CN105553442B - The H of network-based Lipschitz nonlinear system∞Filter information processing method - Google Patents
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Abstract
The invention discloses a kind of H of network-based Lipschitz nonlinear system∞Filter information processing method includes the following steps: 1) to analyze the networking Lipschitz nonlinear system for being difficult to determining noise jamming by statistical property；And signal is exported and be estimated to the measurement of system and carries out quantification treatment, filter end is sent to by network；2) filter will measure output after receiving data and be estimated Signal separator；And the data transmission procedure based on Markov chain description, the H of Lipschitz nonlinear system is discussed∞Filter problem；3) the state transition probability information based on Markov chain is established novel networking Lipschitz nonlinear system and filter model, and is estimated according to state of the newly-established filter model to system.The present invention has studied the H of a kind of Lipschitz nonlinear system with quantization and data-bag lost∞Filter design problem, and the information processing method based on the filter is given, realize estimation of the filter to system mode.
Description
Technical field
The present invention relates to a kind of network control systems, more particularly, to network-based Lipschitz nonlinear system
H∞Filter information processing method.
Background technique
With the development of network technology, communication network in the controls using more and more extensive.It is this to pass through communication
The control system that network forms closed loop is known as network control system or network-based control system.In network control system
In, sensor, controller and actuator use a network for information exchange, and therefore, network control system has installation maintenance
Conveniently, the advantages that flexibility is high and can be realized long-range control.
Since network control system introduces communication network in control loop, data quantization and data are produced
The problems such as packet loss.If the state of networked system can not be surveyed and when by noise disturbance, need to design filter to system
State estimated.When the noise that the system of being estimated is subject to is white noise, can design using estimation error variance conduct
The Kalman filter and linear filter of performance metric estimate system mode.
Currently, network-based filter designs primarily directed to linear system, has fan-shaped nonlinear system and by T-S
The nonlinear system of fuzzy model description.Lipschitz nonlinear system is a kind of important nonlinear system, for example, in the presence of
The robot system of trigonometric function item is Lipschitz nonlinear system, and the nonlinear system of some complexity can be described as line
Property system with meet the sum of the nonlinear terms of Lipschitz condition.However existing for existing network-based filter design
Problem has: one, being estimated signal by conducting wire rather than by network transmission to long-range filter, the system of increasing is implemented as
This, is unfavorable for system installation maintenance.Two, mainly consider network transfer delay and data-bag lost, and make great efforts distribution description with shellfish
Data transmission procedure is not suitable for the case where data pass through wireless network transmissions.Therefore, when Lipschitz nonlinear system by
When being difficult to determining noise disturbance to statistical property, the present invention, which will measure output and be estimated signal, to be quantified and passes through network
It is transferred to long-range filter, and describes data transmission procedure with Markov chain, devises network-based H∞Filter.
Summary of the invention
The purpose of the present invention is in view of the deficienciess of the prior art, proposing that a kind of network-based Lipschitz is non-thread
The H of property system∞Filter information processing method.
The technical solution adopted by the present invention is as follows:
The H of network-based Lipschitz nonlinear system∞Filter information processing method, includes the following steps:
1) the networking Lipschitz nonlinear system that determining noise jamming is difficult to by statistical property is carried out first
Analysis；And signal is exported and be estimated to the measurement of system and carries out quantification treatment, filter end is sent to by network；
2) filter will measure output after receiving data and be estimated Signal separator；And based on Markov chain description
Data transmission procedure discusses the H of Lipschitz nonlinear system∞Filter problem；
3) the state transition probability information based on Markov chain establishes novel networking Lipschitz nonlinear system
System and filter model, and estimated according to state of the newly-established filter model to system.
In the step 1), the state equation that Lipschitz nonlinear system is established is as follows
X (k+1)=Ax (k)+Ff (k, x (k))+Bw (k)
Y (k)=Cx (k)+Gg (k, x (k))+Dw (k) (1)
Z (k)=Lx (k)
Wherein: x (k) ∈ RnIt is state vector, y (k) ∈ RpIt is measurement output, z (k) ∈ RqIt is to be estimated signal, w (k) ∈
RmIt is space L2[0, ∞) on noise signal, A, F, B, C, G, D and L are the coefficient matrix of known corresponding dimension, f (k, x (k))
It is the Nonlinear Vector function for meeting following Lipschitz condition with g (k, x (k)): f (k, 0)=0, g (k, 0)=0, | | f
(k,x(k))||≤||F1X (k) | |, | | g (k, x (k)) | |≤| | G1X (k) | |, wherein F1, G1It is known corresponding dimension square
Battle array.
