CN105606381B - A kind of Distributed filtering network system and design method - Google Patents
A kind of Distributed filtering network system and design method Download PDFInfo
- Publication number
- CN105606381B CN105606381B CN201610058593.6A CN201610058593A CN105606381B CN 105606381 B CN105606381 B CN 105606381B CN 201610058593 A CN201610058593 A CN 201610058593A CN 105606381 B CN105606381 B CN 105606381B
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- wave filter
- filter
- mtd
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M17/00—Testing of vehicles
- G01M17/007—Wheeled or endless-tracked vehicles
- G01M17/04—Suspension or damping
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Vehicle Body Suspensions (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The present invention relates to a kind of Distributed filtering network system and design method.The system includes：Filter network unit：Wave filter including being distributed in different physical sites；Sensor sample unit：Including onboard sensor and filtered sensor, onboard sensor is embedded in automobile suspension system and gathers its status information, and filtered sensor is embedded in each wave filter and gathers the exchange of information of the wave filter and the adjacent filter of setting；Event trigger element：Including event trigger corresponding with each wave filter, each event trigger determines respective filter event generation time respectively；In event generation time, wave filter receives the automobile suspension system status information of corresponding onboard sensor collection and sets the exchange of information of adjacent wave filter, and otherwise the wave filter is failure to actuate.Compared with prior art, present invention the needs of can meeting multiple terminals while obtaining data, and can effectively prevents filter network from collapsing, and energy consumption is low, efficiency high, stability are strong.
Description
Technical field
The present invention relates to a kind of filtering system and design method, more particularly, to a kind of Distributed filtering network system and sets
Meter method.
Background technology
With increasing considerably for automobile usage amount, automobile ride research is increasingly paid close attention to by people, and it also turns into
One of important performance indexes of automobile.During Ride Comfort Analysis, accurate real-time dynamic information collection is accurately analyzed
Premise.Although conventional data collecting system is effective, the experimental situation with ever-increasing high-precision requirement, increasingly complicated
With the interference of measurement noise, existing collecting method can not meet the requirement of people.
At present, most of research be all around properties of product forecasting problem, i.e., it is whole by establishing in the Automobile Design stage
Car simulation model, the parameters of operating part such as suspension, vehicle body are analyzed.Ride Comfort Analysis based on emulation experiment, automobile dynamic are real
When data message be easy to be collected.But Ride Comfort Analysis can not be only limited to product prediction and emulation experiment, when needs pair
When the true car for having certain driving age of wide variety carries out ride comfort estimation, it is contemplated that the polytropy of running car environment and automobile sheet
The complexity of body performance, automobile Simulation Experimental Platform are tended not to simulate all situations, and this is just needed to travelling in true environment
In automobile carry out Ride Comfort Analysis experiment.The presence of the complexity of automobile construction, sampling error and ambient noise is to high accuracy
Data acquisition propose challenge.Furthermore, it is contemplated that the automobile position in motion is changing always, the data message collected needs
To be transferred to the wave filter of fixed station by radio sensing network, the antijamming capability in information exchanging process is that wave filter is set
The important indicator considered is needed in meter.
Kalman filtering is classical filter form, for solving most of problem, it be optimal, efficiency highest even
It is most useful, but it only has good filter effect to white Gaussian noise, it is not strong to the adaptability of noise type change.Fortune
Dynamic automotive system there may be other kinds of noise, therefore unsatisfactory using Kalman filtering mode effect.
In addition, the data message that onboard sensor collects needs to send filter network to by wireless network.Tradition
Sample mode is periodic sampling, although periodic sampling can reduce the degree of transitivity of packet in network, works as the real-time appearance of automobile
When state information change amplitude is small, the faint or wave filter of system noise influence has accurately estimated vehicle condition information, just do not have
It is necessary to still provide for intensive cycle information transmission.So in order to reduce data communication rates, save Internet resources, ensureing to filter
On the premise of ripple device service behaviour, reduction sample information transmission capacity that can be appropriate.In order to reach this purpose, the present invention uses
Event triggers sampling mechanism to replace traditional periodic sampling.Numerous studies demonstrate the superiority of event trigger mechanism, it
The fields such as network control, multi-agent system, air formation are widely applied to, according to the difference of application background, are also divided into base
In the trigger mechanism that system mode, system output, observer export.The present invention is filtered using based on automobile suspension system and neighbours
The event triggering sampling mechanism of device output information, filter network can be achieved with to automobile real time status information according to output information
Accurate estimation.The core of event trigger mechanism is design trigger conditions, and it directly affects sample information and passes frequency and filtering
The quality of effect.Event generator is judged trigger conditions in each sampling instant, once condition deviates predetermined threshold
Value, trigger sensor transmit current sample values to wave filter, otherwise, abandon current data information.Event triggers sampling mechanism energy
It is enough effectively to reduce data transfer number, reduce communication bandwidth occupation rate, save Internet resources.
Although single filter can realize the estimation to vehicle condition, it sometimes appear that multiple Performance Evaluation websites are same
When need to receive the situation of automobile information, or single filter failure causes the whole situation for assessing process interrupt.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to including white noise
A variety of form of noise inside have good anti-noise ability, and effectively reduce data on the premise of filtering performance is ensured and pass
Successive number, Internet resources are saved, meet the Distributed filtering network system and design method of the demand for development of modern economy society.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of Distributed filtering network system, is analyzed for 1/4 vehicle model automobile ride, and the system includes：
Filter network unit：Wave filter including being distributed in different physical sites, each wave filter carry out letter by network
Breath interaction；
Sensor sample unit：Including onboard sensor and filtered sensor, described onboard sensor is embedded in automobile
Suspension system simultaneously gathers automobile suspension system status information, and described filtered sensor is embedded in each wave filter and gathers the filter
The exchange of information of ripple device and the adjacent filter of setting；
Event trigger element：Including event trigger corresponding with each wave filter, each event trigger is based on periodic event
Trigger mechanism, respective filter event generation time is determined respectively；
In event generation time, wave filter receive corresponding onboard sensor collection automobile suspension system status information and
The exchange of information of adjacent wave filter is set, otherwise the wave filter is failure to actuate.
