CN110377955A - Distributed set-membership filtering device design method based on event trigger mechanism - Google Patents
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Abstract
The distributed set-membership filtering device design method based on event trigger mechanism that the invention discloses a kind of, the event triggering rule in the present invention is that filter only can just receive the information of sensor passes in the case where trigger condition meets；The saturation problem of sensor is indicated with a kind of nonlinear function, by solving one group of Recursive Linear MATRIX INEQUALITIES, is determined adequate condition existing for the filter for making filtering error system meet given performance indicator, is finally optimized to distributed set-membership filtering device.So that the filtering performance of discrete nonlinear time_varying system is met Practical Project demand through the invention, substantially increases robustness, fault-tolerance, reliability, non-fragility and the Disturbance Rejection ability of whole network system.
Description
Technical field
The invention belongs to digital filter design techniques, and specifically a kind of distributed set-membership filtering device based on event trigger mechanism is set
Meter method.
Background technique
Currently, it is still Kalman filter technology that Distributed filtering is most typical.Kalman filter technology is usually only applicable in
In all known situation of model parameter and the statistical property of noise, however the situation is actually rare in systems in practice, this just makes
Kalman filter technology is obtained to be limited by very large in the application of engineering in practice.In addition, existing be based on wireless sensor
The Distributed filtering technical solution of network is transmitted just for the information under ecotopia and infinite network resource, is not accounted for
In Practical Project the problem of dropout caused by limited Internet resources.Meanwhile each of network system sensor section
Point is at each moment there are information exchange, and the resource efficiency of Distributed filtering is lower in sensor network system.
Summary of the invention
It is an object of the invention to propose a kind of distributed set-membership filtering device design method based on event trigger mechanism.
Realize technical solution of the invention are as follows: a kind of distributed set-membership filtering device design based on event trigger mechanism
Method, specific steps are as follows:
Step 1 establishes discrete nonlinear time_varying system mathematical model under sensor constraint of saturation；
Step 2 is established according to the discrete nonlinear time_varying system mathematical model under sensor constraint of saturation based on event
Distributed set-membership filtering device；
Step 3, the design object and filtering error matrix for determining distributed set-membership filtering device parameter；
Step 4 determines noise, trigger condition, nonlinear function and sensor constraint of saturation function；
Step 5 determines the distributed set-membership filtering device parameter of solution according to nonlinear function and sensor constraint of saturation function
Linear matrix inequality；
Step 6 optimizes the distributed set-membership filtering device of determining parameter.
Preferably, the discrete nonlinear time_varying system mathematical model under the sensor constraint of saturation that step 1 is established are as follows:
Wherein, N indicates the number of sensor nodes in wireless sensor network,Indicate state vector,Indicate the measurement output of sensor i, A (k), B (k), Ci(k), DiIt (k) is the known reality with different dimensions respectively
Time-varying matrix,It is process noise,It is measurement noise, meets following constraint set:
Wherein, W (k) > 0 and V (k) > 0 is the known positive definite matrix with different dimensions respectively；
G (x (k)) is the nonlinear function for meeting following condition:
(g(x(k))-U1x(k))T(g(x(k))-U2x(k))≤0 (3)
Wherein, U1, U2It is to meet U2-U1The real matrix of > 0, and g () belongs to [U1, U2]。
κ () is sensor constraint of saturation function, specifically:
κ ()=[κ1(y(1))κ2(y(2))…κm(y(m))] (4)
Wherein,y(s)Indicate s-th of component of vector y.Therefore the κ in (1) formula
(Ci(k) x (k)) it is written as follow form:
Wherein, G1iAnd G2iIt is diagonal matrix, and meets 0≤G1i< I≤G2i；Work as U1=0, and U2=Gi=G2i-G2iWhen,
Preferably, distributed set-membership filtering device structural model of the foundation of step 2 based on event are as follows:
Wherein, Fi(k) (i, j ∈ V) is the iteration undated parameter of filter, Hij(k) (i, j ∈ V) is that filter measures more
New parameter, ri(k) it is renewal sequence, is embodied as:
Indicate node i the k moment (t+1) secondary event triggering at the time of, specifically:
Wherein, Ωi(k) > 0 (i=1,2 ..., N) indicates sensor node i in the threshold matrix of moment k.Meetei(k) it is once triggered more for last triggering with newest
The difference of new sequence, is defined as:
Preferably, the filter parameter { F that step 3 determinesi(k)}k≥0, { Hij(k)k≥0Design object are as follows: filter ginseng
Number { Fi(k)}k≥0, { Hij(k)}k≥0So that following condition is set up:
Wherein, PkDescribe the size and Orientation of spheroid.
