provides a kind of control method of switching mode digital power on the basis of embodiment 1 and embodiment 2,
It include: power major loop and the digital control board for controlling power major loop output voltage；The number control
Making sheet card is suitable for being become by embodiment 1 and embedded load characteristic automatic identification method as described in example 2 according to load characteristic
Change to adjust and stablizes the output voltage.
In the present embodiment, the digital control board includes the mainboard of one piece of integrated fpga chip, one piece of digital quantity I/O
Plate, one block of ADDA plate and one piece of power panel；The mainboard of fpga chip can be, but not limited to use with CycloneII EP2C70
FPGA is main control chip, realizes SOPC soft-core processor, Peripheral Interface, Avalon MM bus and the customized IP of SSDC user,
Common peripheral hardware such as 2MB SSRAM, EPCS64 (64Mb) series arrangement device is built on mainboard, for non-volatile memories
16MB flash storage, RJ45 network interface and RS232 local communication interface and the clamping of Santa Cruz standard extension
Mouthful；Digital quantity input and output diode, optocoupler and level conversion device are built on digital quantity I/O plate, are responsible for mainly controlling power supply
Braking makees and Collection；ADDA plate includes ADC and DAC and mating conditioning circuit, is responsible for believing the level of sensor
It number is sampled；Power panel mainly includes a mini-switch power source, is responsible for providing 24V regulated power supply.
It, will to verify working condition of the adaptive state-space model under actual switch type digital power and loading condition
Switching mode digital power is respectively connected to the load of four different characteristics, and adaptive state-space model testing procedure is as follows:
Identification input u is generated using PRNGID, pwm signal is converted to as control input, load is excited, output electricity
Stream sampling is as identification output yID, embedded load characteristic automatic identification method utilize uIDAnd yIDConstruct adaptive state space
Model；
Verifying identification input u is generated using PRNG againtest, and equally excitation load is verified identification output ytest；
Utilize adaptive state-space model and utestOutput signal forecast y is calculatedpre, and and ytestCompare to check
The accuracy of adaptive state-space model.
To guarantee to verify the accuracy of adaptive state-space model, uIDWith utestThere should be biggish irrelevance, it is adaptive
Answer state-space model will by with uIDVisibly different utestCalculate the output data for meeting I/O data pair；Using random
Number can verify the accuracy of adaptive state-space model as test input under the conditions of most stringent of.
Fig. 2 (a) is the discrete figure of prediction error of the adaptive state-space model of resistive load of the present invention.
Fig. 2 (b) is the discrete figure of prediction error of resistance sense loaded self-adaptive state-space model of the present invention.
As shown in Fig. 2 (a) and Fig. 2 (b), obtained respectively using 0.07 Ω resistive load and 0.23 Ω/4.5mH resistance inductive load
The test result arrived, when normalizing given range respectively ± 0.1 and ± 0.05, output signal is forecast under the conditions of resistive load
ypreOutput y is recognized with verifyingtestData covariance is 1.54 × 10-4(numerical value is mainly influenced by the error of original state),
Lower magnet load is 3.47 × 10-6, it is seen that the I/O characteristic of model and the practical controlled device goodness of fit are preferable.
In conclusion the present invention is using the unit module with load characteristic as being identified object；By being identified object
Identification export yID, identification input uIDConstruct adaptive state-space model；The adaptive state-space model is suitable for basis
The variation adjustment for being identified object inputs its identification, is constructed with realizing automatically according to the input-output characteristic for being identified object
Adaptive state-space model.
Taking the above-mentioned ideal embodiment according to the present invention as inspiration, through the above description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property range is not limited to the contents of the specification, it is necessary to which the technical scope thereof is determined according to the scope of the claim.
Claims (10)
1. a kind of embedded load characteristic automatic identification method characterized by comprising
Using the unit module with load characteristic as being identified object；
Identification by being identified object exports yID, identification input uIDConstruct adaptive state-space model；
The adaptive state-space model is suitable for inputting its identification according to the variation adjustment for being identified object.
2. embedded load characteristic automatic identification method as described in claim 1, which is characterized in that
The embedded load characteristic automatic identification method further include:
Identification is inputted into uIDY is exported with corresponding identificationIDIt is recorded as a pair of of I/O number of the adaptive state-space model
According to right；And
U is inputted by identificationIDY is exported with identificationIDThe method for constructing adaptive state-space model includes:
Adaptive state-space model order i is estimated according to the number s of I/O data pair calculating, is made when constructing Hankel matrix
Matrix columns j, and the value of used matrix line number N when Hankel matrix is constructed, to construct Hankel matrix, wherein
Adaptive state-space model is set with the input of m dimension and l dimension output, and corresponding identification inputsIf
Fixed identification output
[0, s-1] k ∈ in formula, s are greater than 5 times of m × i, and m is the dimension of input signal；L is the dimension of output signal；K is discrete
Time variable；For real number field.