A kind of design method of Distributed filtering network system, this method comprise the following steps：
(1) discretization model of 1/4 vehicle model automobile is established：
X (k+1)=Ax (k)+Bv (k) (1)
Wherein, x (k) is k moment automobile suspension system state variables, and x (k+1) is k+1 moment automobile suspension system states
Variable, v (k) are the road rumble at current time, and A and B are respectively the normal matrix of system for being adapted to dimension；
(2) each onboard sensor sampling model is established：
yi(k)=Cix(k)+Diωi(k) (2)
Wherein, i=1,2 ... N, N are onboard sensor total number, yi(k) i-th of onboard sensor of k moment is represented
Output information, ωi(k) sampling noiset of i-th of onboard sensor of k moment, C are representedi、DiIt is the normal matrix of system for being adapted to dimension；
(3) each wave filter exchange of information model in filter network unit is established：
Wherein,The exchange of information of i-th of wave filter is given for j-th of filter transfer of k moment,For the k moment
The estimated state of j-th of wave filter,Represent the communication channel between j-th of wave filter of k moment and i-th of wave filter
Random perturbation, Eij、FijTo be adapted to the coefficient matrix of dimension, i=1,2 ... N, j=1,2 ... N, N are number of filter；
(4) trigger condition of event trigger in each wave filter is established respectively：
Wherein, σi、And εiFor i-th of event trigger threshold parameter, ΦiFor the first weight of i-th of event trigger
Matrix,For the second weight matrix of i-th of event trigger,For i-th of filtering
The estimated state at device k moment,For the event generation time of i-th of wave filter, Ni
For that can transmit set of the exchange of information to all adjacent filters of i-th of wave filter, Ξ represents to ask square；
(5) wave filter discretization model is established based on event trigger trigger condition：
Wherein i=1,2 ... N represent i-th of wave filter, and N is wave filter total number,Filtered for i-th for the k moment
Device estimated state,For i-th of wave filter estimated state of k+1 moment, HiFor the local gain of i-th of wave filter, Ki
The coupling gain of i-th of wave filter,Exist for i-th of wave filterThe state estimation at moment,For
I-th of wave filter existsThe output information at moment,ForJ-th of filter transfer of moment is to i-th of filtering
The exchange of information of device；
(6) formula (1) and formula (5) are subtracted each other, and integrates and obtain filter network evaluated error model：
Wherein,For the evaluated error of i-th of wave filter of k moment,For i-th
Individual wave filter k moment evaluated error and event triggering momentThe difference of evaluated error,
For event triggering momentThe random perturbation of communication channel between j-th of wave filter and i-th of wave filter, FijIt is to be adapted to dimension
The normal matrix of degree；
(7) rememberWhole filter network evaluated error mould is obtained according to formula (7)
Type is：
Wherein, ξ (k)=[eT(k),δT(k),vT(k),ωT(k)]T, H=diag { H1,H2,…,HN, C=diag { C1,C2,…,CN, D=diag { D1,
D2,…,DN, K=diag { K1,K2,…,KN, aijFor adjacency coefficient, i=1,2 ... N, j=1,2 ... N,
(8) there is H using the design of Liapunov stability analysis method to filter network evaluated error model∞Stability
Filter network constraint matrix；
(9) according to above-mentioned constraint matrix and given periodic event activation threshold value parameter σi、And εiCalculate wave filter
Each wave filter local gain H in networkiWith coupling gain Ki。
Described step (8) includes following sub-step：
(801) establishing Lyapunov Equation is：
V (e (k))=eT(k)Pe(k) (8)
Wherein, P is positive matrices；
(802) difference equation of Lyapunov Equation is solved, being solved using the method for Lyapunov's stability theorem second is made
Obtaining filter network system has H∞The constraint matrix of stability：
Wherein, γ decays for disturbance rejection
Level,N is number of filter, and I is N × N-dimensional unit matrix, Φ=
diag{Φ1,Φ2,…ΦN, the Laplacian Matrix for the graph structure that L is formed for filter network, IpUnit matrix is tieed up for p × p,ForTie up unit matrix,
Described filter network system has H∞Stability specifically meets the following steps：
(a) judge whether to consider its exterior disturbance, sensor measurement errors and interchannel noise, if performing step (b),
Otherwise step (c) is performed；
(b) filter network evaluated error meets：
limk→∞Ξ||ei(k)||2≤Mρk (11)
Wherein M and ρ is normal number, and Ξ represents to ask square, and k is discrete time；
(c) filter network evaluated error meets：
Wherein R is normal number,X (0) is the initial of automobile suspension system state
Value.
The constraint matrix obtained to step (802) linearizes, and is specially：
Make Xi=PiHi, Yi=PiKi, X=diag { X1,X2,…,XN, Y=diag { Y1,Y2,…,YN, by constraints
Matrixing is：
Wherein,P is positive definite matrix to be solved.