Determine filtering error matrixMethod particularly includes:
Determine the discrete nonlinear time_varying system initial value under sensor constraint of saturation, it may be assumed that under given sensor constraint of saturation
Discrete nonlinear time_varying system state initial value x (0) and filter initial valueMeet condition:
Wherein, P (0) > 0 is known positive definite matrix；
Determine filtering error matrix
Wherein AndWork as θij=0 andWhen,It is expressed as
Wherein,IfNiFor node i ∈ V itself and
The set of its all neighbors, is defined as
It is set up according to (11) formula:
Wherein, P (k)=Q (k) QT(k),And | | zi(k)||≤1。
(15) formula is further write as:
Wherein,
According to formula (15) (16) and formula (13), filtering error matrix is further obtainedSpecifically:
(17) after formula is transformed, the filtering error matrix of the form of following matrix multiple is obtained:
Wherein, η (k) is
Preferably, step 4 determines noise, trigger condition, nonlinear function and sensor constraint of saturation function detailed process
Are as follows:
η (k) form of noise, trigger condition is obtained according to formula (2) (9) (15) and (19):
Wherein,
The nonlinear function for meeting following condition is obtained according to formula (15) (3) and (19):
Wherein,
The sensor constraint of saturation function of following form can be obtained according to formula (15) and (6):
Preferably, step 5 determines the linear matrix inequality for solving distributed set-membership filtering device parameter specifically: given three
Tuple (G, { ΩI, k, { Pk), if there is real matrix sequenceWithAnd non-negative sequence of scalarsMeet following linear MATRIX INEQUALITIES, it can
Obtain the parameter { F of distributed set-membership filtering devicei(k)}k≥0, { Hij(k)}k≥0:
Wherein,
Preferably, step 6 pair determines what the distributed set-membership filtering device of parameter optimized method particularly includes:
Middle parameter P in formula (11)kThe size and Orientation comprising filtering estimated value spheroid is described, to make the spheroid
Minimum, with the tool box Yalmip in Matlab, by Semidefinite Programming method, from the angle solution matrix (23) of trace of a matrix,
That is: known (G, { Ωi(k) } it), solves the problems, such as follows:
Then obtain { P (k) }k≥0Minimum value；
Ω in formula (9)i(k) sensor node i is indicated in the threshold matrix of moment k, to trigger threshold highest, fortune
Threshold matrix { Ω is maximized from the angle of trace of a matrix in conjunction with the tool box Yalmip with chaos optimization methodi(k)}k≥0, it may be assumed that
Know (G, { P (k) }), solve the problems, such as follows:
Wherein, βi> 0 (i=1,2 ..., N) is weight scalar, and is met For non-negative scalar,
Preferably, with chaos optimization method, in conjunction with the tool box Yalmip, threshold matrix is maximized from the angle of trace of a matrix
{Ωi(k)}k≥0Method particularly includes:
Determine Chaos VariableSection:
It is obtained according to formula (25)
According to formula (23) and formula (25), obtain
Wherein,
According to∈(3)(k) >=0, ∈(4)(k) >=0 and (27) formula obtains Chaos Variable's
Section are as follows:
It solvesMaximum value, solve the problems, such as follows:
Obtain section
Wherein,ForOptimal value；
According to iterative chaotic map:
Wherein, ρ (τ) ∈ [- 1,0) ∪ (0,1] is Chaos Variable, and α > 0 be the parameter selected, τ (τ=0,1,2 ...) be
Iteration factor obtains Chaos VariableExpression are as follows:
Wherein,It is obtained by the τ times iteration；
It is obtained according to the τ times iterationBy Semidefinite Programming method solution matrix equation (25), formula can be solved
(24) threshold matrix { Ω ini(k)}k≥0Maximized problem.
Compared with prior art, the present invention its remarkable advantage are as follows:
(1) the present invention is based on carry out mathematics to nonlinear system there are the wireless network environment of unknown but bounded noise to build
Mould, it is practical without knowing the statistical property of model parameter and noise；
(2) event trigger mechanism is added in the present invention, and the transmission of redundant signals can be reduced while keeping system performance,
Signal can effectively be transmitted under limited Internet resources.
Further detailed description is done to the present invention below.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Fig. 2 is system state variables x1The filter estimated result figure designed with the present invention.
Fig. 3 is system mode x2The filter estimated result figure designed with the present invention.