In the step 1), the method for exporting and being estimated signal progress quantification treatment to the measurement of system is specific as follows:
Measurement output y (k) and the element for being estimated signal z (k) break into a data packet after quantization and are passed by network
It is defeated to arrive filter node, first it is quantified before transmission, quantizer are as follows:
Wherein, quantization level collection the U={ ± u of systemi,ui=ρiu0, i=± 1, ± 2 ... ∪ { ± u0∪ { 0 }, 0 < ρ <
1, u0> 0,ρ is the quantization resolution of quantizer Q (v).
In the step 2), the data transmission procedure method of Markov chain description is as follows:
σ (k)=0 indicates not lose when data pass through network transmission；σ (k)=1 indicates to generate when data pass through network transmission
It loses；State transition probability matrix P=[the p of Markov chainij], wherein
pij>=0,Work as p01+p10When=1, transmission process makes great efforts distribution for shellfish；
When data pass through network normal transmission, then filter will measure output after receiving data and be estimated signal point
From；Filter inputs yf(k)=Q (y (k)), filter is received to be estimated signal zc(k)=Q (z (k))；The quantizer of system
Using logarithmic quantization device, therefore yf(k)=(I+H (k)) y (k), zc(k)=(I+H (k)) z (k), wherein I is the list of corresponding dimension
Bit matrix, H (k) is uncertain matrix, and is met | | H (k) | |≤δ；
When data are lost in transmission process, filter does not receive the measurement output and be estimated that network transmits
Signal, it is assumed that filter input keeps the value of previous moment constant, i.e. yf(k)=yf(k-1), filter do not receive by
Estimate signal, i.e. zc(k)=0, such filter input and the received signal that is estimated can turn to:
yf(k)=(1- σ (k)) (I+H (k)) y (k)+σ (k) yf(k-1) (3)
zc(k)=(1- σ (k)) (I+H (k)) z (k) (4).
The step 2) discusses the H of Lipschitz nonlinear system∞The method of filter problem is specific as follows:
For the output z (k) of estimator (1), the mode for designing following form relies on filter:
xf(k+1)=Afσ(k)xf(k)+Bfσ(k)yf(k)
(5)
zf(k)=Cfσ(k)xf(k)
Wherein: xf(k)∈RnIt is filter status vector, yf(k)∈RpIt is filter input, zf(k)∈RqIt is estimation letter
Number, Afσ(k), Bfσ(k)And Cfσ(k), σ (k)=i ∈ { 0,1 } is filter parameter undetermined；
Definition vectorη (k)=[fT(k,x(k)) gT(k,x(k))]T, estimation
Error e (k)=zc(k)-zf(k), by formula (1), formula (3), formula (4) and formula (5), following filtering error system can be obtained:
Wherein: E1=[C 0 0],
E2=[0 G], E3=D,E4=[L 0 0],
It is specific as follows to establish novel networking Lipschitz nonlinear system and filter model for the step 3):
For given positive number γ and quantization resolution ρ, if there is symmetric positive definite matrix Pi、Mi、RiAnd matrix Ni、Vi、
Bfi, i=0,1, positive scalar ε1、ε2And ε3, so that following linear MATRIX INEQUALITIES (7) and (8) are set up, then filtering error system
(6) formula is Stochastic stable and has H∞Performance γ；Wherein filter parameter
Wherein:
Π22=diag {-ε1I,-ε1I+ε2δ2GTG },
Π31=[ε2δ2DTC 0 0], Π32=[0 ε2δ2DTG], Π33=-γ2I+ε2δ2DTD,
Π61=[L-V00], Π66=-I,Π75=Π74, Π77=-ε2I,
Π86=I, Π88=-ε3I,Ω22=-ε1I,
Ω33=-γ2I,
Ω61=[0-V10], Ω66=-I.
Advantages of the present invention and remarkable result are specific as follows:
(1) signal is exported and is estimated by the measurement of Lipschitz nonlinear system to be quantified and pass through network transmission
To long-range filter, network-based H is devised∞Filter reduces the cost of implementation of filtering system compared with prior art,
The convenience and flexibility for improving system installation maintenance, that is, run data through in network transmission process exist lose, system by
Determining noise jamming is difficult to statistical property, estimation signal, which also can be tracked preferably, is estimated signal.
(2) present invention describes data transmission procedure using Markov chain, makes great efforts distribution description data compared to using shellfish
Transmission process is particularly suitable for the case where data pass through wireless network transmissions with more generality.