Described step (9) specifically includes following sub-step：
(901) given event activation threshold value parameter σi、And εiInitial value, and meetεi＞ 0, performs step
Suddenly (902)；
(902) by event trigger threshold parameter σi、And εiσ is entered as respectivelyi=σi+Δ1,εi=εi
+Δ3, and meetεi＞ 0, wherein Δ1、Δ2And Δ3Respectively σi、And εiIteration step length；
(903) by σi、εiSubstitute into inequality (13) and (14) and solve inequality, if without solution, return to step
(902), if there is solution, matrix X, Y and γ are obtained；
(904) the transfer rate ο of filter network data message is calculated, judges ο≤οopt,γ≤γoptWhether set up, wherein
ooptFor anticipatory data transfer rate, γoptFor expected interference rejection ratio, local gain H is calculated if setting upi=Pi -1Xi, coupling increasing
Beneficial Ki=Pi -1Xi, otherwise return to step (902).
Compared with prior art, the invention has the advantages that：
(1) filter network design method of the present invention is simple, and filter parameter (including local gain and coupling gain) is easy
It is determined that as long as meet MATRIX INEQUALITIES (13) and the matrix K of (14)iAnd HiThe gain matrix of filter network is can serve as, and
MATRIX INEQUALITIES (13) and (14) are two conditions being easily met.
(2) present invention is simple to filtering object requirement, and the present invention only utilizes the Observable output variable of automobile suspension system,
It is filtered using onboard sensor measured value, realizes and the status information of automobile suspension system is observed, to car model
Without other requirements.
(3) filter network layout strategy of the present invention is flexible, meets different accuracy requirement, has a wide range of application, filter network
Need the condition conservative that meets low, for different disturbance rejection Reduction Level γ, can be easy to from MATRIX INEQUALITIES (13)
(14) value of filter gain matrix is solved in, is designed hence for the filter network of different accuracy requirement, the present invention
Strategy can be realized.
(4) present invention determines that wave filter receives the automotive suspension system of onboard sensor collection using event triggering sampling mechanism
At the time of status information of uniting, event triggering sampling can be with the reality of current filter object compared with traditional continuous or periodic sampling
When state be reference quantity, decide whether transmit present sample data.When automobile is disturbed smaller or undisturbed, wave filter filter
When ripple effect is preferable, data transfer is relatively dredged；When automobile is disturbed larger, automobile real-time attitude information change speed is larger
When, data transfer is closeer.In general, the periodic event triggering sampling mechanism based on output feedback can be reduced effectively in network
Data transfer number, save Internet resources.
(5) the Distributed filtering network that the present invention designs to estimate automobile attitude information in real time, the wave filter of strange land distribution
By the exchange of information with onboard sensor, neighbours' wave filter, realize that the data message sent to onboard sensor is carried out
Filtering, is collected into accurate data message.Compared with traditional centralized wave filter, the Distributed filtering network of the invention designed
It disclosure satisfy that multiple observation stations while obtain the requirement of precise information, and have certain tolerance to wave filter collapse.
In a word, on the premise of meeting that more observed terminals can receive precise information simultaneously, the robustness of filter network is also improved.
(6) H that the present invention designs∞Filter network can resist the interference of diversified forms, include disturbance, the biography of external environment
The sampling error and communication channel noise of sensor.And compared with the Kalman filtering of classics, H∞Filter network can filter
More extensive types of noise, for example disturb when not being the Parameter uncertainties of white noise or noise, classical Kalman filtering is just
No longer it is applicable, but H∞Filtering but still functions as good filter effect.
Brief description of the drawings
Fig. 1 is Distributed filtering network architecture schematic diagram of the present invention；
Fig. 2 is the structural representation of 1/4 vehicle model of 2DOF of the present invention；
Fig. 3 is the simulation platform structure schematic diagram for the Distributed filtering network based on ChangAn Automobile that the present invention designs；
Fig. 4 is the displacement curve comparison diagram of unsprung mass block；
Fig. 5 is the displacement curve comparison diagram of sprung mass block；
Fig. 6 is the rate curve comparison diagram of unsprung mass block；
Fig. 7 is the rate curve comparison diagram of sprung mass block；
Fig. 8 is the evaluated error curve map of the filter network based on event triggering；
Fig. 9 is the timing diagram that the first wave filter receives external information in Fig. 3；
Figure 10 is the timing diagram that the second wave filter receives external information in Fig. 3；
Figure 11 is the timing diagram that the 3rd wave filter receives external information in Fig. 3
In figure, 1 is automobile suspension system, and 2 be onboard sensor, and 3 be event trigger, and 4 be wave filter, and 5 be transmission network
Network.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
As shown in figure 1, a kind of Distributed filtering network system, is analyzed, the system bag for 1/4 vehicle model automobile ride
Include：Filter network unit：Wave filter 4 including being distributed in different physical sites, each wave filter 4 enter row information by network and handed over
Mutually；Sensor sample unit：Including onboard sensor 2 and filtered sensor, described onboard sensor 2 is embedded in automotive suspension
System 1 simultaneously gathers the status information of automobile suspension system 1, and described filtered sensor is embedded in each wave filter 4 and gathers the filter
The exchange of information of ripple device 4 and the adjacent filter of setting；Event trigger element：Including being triggered with each 4 corresponding event of wave filter
Device 3, each event trigger 3 are based on periodic event trigger mechanism, determine the event generation time of respective filter 4 respectively；In event
Generation moment, wave filter 4 receive the automobile suspension system status information of the corresponding collection of onboard sensor 2 and the filter that setting is adjacent
The exchange of information of ripple device, otherwise wave filter 4 be failure to actuate.