Fig. 4 is the filter of the invention designed to system mode x1Evaluated error result figure.
Fig. 5 is the filter of the invention designed to system mode x2Evaluated error result figure.
Specific embodiment
In sensor network, N number of sensor is distributed in space according to topological structure described in orientation diagram G=(V, E, L)
On, wherein V={ 1,2 ... N } indicates the set of sensor node,Indicate the set at edge, L=[θij]N×NTable
Show non-negative adjacency matrix relevant to edge in orientation diagram, it may be assumed that θij> 0 and if only if edge (i, j) ∈ E indicate sensor j with
Sensor i has information transmitting, if (i, j) ∈ E, j are referred to as the neighbors of i.For all i ∈ V, it is assumed that θii=1, (i,
It i) is 1 additional edge.
A kind of distributed set-membership filtering device design method based on event trigger mechanism, specific steps are as follows:
Step 1 establishes discrete nonlinear time_varying system mathematical model under sensor constraint of saturation, specifically:
Wherein,Indicate state vector,Indicate the measurement output of sensor i, A (k), B (k), Ci
(k), DiIt (k) is the known real-time bending moment battle array with different dimensions respectively,It is process noise,It is measurement
Noise meets following constraint set:
Wherein, W (k) > 0 and V (k) > 0 is the known positive definite matrix with different dimensions respectively, describes unknown but has
The size and Orientation of boundary's noise.
G (x (k)) is the nonlinear function for meeting following condition:
(g(x(k))-U1x(k))T(g(x(k))-U2x(k))≤0 (3)
Wherein, U1, U2It is to meet U2-U1The real matrix of > 0, and g () belongs to [U1, U2]。
κ () is sensor constraint of saturation function, specifically:
κ ()=[κ1(y(1))κ2(y(2))…κm(y(m))] (4)
Wherein,y(s)Indicate s-th of component of vector y.Therefore the κ in (1) formula
(Ci(k) x (k)) form can be written as follow:
Wherein, G1iAnd G2iIt is diagonal matrix, and meets 0≤G1i< I≤G2i；Work as U1=0, and U2=Gi=G2i-G2iWhen,
Step 2 is established according to the discrete nonlinear time_varying system mathematical model under sensor constraint of saturation based on event
Distributed set-membership filtering device, specifically:
Wherein, Fi(k) (i, j ∈ V) is the iteration undated parameter of filter, Hij(k) (i, j ∈ V) is that filter measures more
New parameter.ri(k) it is renewal sequence, is embodied as:
Indicate node i the k moment (t+1) secondary event triggering at the time of, specifically:
Wherein, Ωi(k) > 0 (i=1,2 ..., N) indicates sensor node i in the threshold matrix of moment k.Meetei(k) it is once triggered more for last triggering with newest
The difference of new sequence, is defined as:
Step 3 determines that the design object of distributed set-membership filtering device parameter and filtering error matrix determine filter parameter
{Fi(k)}k≥0, { Hij(k)}k≥0Design object, it may be assumed that so that following condition set up:
Wherein, PkDescribe the size and Orientation of spheroid.
Determine the initial value of discrete nonlinear time_varying system, it may be assumed that give the state initial value x (0) of discrete nonlinear time_varying system
With the initial value of filterMeet condition:
Wherein, P (0) > 0 is known positive definite matrix.
Determine filtering error matrix
Wherein AndWork as θij=0 andWhen,It can be expressed as
Wherein,IfNiFor node i ∈ V itself and
The set of its all neighbors, is defined as
It is set up according to (11) formula:
Wherein, P (k)=Q (k) QT(k),And | | zi(k)||≤1。
(15) formula can further be write as:
Wherein,
According to formula (15) (16) and formula (13), filtering error matrix is further obtainedSpecifically:
(17) after formula is transformed, the filtering error matrix of the form of following matrix multiple is obtained:
Wherein, η (k) is
Step 4 determines noise, trigger condition, nonlinear function and sensor constraint of saturation function；
η (k) form of noise, trigger condition can be obtained according to formula (2) (9) (15) and (19):
Wherein,
The nonlinear function for meeting following condition can be obtained according to formula (15) (3) and (19):
Wherein,
The sensor constraint of saturation function of following form can be obtained according to formula (15) and (6):
Step 5 determines the distributed set-membership filtering device parameter of solution according to nonlinear function and sensor constraint of saturation function
Linear matrix inequality, it may be assumed that given triple (G, { ΩI, k, { Pk), if there is real matrix sequenceWithAnd non-negative sequence of scalars
Meet following linear MATRIX INEQUALITIES, then, the parameter { F of filter can be obtainedi(k)}k≥0, { Hij(k)}k≥0:
Wherein,
At this point, the distributed collection person designed has met design object, specific proof procedure are as follows:
Utilize Schur Complement lemma: given constant matrices S1, S2And S3, wherein
SoAnd if only if
Then formula (23) is equivalent to:
With S-procedure lemma:
ψ0(), ψ1() ..., ψp() is variableQuadratic function,
AndIf there is ∈1>=0 ..., ∈p>=0 makesSo, equivalently,
Then (24) formula can be written as follow form:
After transposition, (25) formula is further write as:
According to formula (19), (26) formula be can be written as:
According in formula (13)Definition, formula (27) is further written as follow form:
So far, filter design object Δi(k)≤1 it sets up.