(3) of the invention to utilize Lyapunov Functional Approach, it has studied a kind of with quantization and data-bag lost
The H of Lipschitz nonlinear system∞Filter design problem, and the information processing method based on the filter is given, it realizes
Estimation of the filter to system mode.
Detailed description of the invention
Fig. 1 is network-based nonlinear system filter structure figure；
Fig. 2 is data transmission procedure figure；
Fig. 3 is estimation figure of the filter to system mode.
Specific embodiment
Technical solution of the present invention is described in detail with reference to the accompanying drawing.
It as shown in Figure 1, Figure 2 and Figure 3, is network-based nonlinear system filter structure figure, data transmission procedure respectively
Figure and filter are to the estimation figure of system mode, and the present invention is based on the H of the Lipschitz nonlinear system of network∞Filter letter
Processing method is ceased, is included the following steps:
1) the networking Lipschitz nonlinear system that determining noise jamming is difficult to by statistical property is carried out first
Analysis；And signal is exported and be estimated to the measurement of system and carries out quantification treatment, filter end is sent to by network；
2) filter will measure output after receiving data and be estimated Signal separator；And based on Markov chain description
Data transmission procedure discusses the H of Lipschitz nonlinear system∞Filter problem；
3) the state transition probability information based on Markov chain establishes novel networking Lipschitz nonlinear system
System and filter model, and estimated according to state of the newly-established filter model to system.
Wherein, the networking Lipschitz that determining noise jamming is difficult to by statistical property in step 1) is non-linear
The state equation such as formula (1) that system is established.The side that signal is quantified and be packaged processing is exported and is estimated to the measurement of system
Method is as follows: measurement output y (k) and the element for being estimated signal z (k) break into a data packet after quantization and pass through network transmission
To filter node.First it is quantified before transmission, quantizer is formula (2).
The data transmission procedure method that Markov chain describes in step 2) is as follows: indicating that data pass through net with σ (k)=0
Network is not lost when transmitting；σ (k)=1 indicates to generate loss when data pass through network transmission.The state transfer of Markov chain is general
Rate matrix P=[pij], whereinpij>=0,When
p01+p10When=1, transmission process makes great efforts distribution for shellfish, therefore describes data transmission procedure with more general with Markov chain
Property, it is especially suitable for description data and passes through wireless network transmissions situation.
When data pass through network normal transmission, then filter will measure output after receiving data and be estimated signal point
From.Filter inputs yf(k)=Q (y (k)), filter is received to be estimated signal zc(k)=Q (z (k)).Due to the amount of system
Change device and uses logarithmic quantization device, therefore yf(k)=(I+H (k)) y (k), zc(k)=(I+H (k)) z (k), wherein | | H (k) | |
≤δ.When data are lost in transmission process, filter does not receive the measurement output and be estimated letter that network transmits
Number, it is assumed that filter input keeps the value of previous moment constant, i.e. yf(k)=yf(k-1), filter does not receive and is estimated
Count signal, i.e. zc(k)=0.Filter input and the received signal that is estimated can turn to formula (3) and formula (4) in this way
For the output z (k) of estimator (1), designs the mode of formula (5) such as and rely on filter.
Definition vectorη (k)=[fT(k,x(k)) gT(k,x(k))]T, evaluated error e
(k)=zc(k)-zf(k), by formula (1), formula (3), formula (4) and formula (5), filtering error systematic (6) can be obtained.
It is specific as follows that step 3) establishes novel networking Lipschitz nonlinear system and filter model: for
Fixed positive number γ and quantization resolution ρ, if there is symmetric positive definite matrix Pi, Mi, Ri, matrix Ni, Vi, Bfi, i=0,1, positive scalar
ε1, ε2And ε3, so that linear matrix inequality (7) and (8) are set up, then filtering error system (6) is Stochastic stable and has
H∞Performance γ；Wherein filter parameter
The given matrix W with corresponding dimension of lemma, D and E, wherein W is symmetrical matrix.Meet F for allT(k)F(k)
The matrix F (k) of≤I, W+DF (k) E+ETFT(k)DT<0 necessary and sufficient condition set up is that there are ε>0, so that W+ ε DDT+ε-1ETE<0。
Select following Lyapunov functional:
As w (k)=0,Difference along system (6) are as follows:
Due to
So there are ε1> 0, so thatThen
Wherein
IfN0=Af0M0, V0=Cf0M0, N1=Af1M1, V1=Cf1M1,
(7) formula both sides respectively multiplied by:
I.e.