3. embedded load characteristic automatic identification method as claimed in claim 2, which is characterized in that
Identification input u is constructed according to the value of i, j and NIDY is exported with identificationIDHankel matrix, i.e.,
In above-mentioned Hankel matrix, U0,i,NFor the input Hankel matrix before the i-th moment；Ui,j,NTo be arrived at the i-th moment
The input Hankel matrix at jth moment；Y0,i,NFor the output Hankel matrix before the i-th moment；Yi,j,NFor at the i-th moment
To the output Hankel matrix at jth moment.
4. embedded load characteristic automatic identification method as claimed in claim 3, which is characterized in that
By U0,i,N、Ui,j,N、Y0,i,NAnd Yi,j,NLQ decomposition is carried out, making rectangular projection can be obtained from the extension of adaptive state spatial model
See matrix Γj, i.e.,
In formula, Wherein
L11~L44Reflect U0,i,NRow vector at space, Ui,j,NRow vector arrive Y respectively at space0,i,NRow vector
It opens into space, Yi,j,NRow vector at space projection components.
5. embedded load characteristic automatic identification method as claimed in claim 4, which is characterized in that
To L42And L43Matrix singular value decomposition is carried out, i.e.,
In formula, U is [L42 L43] the premultiplication matrix that is obtained through matrix singular value decomposition of matrix；
V is [L42 L43] matrix through the right side that matrix singular value decomposition obtains multiplies matrix；
Σ is the eigenvalue matrix of adaptive state-space model；
T is transposition；
Σ1For the adaptive state-space model nonzero eigenvalue for being identified object；
Σ2For null matrix；
U1,U2For according to Σ1Number to U carry out piecemeal, U1Columns be equal to Σ1Line number, U2Columns be equal to Σ2Line number；
And
V1 T,For according to Σ1Number to VTCarry out piecemeal, V1 TLine number be equal to Σ1Columns,Line number be equal to Σ2's
Columns.
6. embedded load characteristic automatic identification method as claimed in claim 5, which is characterized in that
Take U1N, left side column vector be denoted as Un, UnUpper l (j-1) row be denoted as Up, lower l (j-1) is capable to be denoted as Uq, then adaptive shape
State space model matrixC is UnUpper l row；N is the practical order obtained by i.
7. embedded load characteristic automatic identification method as claimed in claim 6, which is characterized in that
By least square solution overdetermined equation, obtained from adaptive state spatial model matrix A, B, C, D and original state x0,
And construct the adaptive state-space model for being identified object, i.e.,
The overdetermined equation are as follows:
In formula: Y0,s,1For by yIDNumerical value write as the form of single-row vector；ΓsSystem to be identified object extends considerable square
Battle array；Φ, Ψ, Ω, Ξ are intermediate variable；
Y0,s,1For the column tandem of all identification output datas；
For Kronecker product；
IlUnit matrix is tieed up for l；
Vec () is by the rectangular array tandem in bracket；τ is to make in accumulating operation
Subfix；
The adaptive state-space model are as follows:
xk+1=Axk+Buk
yk=Cxk+Duk；
In formula, xk+1For the state variable forecast for being identified object of the subsequent time at current k moment；xkWhen to be identified object k
The state variable estimate at quarter；ukFor the input signal for being identified the object k moment；ykFor the output signal for being identified the object k moment
Forecast.
8. embedded load characteristic automatic identification method as claimed in claim 7, which is characterized in that
The embedded load characteristic automatic identification method further include: whether accurate, i.e., if verifying adaptive state-space model
It is identified object and is receiving verifying identification input utestVerifying identification output y is generated afterwardstest, and utilize adaptive state
Spatial model recognizes input u according to verifyingtestIt obtains output signal and forecasts ypre, work as ytsetAnd ypreAdaptive shape is judged when identical
State space model is accurate.
9. a kind of adaptive state-space model recognized automatically for load characteristic characterized by comprising
xk+1=Axk+Buk
yk=Cxk+Duk；
In formula, xk+1The state variable forecast of object is identified for the subsequent time at current k moment；xkTo be identified the object k moment
State variable estimate；ukFor the input signal for being identified the object k moment；ykIt is pre- for the output signal that is identified the object k moment
Report；A, B, C, D are adaptive state-space model matrix.
10. a kind of control method of switching mode digital power characterized by comprising
Power major loop and digital control board for controlling power major loop output voltage；
The digital control board is suitable for by such as the described in any item embedded load characteristics of the claim 1-9 side of identification automatically
Method is adjusted according to load characteristic variation stablizes the output voltage.
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