Step 6 optimizes the distributed set-membership filtering device of determining parameter.
To keep the spheroid minimum, with the tool box Yalmip in Matlab, by Semidefinite Programming method, from trace of a matrix
Angle solution matrix (23), it may be assumed that known (G, { Ωi(k) } it), solves the problems, such as follows:
Then obtain { P (k) }k≥0Minimum value, it may be assumed that { P (k) }k≥0Minimization problem is resolved.
Ω in formula (9)i(k) sensor node i is indicated in the threshold matrix of moment k, to trigger threshold highest.Fortune
With chaos optimization method (see step 2-8 to step 2-11), in conjunction with the tool box Yalmip, threshold value is maximized from the angle of trace of a matrix
Matrix { Ωi(k)}k≥0, it may be assumed that known (G, { P (k) }) is solved the problems, such as follows:
Wherein, βi> 0 (i=1,2 ..., N) is weight scalar, and is met For non-negative scalar,
With chaos optimization method, in conjunction with the tool box Yalmip, threshold matrix { Ω is maximized from the angle of trace of a matrixi
(k)}k≥0Method particularly includes:
Determine Chaos VariableSection:
It is obtained according to formula (30)
According to formula (23) and formula (30), obtain
Wherein,
According to∈(3)(k) >=0, ∈(4)(k) >=0 and (32) formula obtains Chaos Variable's
Section are as follows:
It solvesMaximum value, solve following problem:
Obtain section
Wherein,ForOptimal value.
According to following iterative chaotic map:
Wherein, ρ (τ) ∈ [- 1,0) ∪ (0,1] is Chaos Variable, and α > 0 be the parameter selected, τ (τ=0,1,2 ...) be
Iteration factor.
Obtain Chaos VariableExpression are as follows:
Wherein,It is obtained by the τ times iteration.
It is obtained according to the τ times iterationBy Semidefinite Programming method solution matrix equation (30), formula can be solved
(29) threshold matrix { Ω ini(k)}k≥0Maximized problem.
Verify the validity of the linear matrix inequality of distributed set-membership filtering device parameter:
Verification result is as shown in Figure 2-5, and Fig. 2 gives system state variables x1(k) and filter node 1-3 estimates it
Evaluation；Fig. 3 gives system state variables x2(k) and filter node 1-3 is to its estimated value；Filter is set forth in Fig. 4,5
Evaluated error of the wave device node 1-3 to system variable.Simulation result surface error in the reasonable scope, further illustrates this hair
The validity of bright proposed filter design method.
In conclusion the present invention under sensor constraint of saturation, for a kind of Nonlinear Discrete Time-Varying Systems, provides one kind
The design method of distributed set-membership filtering device based on event trigger mechanism.Event triggering rule in the present invention is filter
The information of sensor passes can just be received in the case where trigger condition meets；One quasi-nonlinear letter of the saturation problem of sensor
Number indicates, by solving one group of Recursive Linear MATRIX INEQUALITIES, determines the filter for making filtering error system meet given performance indicator
Adequate condition existing for wave device finally optimizes distributed set-membership filtering device.Filter designed by the present invention has good
Good mapping, meanwhile, it is capable to which dissolving sensor in sensor network saturation problem influence caused by filtering performance occurs.