It willIt substitutes into, according to lemma, above formula can be turned to:
I.e.
It willIt substitutes into, according to lemma,
Above formula can turn to:
Lemma is mended by Schur, above formula can turn to:
As available from the above equation:
Similarly, by (8) Shi Ke get:
It can further obtain:
Therefore,
WhereinFor ΘiMaximum eigenvalue.
As N >=1,
Above formula can turn to:
As N → ∞, can obtain:
Therefore, filtering error system (6) is Stochastic stable.
Filtering error system (6) be will be proven below with H∞Performance γ.Under zero initial condition, definition
Above formula may be expressed as:
Wherein
As N → ∞, can obtain:
Therefore, filtering error system (6) has H∞Performance γ.
Below according to example, to verify effectiveness of the invention and superiority.
In Fig. 1, such as Lipschitz nonlinear system shown in formula (1), parameter are as follows:
C=[0.1 0.1], G=[0.1 0.2], D=
0.3, L=[1 1], nonlinear function f (k, x (k))=0.1sin (x (k)), g (k, x (k))=0.1sin (x (k)), quantization
The quantization resolution ρ=0.5, H of device Q (v)∞Performance γ=1.8, data transmission procedure are as shown in Fig. 2, its state transition probability square
Battle arrayNoise signal
Filter parameter can be obtained by solving linear matrix inequality formula (7) and formula (8) using the tool box LMI in MATLAB
Are as follows:
Cf0=[0.1013 0.1001],
Cf1=[0.1195 0.0962].
Claims (5)
1. the H of network-based Lipschitz nonlinear system∞Filter information processing method, which is characterized in that including as follows
Step:
1) the networking Lipschitz nonlinear system that determining noise jamming is difficult to by statistical property is divided first
Analysis；And signal is exported and be estimated to the measurement of system and carries out quantification treatment, filter end is sent to by network；
2) filter will measure output after receiving data and be estimated Signal separator；And the data based on Markov chain description
Transmission process discusses the H of Lipschitz nonlinear system∞Filter problem, the mode for designing following form rely on filter:
Wherein: xf(k)∈RnIt is filter status vector, yf(k)∈RpIt is filter input, zf(k)∈RqIt is to estimate signal, n,
P and q is the dimension of vector, Afσ(k)、Bfσ(k)And Cfσ(k)It is filter parameter undetermined, σ (k)=i ∈ { 0,1 }；
3) the state transition probability information based on Markov chain, establish novel networking Lipschitz nonlinear system and
Filter model, and estimated according to state of the newly-established filter model to system, wherein establish novel networking
Lipschitz nonlinear system and filter model are specific as follows:
For given positive number γ and quantization resolution ρ, if there is symmetric positive definite matrix Pi、Mi、RiAnd matrix Ni、Vi、Bfi, i=
0,1, positive scalar ε1、ε2And ε3, so that following linear MATRIX INEQUALITIES (2) and (3) are set up, wherein filter parameter
Wherein:
Π22=diag {-ε1I,-ε1I+ε2δ2GTG },
Π31=[ε2δ2DTC 0 0], Π32=[0 ε2δ2DTG], Π33=-γ2I+ε2δ2DTD,
Π61=[L-V00], Π66=-I,Π75=Π74, Π77=-ε2I,
Π86=I, Π88=-ε3I,Ω22=-ε1I, Ω33
=-γ2I,
Ω61=[0-V10], Ω66=-I；
Wherein, F1And G1It is known corresponding dimension matrix in Lipschitz nonlinear system；F (k, x (k)) and g (k, x (k))
It is non-linear vector function；A, F, B, C, G, D and L are the coefficient matrixes of the known corresponding dimension of Lipschitz nonlinear system；
I is the unit matrix of corresponding dimension；p00、p01、p10And p11It is the element of the state transition probability matrix P of Markov chain；δ is
The uncertain norm of matrix upper bound in known quantizer output；γ is given positive number i.e. H∞Performance.
2. the H of network-based Lipschitz nonlinear system according to claim 1∞Filter information processing method,
It is characterized in that, the state equation that Lipschitz nonlinear system is established is as follows in the step 1)
Wherein: x (k) ∈ RnIt is state vector, y (k) ∈ RpIt is measurement output, z (k) ∈ RqIt is to be estimated signal, w (k) ∈ RmIt is
Space L2[0, ∞) on noise signal, n, p, q and m are the dimensions of vector；A, F, B, C, G, D and L are known corresponding dimensions
Coefficient matrix, f (k, x (k)) and g (k, x (k)) they are the Nonlinear Vector functions for meeting following Lipschitz condition: f (k, 0)=
0, g (k, 0)=0, | | f (k, x (k)) | |≤| | F1X (k) | |, | | g (k, x (k)) | |≤| | G1X (k) ‖, wherein F1, G1It is known
Corresponding dimension matrix.