Based on a kind of above-mentioned design method of Distributed filtering network system, this method comprises the following steps：
(1) as shown in Fig. 2 car model can be reduced to 1/4 vehicle model of 2 frees degree, wherein k1And k2It is spring
Coefficient of elasticity, m1、m2、x1And x2Quality and the displacement of unsprung mass block and sprung mass block are represented respectively, and v represents to become on road surface
The input disturbance of change, c are damped coefficient.According to Hooke's law and Newton's second law, the dynamic of the vehicle model of two degrees of freedom 1/4 is obtained
Shown under mechanical equation：
Definition,So obtaining continuous system model isWhereinBc=[0 0-k1/m1 0]T.Then state space time domain solution handle is utilized
Continuous system model discretization, obtain the discretization model of 1/4 vehicle model automobile：
X (k+1)=Ax (k)+Bv (k) (1)
Wherein, x (k) is k moment automobile suspension system state variables, and x (k+1) is k+1 moment automobile suspension system states
Variable, v (k) are the road rumble at current time, and v (k) ∈ l2[0, ∞), A and B are respectively the normal square of system for being adapted to dimension
Battle array；
(2) each sampling model of onboard sensor 2 is established：
yi(k)=Cix(k)+Diωi(k) (2)
Wherein, i=1,2 ... N, N are the total number of onboard sensor 2, yi(k) i-th of onboard sensor of k moment 2 is represented
Output information, ωi(k) sampling noiset of i-th of onboard sensor of k moment 2, C are representedi、DiIt is the normal square of system for being adapted to dimension
Battle array；
(3) each wave filter exchange of information model in filter network unit is established：
Wherein,The exchange of information of i-th of wave filter is given for j-th of filter transfer of k moment,For the k moment
The estimated state of j-th of wave filter, ωij(k) communication channel between j-th of wave filter of k moment and i-th of wave filter is represented
Random perturbation, Eij、FijTo be adapted to the coefficient matrix of dimension, i=1,2 ... N, j=1,2 ... N, N are number of filter；
(4) trigger condition of event trigger in each wave filter is established respectively：
Wherein, σi、And εiFor i-th of event trigger threshold parameter, ΦiFor the first weight of i-th of event trigger
Matrix,For the second weight matrix of i-th of event trigger,For i-th of wave filter k moment
Estimated state,For the event generation time of i-th of wave filter,For i-th
The event generation time of wave filter,Ni
For that can transmit set of the exchange of information to all adjacent filters of i-th of wave filter, Ξ represents to ask square；
Trigger conditions are presented in the form of inequality, such as (4) formula, when a triggering condition is met, i.e. inequality (4) left side
Value when being more than or equal to the right, event can just occur, so, threshold parameter σi、And εiThe selection of value is just particularly important.It is excessive, εiToo small, trigger conditions are difficult to deviate, and cause filter effect poor；It is too small, εiExcessive, event occurs
Intensive, network bandwidth occupation rate is high, and resource consumption is big, and cost is high.In addition, the concrete operating principle on event trigger mechanism,
Design is as follows：From being carved at the beginning of test experiments, onboard sensor 2 and to be embedded in filtered sensor in wave filter 4 right respectively
The exchange of information of the status information of automobile suspension system 1 and the adjacent filter of setting carries out numerical sample according to cycle h.It is and false
When being located at the initial quarter of experiment, the event of each wave filter 4 occurs once, receives the phase from automobile suspension system 1 and setting
The initial value information of adjacent wave filter.On the basis of the value of initial time, in ensuing each sampling instant, local filter is embedded in
Event trigger 3 in ripple device all can be according to last event generation time receive information and local filter state information
Situation of change, come judge current sample time whether occur event triggering.If event occurs, local wave filter can just receive net
The current information that network transmission comes, update the variate-value of corresponding state in local filter model；If event does not occur, local filter
The state variable value related to automobile suspension system 1 and neighbours' wave filter keeps the value of last event time not in ripple device model
Become, until next time, event is arrived.Periodic event triggering sampling mechanism has merged periodic sampling and continuous events triggering Sampling Machine
System, it can effectively reduce data transfer number, save Internet resources, and and can ensures good filter effect.
(5) wave filter discretization model is established based on event trigger trigger condition：
Wherein i=1,2 ... N represent i-th of wave filter, and N is wave filter total number,Filtered for i-th for the k moment
Device estimated state,For i-th of wave filter estimated state of k+1 moment, HiFor the local gain of i-th of wave filter, KiI-th
The coupling gain of individual wave filter,Exist for i-th of wave filterThe state estimation at moment,For i-th
Individual wave filter existsThe output information at moment,ForJ-th of filter transfer of moment gives i-th of wave filter
Exchange of information；
(6) formula (1) and formula (5) are subtracted each other, and integrates and obtain filter network evaluated error model：
Wherein,For the evaluated error of i-th of wave filter of k moment,For i-th
Individual wave filter k moment evaluated error and event triggering momentThe difference of evaluated error,
For event triggering momentThe random perturbation of communication channel between j-th of wave filter and i-th of wave filter, FijIt is to be adapted to dimension
The normal matrix of degree；
(7) rememberWhole filter network evaluated error mould is obtained according to formula (7)
Type is：
Wherein, ξ (k)=[eT(k),δT(k),vT(k),ωT(k)]T, H=diag { H1,H2,…,HN, C=diag { C1,C2,…,CN, D=diag { D1,D2,…,
DN, K=diag { K1,K2,…,KN,
aijFor adjacency coefficient, i=1,2 ... N, j=1,2 ... N,
(8) there is H using the design of Liapunov stability analysis method to filter network evaluated error model∞Stability
Filter network constraint matrix；
(9) according to above-mentioned constraint matrix and given periodic event activation threshold value parameter σi、And εiCalculate wave filter
Each wave filter local gain H in networkiWith coupling gain Ki。
Described step (8) includes following sub-step：
(801) establishing Lyapunov Equation is：
V (e (k))=eT(k)Pe(k) (8)
Wherein, P is positive matrices；
(802) difference equation of Lyapunov Equation is solved, being solved using the method for Lyapunov's stability theorem second is made
Obtaining filter network system has H∞The constraint matrix of stability：
Wherein, γ is disturbance rejection Reduction Level,N is wave filter
Number, I are N × N-dimensional unit matrix, Φ=diag { Φ1,Φ2,…ΦN, L is filter network structure
Into graph structure Laplacian Matrix, IpUnit matrix is tieed up for p × p,ForTie up unit matrix,
Described filter network system has H∞Stability specifically meets the following steps：
(a) judge whether to consider its exterior disturbance, sensor measurement errors and interchannel noise, if performing step (b),
Otherwise step (c) is performed；
(b) filter network evaluated error meets：
limk→∞Ξ||ei(k)||2≤Mρk (11)
Wherein M and ρ is normal number, and Ξ represents to ask square, and k is discrete time；
(c) filter network evaluated error meets：
Wherein R is normal number,X (0) is the initial of automobile suspension system state
Value.
