CN107247818A - A kind of cloud aids in half car Active suspension condition estimating system and design method - Google Patents
A kind of cloud aids in half car Active suspension condition estimating system and design method Download PDFInfo
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Abstract
Half car Active suspension condition estimating system and design method are aided in the present invention relates to a kind of cloud, the system includes：Sensor sample unit：Onboard sensor including being distributed in half car Active suspension front wheel suspension and rear wheel suspension；Event trigger element：Include the event trigger of 2 onboard sensors for being connected respectively front wheel suspension and rear wheel suspension；Filter cell：Filter cell is arranged on Cloud Server, and filter cell includes 2 wave filters for connecting 1 event trigger by network correspondence respectively；The onboard sensor of front wheel suspension and rear wheel suspension is connected to wave filter 2 independent state estimation subsystems of formation by 1 event trigger respectively in the system, and 2 wave filters carry out state estimation by double of car Active suspension of sample information of the sensor sent from event trigger respectively.Compared with prior art, the present invention can realize the reliable estimation of vehicle active suspension state, and automobile making expense can be reduced again and mitigates network service burden.
Description
Technical field
The present invention relates to a kind of half car Active suspension condition estimating system and design method, aided in more particularly, to a kind of cloud
Half car Active suspension condition estimating system and design method.
Background technology
With the fast development of automobile industry, requirement more and more higher of the people to automotive performance.Automobile suspension system is with changing
Kind car steering comfortableness and security are closely related.Automobile suspension system generally has following three class：Passive suspension system, half master
Dynamic suspension system and active suspension system.Compared to passive and semi-active suspension, active suspension system is for reducing road surface input
Influence to body of a motor car has very big potentiality.Therefore, active suspension system receives the concern of domestic and international expert.
The automobile of one normally travel generally requires the hundreds of embedded electronic device for having and calculating with storage capacity
Part, and these electronic devices increase the cost of automobile during design and manufacture.Cloud computing has unlimited storage energy
Power and computing capability, if automobile can borrow the memory space and server rather than design and manufacture embedded-type electric of cloud computing
Sub- device, then the cost of automobile making will be reduced.Under the automobile frame based on cloud service, the local sensor of automobile will
The information collected is sent to cloud computing platform by network.Cloud computing platform provides corresponding control letter based on automobile information
Number, control signal sends local vehicle to by network, so as to improve the performance of automobile.
In the automotive system based on cloud computing service, all devices are by real-time performance information sharing, but Netowrk tape
The wide limited network blockage phenomenon that causes happens occasionally.The blocking of network can lure the generation of network delay and packet loss, and then
Influence the performance of automotive system.In order to reduce the transmission bandwidth of network, mainly there is two ways at present：Time triggered device and thing
Part triggering device.Time triggered device periodically takes system information, therefore the size in sampling period can be to automotive system
Performance has a great impact.Sampling period, too small sample frequency will increase, and more packet can be sent to communication network, make
Into network blockage, and then lure network transfer delay and packet loss so that the performance of automotive system is affected.Sampling period is too big
Sample frequency can be reduced, and the performance of automotive system can also be affected, therefore one suitable sampling period of selection is very heavy
Want.Sampling period is chosen generally according to the conventional experience of people, and this has resulted in the difficulty of sampling period selection.It is different from
Traditional time triggered device, event triggering device is staged by demand, i.e., only when prior designed trigger conditions
When being met, event triggering device can just discharge sampled value, otherwise, and sampled value will be dropped.Therefore event triggering
Device can not only inherently reduce the transmission burden of network, moreover it is possible to ensure the performance of automobile.
The accuracy of automobile information collection is to improve the basis of automotive performance.Traditional Kalman filter is for white Gaussian
Noise has good effect, but the adaptability for random noise is not strong.And in the wireless network in addition to it there is white noise,
It also there is random noise.Therefore a kind of filtering system how is designed, the accuracy for improving data message is also urgently to be resolved hurrily
One problem.
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 a kind of cloud aids in half car owner
Dynamic suspension condition estimating system and design method.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of cloud aids in half car Active suspension condition estimating system, and the system includes：
Sensor sample unit：Onboard sensor including being distributed in half car Active suspension front wheel suspension and rear wheel suspension；
Event trigger element：Including 2 event triggers, 2 event triggers are connected respectively front wheel suspension with after
Take turns the onboard sensor of suspension；
Filter cell：Described filter cell is arranged on Cloud Server, and filter cell includes 2 wave filters, 2
Individual wave filter passes through network correspondence 1 event trigger of connection respectively；
The onboard sensor of front wheel suspension and rear wheel suspension is connected to filtering by 1 event trigger respectively in the system
Device 2 independent state estimation subsystems of formation, 2 wave filters are respectively by the sampling of the sensor sent from event trigger
Double of car Active suspension of information carries out state estimation.