Claims (8)
1. a kind of distributed set-membership filtering device design method based on event trigger mechanism, which is characterized in that specific steps are as follows:
Step 1 establishes discrete nonlinear time_varying system mathematical model under sensor constraint of saturation；
Step 2 establishes the distribution based on event according to the discrete nonlinear time_varying system mathematical model under sensor constraint of saturation
Formula set-membership filtering device；
Step 3, the design object and filtering error matrix for determining distributed set-membership filtering device parameter；
Step 4 determines noise, trigger condition, nonlinear function and sensor constraint of saturation function；
Step 5 determines the line for solving distributed set-membership filtering device parameter according to nonlinear function and sensor constraint of saturation function
Property MATRIX INEQUALITIES；
Step 6 optimizes the distributed set-membership filtering device of determining parameter.
2. the distributed set-membership filtering device design method according to claim 1 based on event trigger mechanism, feature exist
Discrete nonlinear time_varying system mathematical model under the sensor constraint of saturation that, step 1 is established are as follows:
Wherein, N indicates the number of sensor nodes in wireless sensor network,Indicate state vector,
Indicate the measurement output of sensor i, A (k), B (k), Ci(k), DiIt (k) is the known real-time bending moment battle array with different dimensions respectively,It is process noise,It is measurement noise, meets following constraint set:
Wherein, W (k) > 0 and V (k) > 0 is the known positive definite matrix with different dimensions respectively；
G (x (k)) is the nonlinear function for meeting following condition:
(g(x(k))-U1x(k))T(g(x(k))-U2x(k))≤0 (3)
Wherein, U1, U2It is to meet U2-U1The real matrix of > 0, and g () belongs to [U1, U2]。
κ () is sensor constraint of saturation function, specifically:
κ ()=[κ1(y(1)) k2(y(2)) … κm(y(m))] (4)
Wherein,y(s)Indicate s-th of component of vector y.Therefore κ (the C in (1) formulai(k)x
(k)) it is written as follow form:
Wherein, G1iAnd G2iIt is diagonal matrix, and meets 0≤G1i< I≤G2i；Work as U1=0, and U2=Gi=G2i-G2iWhen,
3. the distributed set-membership filtering device design method according to claim 1 based on event trigger mechanism, feature exist
In the foundation of step 2 is based on the distributed set-membership filtering device structural model of event are as follows:
Wherein, Fi(k) (i .j ∈ V) is the iteration undated parameter of filter, Hij(k) (i, j ∈ V) is that filter measures update ginseng
Number, ri(k) it is renewal sequence, is embodied as:
Indicate node i the k moment (t+1) secondary event triggering at the time of, specifically:
Wherein, Ωi(k) > 0 (i=1,2 ..., N) indicates sensor node i in the threshold matrix of moment k.
MeetThe difference with the newest renewal sequence once triggered is triggered for the last time,
Is defined as:
4. the distributed set-membership filtering device design method according to claim 1 based on event trigger mechanism, feature exist
In the filter parameter { F that step 3 determinesi(k)}k≥0, { Hij(k)}k≥0Design object are as follows: filter parameter { Fi(k)}k≥0,
{Hij(k)}k≥0So that following condition is set up:
Wherein, PkDescribe the size and Orientation of spheroid.
Determine filtering error matrixMethod particularly includes:
Determine the discrete nonlinear time_varying system initial value under sensor constraint of saturation, it may be assumed that under given sensor constraint of saturation from
Dissipate the initial value of state initial value x (0) and filter of nonlinear time_varying systemMeet condition:
Wherein, P (0) > 0 is known positive definite matrix；
Determine filtering error matrix
Wherein AndWork as θij=0 andWhen,It is expressed as
Wherein,NiFor node i ∈ V itself and its own
The set of neighbors, is defined as
It is set up according to (11) formula:
Wherein, P (k)=Q (k) QT(k),And | | zi(k)||≤1。
(15) formula is further write as:
Wherein,
According to formula (15) (16) and formula (13), filtering error matrix is further obtainedSpecifically:
(17) after formula is transformed, the filtering error matrix of the form of following matrix multiple is obtained:
Wherein, η (k) is
5. the distributed set-membership filtering device design method according to claim 4 based on event trigger mechanism, feature exist
In step 4 determines noise, trigger condition, nonlinear function and sensor constraint of saturation function detailed process are as follows:
η (k) form of noise, trigger condition is obtained according to formula (2) (9) (15) and (19):
Wherein,
The nonlinear function for meeting following condition is obtained according to formula (15) (3) and (19):
Wherein,