The constraint matrix obtained to step (802) linearizes, and is specially：
Make Xi=PiHi, Yi=PiKi, X=diag { X1,X2,…,XN, Y=diag { Y1,Y2,…,YN, by constraints
Matrixing is：
Wherein,P is positive definite matrix to be solved.
Described step (9) specifically includes following sub-step：
(901) given event activation threshold value parameter σi、And εiInitial value, and meetεi＞ 0, performs step
Suddenly (902)；
(902) by event trigger threshold parameter σi、And εiσ is entered as respectivelyi=σi+Δ1,εi=εi
+Δ3, and meetεi＞ 0, wherein Δ1、Δ2And Δ3Respectively σi、And εiIteration step length；
(903) by σi、εiSubstitute into inequality (13) and (14) and solve inequality, if without solution, return to step
(902), if there is solution, matrix X, Y and γ are obtained；
(904) the transfer rate ο of filter network data message is calculated, judges ο≤οopt,γ≤γoptWhether set up, wherein
ooptFor anticipatory data transfer rate, γ optFor expected interference rejection ratio, local gain H is calculated if setting upi=Pi -1Xi, coupling increasing
Beneficial Ki=Pi -1Xi, otherwise return to step (902).
Based on above Distributed filtering network system and its design method, by taking ChangAn Automobile as an example, devise is for ginseng
The vertical displacement of the accurate estimation vehicle model car body of ChangAn Automobile 1/4 and the Distributed filtering network system of speed state.Based on table 1
Parameter, by modeling and discretization, obtain the car discrete form mathematical model parameter of ChangAn Automobile 1/4：
B=[- 1.0970, -1.0591,5.4462, -
6.9661]T。
The ChangAn Automobile parameter list of table 1
| Variable | m1 m2 k1 k2 c T |
| Numerical value | 510 3886 1250000 279300 9224 0.2 |
| Unit | kg kg N/s N/s s |
Devised in the embodiment wave filter group of three strange lands distribution into filtering system, to estimate automotive suspension respectively
The status information of system, the embodiment devise a kind of emulation platform such as Fig. 3 of the Distributed filtering network based on ChangAn Automobile
Shown, figure median filter 4 is respectively the first wave filter, the second wave filter and the 3rd wave filter, it is assumed that system disturbance form is v
(k)=e-2k, the sampling noiset of i-th of onboard sensor of k moment 2 is ωi(k)=[e-ksin(k) e-kcos(2k)]T, i=1,
2,3, the interchannel noise of information transmission is ω between wave filterij(k)=N (0,1/k).By matlab emulation obtain one group it is optimal
Solution, the gain of wave filter local and coupling gain are respectively：
Emulation experiment is carried out to the emulation platform of above-mentioned design.Fig. 4 is the displacement curve comparison diagram of unsprung mass block, in figure
Transverse axis represents that simulation time is 20 step-lengths, and the longitudinal axis represents the displacement of unsprung mass block, and the figure depicts suspension system respectively
Output information and the curve using the output information of event triggering wave filter and the output information using periodic sampling wave filter
Figure, equally, Fig. 5 are the displacement curve comparison diagram of sprung mass block, and Fig. 6 is the rate curve comparison diagram of unsprung mass block, and Fig. 7 is
The rate curve comparison diagram of sprung mass block, complex chart 4~7 is visible, and the filter network system triggered using event can be fine
The status information of ground estimation suspension system, its effect are very nearly the same with using the green wave network effect of periodic sampling.Fig. 8 is use
The filter network state estimation error curve diagram of event triggering, transverse axis represent 100 simulation step lengths, and the longitudinal axis represents the margin of error, can
See that filter network system can estimate the status information of object well, and have various types of disturbances by strong robust
Property.Fig. 9, Figure 10, Figure 11 are respectively the timing diagram that three wave filters receive external information in Fig. 3, and longitudinal direction value is bigger, shows to trigger
Time interval is longer, more can effectively reduce data transfer number, reduces traffic load, complex chart 4~11 is visible, is touched based on event
While the filter network system of hair can ensure the preferably status information of estimation suspension system, additionally it is possible to effectively reduce data
Degree of transitivity, reduce traffic load.
Claims (6)
1. a kind of Distributed filtering network system, analyzed for 1/4 vehicle model automobile ride, it is characterised in that the system bag
Include：
Filter network unit：Wave filter (4) including being distributed in different physical sites, each wave filter (4) are carried out by network
Information exchange；
Sensor sample unit：Including onboard sensor (2) and filtered sensor, described onboard sensor (2) is embedded in vapour
Suspension system (1) simultaneously gathers automobile suspension system status information, and described filtered sensor is embedded in each wave filter (4) simultaneously
Gather the exchange of information of the wave filter (4) and the adjacent filter of setting；
Event trigger element：Including event trigger (3) corresponding with each wave filter (4), each event trigger (3) is based on the cycle
Event trigger mechanism, respective filter (4) event generation time is determined respectively；
In event generation time, wave filter (4) receive the automobile suspension system status information of corresponding onboard sensor (2) collection with
And the exchange of information of adjacent wave filter is set, otherwise the wave filter (4) is failure to actuate.