3. the H of network-based Lipschitz nonlinear system according to claim 2∞Filter information processing method,
It is characterized in that, in the step 1), signal is exported and be estimated to the measurement of system and carry out the method for quantification treatment specifically such as
Under:
Measurement output y (k) and the element for being estimated signal z (k) break into a data packet after quantization and are arrived by network transmission
Filter node first quantifies it before transmission, quantizer are as follows:
Wherein, quantization level collection the U={ ± u of systemi,ui=ρiu0, i=± 1, ± 2 ... ∪ { ± u0∪ { 0 }, 0 < ρ < 1,
u0> 0,ρ is the quantization resolution of quantizer Q (v).
4. the H of network-based Lipschitz nonlinear system according to claim 3∞Filter information processing method,
It is characterized in that, the data transmission procedure method of Markov chain description is as follows in the step 2):
σ (k)=0 indicates not lose when data pass through network transmission；σ (k)=1, which indicates to generate when data pass through network transmission, to be lost
It loses；State transition probability matrix P=[the p of Markov chainij], wherein
pij>=0,Work as p01+p10When=1, transmission process makes great efforts distribution for shellfish；
When data pass through network normal transmission, then filter will measure output after receiving data and be estimated Signal separator；
Filter inputs yf(k)=Q (y (k)), filter is received to be estimated signal zc(k)=Q (z (k))；The quantizer of system uses
Logarithmic quantization device, therefore yf(k)=(I+H (k)) y (k), zc(k)=(I+H (k)) z (k), wherein I is the unit square of corresponding dimension
Battle array, H (k) is uncertain matrix, and meets ‖ H (k) ‖≤δ；
When data are lost in transmission process, filter does not receive the measurement output and be estimated letter that network transmits
Number, it is assumed that filter input keeps the value of previous moment constant, i.e. yf(k)=yf(k-1), filter does not receive and is estimated
Count signal, i.e. zc(k)=0, such filter input and the received signal that is estimated can turn to:
yf(k)=(1- σ (k)) (I+H (k)) y (k)+σ (k) yf(k-1) (6)
zc(k)=(1- σ (k)) (I+H (k)) z (k) (7).
5. the H of network-based Lipschitz nonlinear system according to claim 4∞Filter information processing method,
It is characterized in that, in the step 2), definition vectorη (k)=[fT(k,x(k))gT
(k,x(k))]T, evaluated error e (k)=zc(k)-zf(k), by formula (1), formula (4), formula (6) and formula (7), following filtering can be obtained
Error system:
Wherein: E1=[C 0 0], E2
=[0 G], E3=D,E4=[L 0 0],
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6574336B1 (en) * | 1996-06-19 | 2003-06-03 | Nokia Telecommunications Oy | Echo suppressor and non-linear processor of echo canceller |
| CN103701433A (en) * | 2013-12-29 | 2014-04-02 | 哈尔滨理工大学 | Quantitative filtering method of time-varying target tracking system under condition with multiple measurement loss |
-
2015
- 2015-12-18 CN CN201510967000.3A patent/CN105553442B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6574336B1 (en) * | 1996-06-19 | 2003-06-03 | Nokia Telecommunications Oy | Echo suppressor and non-linear processor of echo canceller |
| CN103701433A (en) * | 2013-12-29 | 2014-04-02 | 哈尔滨理工大学 | Quantitative filtering method of time-varying target tracking system under condition with multiple measurement loss |
Non-Patent Citations (1)
| Title |
|---|
| 基于Markov模型的无线网络量化系统的H∞滤波研究;楼斌;《中国优秀硕士学位论文全文数据库》;20131215;第11-18、29-40页 * |
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Effective date of registration: 20201224 Address after: Room 504, building 6, Tianan Digital City, 99 Shennan Road, Gangzha District, Nantong City, Jiangsu Province, 226000 Patentee after: JIANGSU EMPOWER INTELLIGENT TECHNOLOGY Co.,Ltd. Address before: 226019 Jiangsu Province, Nantong City Chongchuan District sik Road No. 9 Patentee before: NANTONG University |