The sensor constraint of saturation function of following form can be obtained according to formula (15) and (6):
6. the distributed set-membership filtering device design method according to claim 1 based on event trigger mechanism, feature exist
The linear matrix inequality for solving distributed set-membership filtering device parameter is determined in, step 5 specifically: given triple (G,
{ΩI, k, { Pk), if there is real matrix sequenceWithAnd non-negative sequence of scalarsMeet following linear MATRIX INEQUALITIES, it can
Obtain the parameter { F of distributed set-membership filtering devicei(k)}k≥0, { Hij(k)}k≥0:
Wherein,
7. the distributed set-membership filtering device design method according to claim 1 based on event trigger mechanism, feature exist
In step 6 pair determines what the distributed set-membership filtering device of parameter optimized method particularly includes:
Middle parameter P in formula (11)kThe size and Orientation comprising filtering estimated value spheroid is described, to keep the spheroid minimum,
With the tool box Yalmip in Matlab, by Semidefinite Programming method, from the angle solution matrix (23) of trace of a matrix, it may be assumed that
Know (G, { Ωi(k) } it), solves the problems, such as follows:
Then obtain { P (k) }k≥0Minimum value；
Ω in formula (9)i(k) sensor node i is indicated in the threshold matrix of moment k, to trigger threshold highest, with mixed
Ignorant optimization method maximizes threshold matrix { Ω from the angle of trace of a matrix in conjunction with the tool box Yalmipi(k)}k≥0, it may be assumed that known (G,
{ P (k) }), it solves the problems, such as follows:
Wherein, βi> 0 (i=1,2 ..., N) is weight scalar, and is met For non-negative scalar,
8. the distributed set-membership filtering device design method according to claim 7 based on event trigger mechanism, feature exist
In with chaos optimization method, in conjunction with the tool box Yalmip, from the angle of trace of a matrix maximization threshold matrix { Ωi(k)}k≥0's
Method particularly includes:
Determine Chaos VariableSection:
It is obtained according to formula (25)
According to formula (23) and formula (25), obtain
Wherein,
According to∈(3)(k) >=0, ∈(4)(k) >=0 and (27) formula obtains Chaos VariableSection
Are as follows:
It solvesMaximum value, solve the problems, such as follows:
Obtain section
Wherein,ForOptimal value；
According to iterative chaotic map:
Wherein, ρ (τ) ∈ [- 1,0) ∪ (0,1] is Chaos Variable, and α > 0 be selected parameter, and τ (τ=0,1,2 ...) is iteration
The factor obtains Chaos VariableExpression are as follows:
Wherein,It is obtained by the τ times iteration；
It is obtained according to the τ times iterationBy Semidefinite Programming method solution matrix equation (25), formula (24) can be solved
Middle threshold matrix { Ωi(k)}k≥0Maximized problem.
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| CN112698573A (en) * | 2020-12-28 | 2021-04-23 | 杭州电子科技大学 | Networked system non-fragile event trigger control method based on positive switching system modeling |
| CN112731937A (en) * | 2020-12-29 | 2021-04-30 | 苏州科技大学 | Design method of event-triggered vehicle queue control system containing noise interference |
| CN113110363A (en) * | 2021-05-24 | 2021-07-13 | 齐齐哈尔大学 | Method for designing non-fragile fuzzy filter of network system based on event-driven strategy |
| CN116431981A (en) * | 2022-12-07 | 2023-07-14 | 哈尔滨理工大学 | Distributed group member filtering method based on mobile robot positioning system |
-
2019
- 2019-06-19 CN CN201910532006.6A patent/CN110377955A/en not_active Withdrawn
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112698573A (en) * | 2020-12-28 | 2021-04-23 | 杭州电子科技大学 | Networked system non-fragile event trigger control method based on positive switching system modeling |
| CN112698573B (en) * | 2020-12-28 | 2022-05-20 | 杭州电子科技大学 | Networked system non-fragile event trigger control method based on modeling of tangent switching system |
| CN112731937A (en) * | 2020-12-29 | 2021-04-30 | 苏州科技大学 | Design method of event-triggered vehicle queue control system containing noise interference |
| CN112731937B (en) * | 2020-12-29 | 2022-06-03 | 苏州科技大学 | Design method of event-triggered vehicle queue control system containing noise interference |
| CN113110363A (en) * | 2021-05-24 | 2021-07-13 | 齐齐哈尔大学 | Method for designing non-fragile fuzzy filter of network system based on event-driven strategy |
| CN116431981A (en) * | 2022-12-07 | 2023-07-14 | 哈尔滨理工大学 | Distributed group member filtering method based on mobile robot positioning system |
| CN116431981B (en) * | 2022-12-07 | 2023-09-29 | 哈尔滨理工大学 | Distributed group member filtering method based on mobile robot positioning system |
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Application publication date: 20191025 |