2. a kind of design method of Distributed filtering network system as claimed in claim 1, it is characterised in that this method includes
Following steps：
(1) discretization model of 1/4 vehicle model automobile is established：
X (k+1)=Ax (k)+Bv (k) (1)
Wherein, x (k) is k moment automobile suspension system state variables, and x (k+1) is k+1 moment automobile suspension system state variables,
V (k) is the road rumble at current time, and A and B are respectively the normal matrix of system for being adapted to dimension；
(2) each onboard sensor sampling model is established：
yi(k)=Cix(k)+Diωi(k) (2)
Wherein, i=1,2 ... N, N are onboard sensor total number, yi(k) the output letter of i-th of onboard sensor of k moment is represented
Breath, ωi(k) sampling noiset of i-th of onboard sensor of k moment, C are representedi、DiIt is the normal matrix of system for being adapted to dimension；
(3) each wave filter exchange of information model in filter network unit is established：
<mrow>
<msub>
<mover>
<mi>y</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mover>
<mi>&omega;</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,The exchange of information of i-th of wave filter is given for j-th of filter transfer of k moment,For j-th of k moment
The estimated state of wave filter,Represent that the random of the communication channel between j-th of wave filter of k moment and i-th of wave filter is disturbed
It is dynamic, Eij、FijTo be adapted to the coefficient matrix of dimension, i=1,2 ... N, j=1,2 ... N, N are number of filter；
(4) trigger condition of event trigger in each wave filter is established respectively：
Wherein, σi、And εiFor i-th of event trigger threshold parameter, ΦiFor the first weight matrix of i-th of event trigger,For the second weight matrix of i-th of event trigger, For i-th of wave filter k moment
Estimated state, For the event generation time of i-th of wave filter, Ni
For that can transmit set of the exchange of information to all adjacent filters of i-th of wave filter, Ξ represents to ask square；
(5) wave filter discretization model is established based on event trigger trigger condition：
<mrow>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>A</mi>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>H</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>(</mo>
<msubsup>
<mi>d</mi>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mi>i</mi>
</msubsup>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>i</mi>
</msub>
<mo>(</mo>
<msubsup>
<mi>d</mi>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mi>i</mi>
</msubsup>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>K</mi>
<mi>i</mi>
</msub>
<munder>
<mi>&Sigma;</mi>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>N</mi>
<mi>i</mi>
</msub>
</mrow>
</munder>
<mrow>
<mo>(</mo>
<msub>
<mover>
<mi>y</mi>
<mo>^</mo>
</mover>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>(</mo>
<msubsup>
<mi>d</mi>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mi>i</mi>
</msubsup>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mi>i</mi>
</msub>
<mo>(</mo>
<msubsup>
<mi>d</mi>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mi>i</mi>
</msubsup>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein i=1,2 ... N represent i-th of wave filter, and N is wave filter total number,For i-th of the wave filter estimation of k moment
State,For i-th of wave filter estimated state of k+1 moment, HiFor the local gain of i-th of wave filter, KiI-th of filter
The coupling gain of ripple device,Exist for i-th of wave filterThe state estimation at moment,For i-th of filter
Ripple device existsThe output information at moment,ForJ-th of filter transfer of moment is to i-th wave filter
Exchange of information；
(6) formula (1) and formula (5) are subtracted each other, and integrates and obtain filter network evaluated error model：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>e</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mrow>
<mo>(</mo>
<mi>A</mi>
<mo>-</mo>
<msub>
<mi>H</mi>
<mi>i</mi>
</msub>
<msub>
<mi>C</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>e</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>H</mi>
<mi>i</mi>
</msub>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>B</mi>
<mi>v</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>H</mi>
<mi>i</mi>
</msub>
<msub>
<mi>D</mi>
<mi>i</mi>
</msub>
<msub>
<mi>&omega;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mi>K</mi>
<mi>i</mi>
</msub>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msub>
<mi>N</mi>
<mi>i</mi>
</msub>
</mrow>
</munder>
<msub>
<mi>E</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>e</mi>
<mi>i</mi>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>e</mi>
<mi>j</mi>
</msub>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>-</mo>
<mo>(</mo>
<mrow>
<msub>
<mover>
<mi>e</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msub>
<mover>
<mi>e</mi>
<mo>&OverBar;</mo>
</mover>
<mi>j</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
<mo>)</mo>
<mo>-</mo>
<msub>
<mi>K</mi>
<mi>i</mi>
</msub>
<msub>
<mi>&Theta;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<msubsup>
<mi>d</mi>
<mrow>
<msub>
<mi>m</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mi>i</mi>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,For the evaluated error of i-th of wave filter of k moment,For i-th
Individual wave filter k moment evaluated error and event triggering momentThe difference of evaluated error, For event triggering momentThe random perturbation of communication channel between j-th of wave filter and i-th of wave filter, Fij
It is the normal matrix for being adapted to dimension；
(7) rememberObtaining whole filter network evaluated error model according to formula (7) is：
<mrow>
<mi>e</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>H</mi>
<mi>&xi;</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>K</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mi>a</mi>
</msub>
<mover>
<mi>e</mi>
<mo>&OverBar;</mo>
</mover>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>-</mo>
<mi>&Theta;</mi>
<mo>(</mo>
<msub>
<mi>d</mi>
<mi>k</mi>
</msub>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, ξ (k)=[eT(k),δT(k),vT(k),ωT(k)]T, H
=diag { H1,H2,…,HN, C=diag { C1,C2,…,CN, D=diag { D1,D2,…,DN, K=diag { K1,K2,…,
KN, aijIt is to be adjacent
Number, i=1,2 ... N, j=1,2 ... N,
(8) there is H using the design of Liapunov stability analysis method to filter network evaluated error model∞The filter of stability
The constraint matrix of ripple device network；
(9) according to above-mentioned constraint matrix and given periodic event activation threshold value parameter σi、And εiCalculate filter network
In each wave filter local gain HiWith coupling gain Ki。
A kind of 3. design method of Distributed filtering network system as claimed in claim 2, it is characterised in that described step
(8) following sub-step is included：
(801) establishing Lyapunov Equation is：
V (e (k))=eT(k)Pe(k) (8)
Wherein, P is positive matrices；
(802) difference equation of Lyapunov Equation is solved, is solved using the method for Lyapunov's stability theorem second and to filter
Wave network system has H∞The constraint matrix of stability：
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mover>
<mi>&Phi;</mi>
<mo>^</mo>
</mover>
</mtd>
<mtd>
<mrow>
<mn>2</mn>
<msup>
<mi>K</mi>
<mi>T</mi>
</msup>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>2</mn>
<mi>P</mi>
<mi>K</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mn>2</mn>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mi>&Pi;</mi>
</mtd>
<mtd>
<mrow>
<mn>2</mn>
<msup>
<mi>&Gamma;</mi>
<mi>T</mi>
</msup>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>2</mn>
<mi>&Gamma;</mi>
<mi>K</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<mn>2</mn>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>&le;</mo>
<mn>0</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein, γ is disturbance rejection Reduction Level,N is wave filter
Number, I are N × N-dimensional unit matrix, Φ=diag { Φ1,Φ2,…ΦN, L is filter network
The Laplacian Matrix of the graph structure of composition, IpUnit matrix is tieed up for p × p,ForTie up unit matrix,
A kind of 4. design method of Distributed filtering network system as claimed in claim 3, it is characterised in that described filtering
Network system has H∞Stability specifically meets the following steps：
(a) judge whether to consider its exterior disturbance, sensor measurement errors and interchannel noise, if performing step (b), otherwise
Perform step (c)；
(b) filter network evaluated error meets：
limk→∞Ξ||ei(k)||2≤Mρk (11)
Wherein M and ρ is normal number, and Ξ represents to ask square, and k is discrete time；
(c) filter network evaluated error meets：
<mrow>
<mi>&Xi;</mi>
<mo>&lsqb;</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mi>&infin;</mi>
</munderover>
<mi>&psi;</mi>
<mo>(</mo>
<mrow>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
<mo>)</mo>
<mo>&rsqb;</mo>
<mo>&le;</mo>
<mi>&gamma;</mi>
<mrow>
<mo>(</mo>
<mo>|</mo>
<mo>|</mo>
<mi>x</mi>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
<mo>|</mo>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mi>R</mi>
<mo>+</mo>
<mo>|</mo>
<mo>|</mo>
<mi>v</mi>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>|</mo>
<msubsup>
<mo>|</mo>
<mn>2</mn>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mo>(</mo>
<mrow>
<mo>|</mo>
<mo>|</mo>
<msub>
<mi>&omega;</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<msubsup>
<mo>|</mo>
<mn>2</mn>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mo>|</mo>
<mo>|</mo>
<msub>
<mi>&omega;</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<msubsup>
<mo>|</mo>
<mn>2</mn>
<mn>2</mn>
</msubsup>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein R is normal number,X (0) is the initial value of automobile suspension system state.
5. a kind of design method of Distributed filtering network system as claimed in claim 3, it is characterised in that to step
(802) constraint matrix obtained is linearized, and is specially：
Make Xi=PiHi, Yi=PiKi, X=diag { X1,X2,…,XN, Y=diag { Y1,Y2,…,YN, by constraint matrix
It is transformed to：
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mover>
<mi>&Phi;</mi>
<mo>^</mo>
</mover>
</mtd>
<mtd>
<mrow>
<mn>2</mn>
<msup>
<mi>Y</mi>
<mi>T</mi>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>2</mn>
<mi>Y</mi>
</mrow>
</mtd>
<mtd>
<mrow>
<mn>2</mn>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>&GreaterEqual;</mo>
<mn>0</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mi>&Pi;</mi>
</mtd>
<mtd>
<mrow>
<mn>2</mn>
<msup>
<mi>&Gamma;</mi>
<mo>&prime;</mo>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>2</mn>
<msup>
<mi>&Gamma;</mi>
<mrow>
<mo>&prime;</mo>
<mi>T</mi>
</mrow>
</msup>
</mrow>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<mn>2</mn>
<mi>P</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>&le;</mo>
<mn>0</mn>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>14</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,P is positive definite matrix to be solved.
A kind of 6. design method of Distributed filtering network system as claimed in claim 5, it is characterised in that described step
(9) following sub-step is specifically included：
(901) given event activation threshold value parameter σi、And εiInitial value, and meetεi＞ 0, perform step
(902)；
(902) by event trigger threshold parameter σi、And εiσ is entered as respectivelyi=σi+Δ1,εi=εi+Δ3,
And meetεi＞ 0, wherein Δ1、Δ2And Δ3Respectively σi、And εiIteration step length；
(903) by σi、εiSubstitute into inequality (13) and (14) and solve inequality, if without solution, return to step (902), if
There is solution, obtain matrix X, Y and γ；
(904) the transfer rate ο of filter network data message is calculated, judges ο≤οopt,γ≤γoptWhether set up, wherein oopt
For anticipatory data transfer rate, γoptFor expected interference rejection ratio, local gain H is calculated if setting upi=Pi -1Xi, coupling gain Ki
=Pi -1Xi, otherwise return to step (902).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610058593.6A CN105606381B (en) | 2016-01-28 | 2016-01-28 | A kind of Distributed filtering network system and design method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610058593.6A CN105606381B (en) | 2016-01-28 | 2016-01-28 | A kind of Distributed filtering network system and design method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105606381A CN105606381A (en) | 2016-05-25 |
| CN105606381B true CN105606381B (en) | 2017-12-26 |
Family
ID=55986492
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610058593.6A Expired - Fee Related CN105606381B (en) | 2016-01-28 | 2016-01-28 | A kind of Distributed filtering network system and design method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105606381B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107124158B (en) * | 2017-03-29 | 2020-07-28 | 同济大学 | Wireless sensor network filtering information processing system and method based on logarithmic quantization |
| CN107065545B (en) * | 2017-04-01 | 2020-03-24 | 同济大学 | Distributed event trigger filtering system based on Markov jump and design method |
| CN107247818A (en) * | 2017-04-27 | 2017-10-13 | 同济大学 | A kind of cloud aids in half car Active suspension condition estimating system and design method |
| CN107169193B (en) * | 2017-05-11 | 2020-07-07 | 南京师范大学 | Design method of nonlinear system filter based on self-adaptive event trigger mechanism |
| CN107329823B (en) * | 2017-05-16 | 2020-04-03 | 泉州味盛食品有限公司 | Flexible filtering device for triaxial acceleration data of motion detection |
| CN109410361A (en) * | 2018-11-02 | 2019-03-01 | 华东理工大学 | A kind of event triggering state estimating system based on Markov jump |
| CN110933056B (en) * | 2019-11-21 | 2022-07-08 | 博智安全科技股份有限公司 | Anti-attack multi-agent control system and method thereof |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| ITMI20071632A1 (en) * | 2007-08-06 | 2009-02-07 | Consiglio Nazionale Ricerche | APPARATUS FOR AFTERFLOWING INVESTIGATIONS AND METHODS OF OPTIMIZATION OF THE CONDITIONING CHAIN |
| US8341736B2 (en) * | 2007-10-12 | 2012-12-25 | Microsoft Corporation | Detection and dynamic alteration of execution of potential software threats |
| CN101367324B (en) * | 2008-10-15 | 2011-01-12 | 江苏大学 | Pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor |
| CN102189909A (en) * | 2010-03-11 | 2011-09-21 | 蒋丰璘 | Filtering control strategy for skyhook damping frequencies of semi-active suspension of vehicle |
| CN103778587B (en) * | 2012-10-18 | 2017-02-01 | 同济大学 | Process evolution theoretical model construction method based on Internet of vehicles large-scale network |
| CN103852271B (en) * | 2012-12-01 | 2017-02-08 | 中车青岛四方机车车辆股份有限公司 | High-speed train running gear fault diagnosis and remote monitoring system based on Internet of Things |
| CN104085265B (en) * | 2014-06-12 | 2016-01-20 | 江苏大学 | A kind of energy regenerative suspension self adaptation off-line Neural network inverse control system and method |
-
2016
- 2016-01-28 CN CN201610058593.6A patent/CN105606381B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN105606381A (en) | 2016-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105606381B (en) | A kind of Distributed filtering network system and design method | |
| CN106205114A (en) | A kind of Freeway Conditions information real time acquiring method based on data fusion | |
| CN103353923B (en) | Adaptive space interpolation method and system thereof based on space characteristics analysis | |
| CN107529651A (en) | A kind of urban transportation passenger flow forecasting and equipment based on deep learning | |
| CN109887282A (en) | A kind of road network traffic flow prediction technique based on level timing diagram convolutional network | |
| CN106878375A (en) | A kind of cockpit pollutant monitoring method based on distribution combination sensor network | |
| CN104765916A (en) | Dynamics performance parameter optimizing method of high-speed train | |
| CN101739822B (en) | Sensor network configuring method for regional traffic state acquisition | |
| CN103456167B (en) | Based on the good driving technology parameter acquiring method of critical area | |
| CN105740643A (en) | Self-adaptive PM<2.5>concentration speculating method based on city region grid | |
| EP3871938A1 (en) | Method and device for determining pavement rating, storage medium and automobile | |
| CN107197439A (en) | Wireless sensor network locating method based on matrix completion | |
| CN106525466B (en) | A kind of Braking System for Multiple Units critical component robust filtering method and system | |
| CN105894814A (en) | Joint optimization method and system for multiple traffic management and control measures in consideration of environmental benefits | |
| CN107994885A (en) | Distributed fused filtering method that is a kind of while estimating Unknown worm and state | |
| Barcelo et al. | Dynamic OD matrix estimation exploiting bluetooth data in urban networks | |
| CN106507275A (en) | A kind of robust Distributed filtering method and apparatus of wireless sensor network | |
| CN108490115A (en) | A kind of air quality method for detecting abnormality based on distributed online principal component analysis | |
| CN103279032A (en) | Robust convergence control method of heterogeneous multi-agent system | |
| CN109787699A (en) | A kind of wireless sensor network routing link trend prediction method based on interacting depth model | |
| CN107124158A (en) | Wireless sensor network filtering information processing system and method based on logarithmic quantization | |
| CN105721544A (en) | Inter-vehicle information sharing method and device based on contents | |
| CN103313386B (en) | Based on the radio sensing network method for tracking target of consistency on messaging right-value optimization | |
| CN105072582B (en) | The adaptive wireless sensor network passive type localization method of distance based on RSS distributions | |
| CN102074112A (en) | Time sequence multiple linear regression-based virtual speed sensor design method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171226 Termination date: 20210128 |