The trigger condition of event trigger is：
Wherein,For the kth time event triggering moment of i-th of subsystem,For the kth time of i-th of subsystem
Jth time sampling instant after event triggering moment,After the kth time event triggering moment of i-th of subsystem
Jth time sampled value,Sent for the kth time event triggering moment of i-th of subsystem to the sample information of wave filter, ΦiFor
The weighting matrix of event trigger, δ in i-th of state estimation subsystemiFor event trigger in i-th of state estimation subsystem
Given event triggering threshold, i=1,2.
Described wave filter is distribution H∞Wave filter.
A kind of cloud aids in the design method of half car Active suspension condition estimating system, and this method comprises the following steps：
(1) build cloud described above and aid in half car Active suspension condition estimating system；
(2) kinetics equation of half car Active suspension in 2 state estimation subsystems is set up；
(3) trigger condition of the event trigger in 2 state estimation subsystems is determined；
(4) the wave filter kinetic model triggered based on event is set up to 2 state estimation subsystems；
(5) designing filter parameter according to two kinetics equations of step (2) and step (4) causes wave filter to have H∞
Characteristic.
The kinetics equation of half car Active suspension is in i-th of state estimation subsystem in step (2)：
Wherein,The state of half car Active suspension becomes in respectively i-th and j-th of state estimation subsystem
Amount, i=1,2, j=1,2, and i ≠ j, wi(t) it is road surface disturbance input in i-th of state estimation subsystem,For i-th
The sample information of sensor, z in state estimation subsystemi(t) half car owner to be estimated in i-th of state estimation subsystem is represented
The state variable of dynamic suspension, Ai、Bi、Ci、LiFor constant matrices, LiFor unit battle array, HijFor j-th pair of state estimation subsystem pair
The influence matrix of i-th of state estimation subsystem.
The trigger condition of event trigger is in step (3)：
Wherein,For the kth time event triggering moment of i-th of subsystem,For the kth time of i-th of subsystem
Jth time sampling instant after event triggering moment,For the after the kth time event triggering moment of i-th of subsystem
J sampled value,Sent for the kth time event triggering moment of i-th of subsystem to the sample information of wave filter, ΦiFor
The weighting matrix of event trigger, δ in i state estimation subsystemiGiven for event trigger in i-th of state estimation subsystem
Fixed event triggering threshold, i=1,2.
The wave filter kinetic model of 2 state estimation subsystems is in step (4)：
Wherein, i=1,2,For the state variable of i-th of state estimation subsystem median filter,For i-th of state
Estimate the state estimation of half car Active suspension of subsystem median filter output,For in i-th of state estimation subsystem
The sample information for the sensor that wave filter is received, Afi、BfiAnd LfiIt is the gain square of i-th of state estimation subsystem median filter
Battle array.
Step (4) is specially：
(401) according to the kinetics equation and wave filter dynamics of half car Active suspension in i-th of state estimation subsystem
Model sets up the evaluated error model of i-th of state estimation subsystem median filter：
Wherein, τi(t) represent i-th
Network transfer delay in state estimation subsystem；
(402) constraints of evaluated error model is determined；
(403) being set up using Liapunov stability analytic approach causes error model to meet constraint bar in step (402)
The MATRIX INEQUALITIES of part；
(404) solution matrix inequality obtains design parameter in each state estimation subsystem, including filter gain square
Battle array Afi、BfiAnd LfiAnd the weighting matrix Φ of event part triggeri。
Constraints is in step (402)：
(A) w is worked asi(t) when=0, the evaluated error model in step (401) is asymptotically stable；
(B) under zero input condition, for given performance parameter γi＞ 0, arbitrary non-zero wi(t) ＞ 0,It is full
Foot：
Step (403) is specially：
(403a) is for i-th of state estimation subsystem selection liapunov function：
For the network transfer delay τ in i-th of state estimation subsystemi(t) higher limit, Pi、QiAnd RiRespectively just
Set matrix；
(403b) is tried to achieve so that liapunov function meets step (402) constraint bar to liapunov function derivation
The MATRIX INEQUALITIES of part：
Wherein,
γiJoin for given performance
Number, I is suitable dimension unit matrix, F=[I 0].
Compared with prior art, the invention has the advantages that：
(1) compared with traditional automobile, set filter unit to carry out car Active suspension state on cloud computing server and estimate
Meter, it is possible to reduce automobile locally has the design and manufacture calculated with the embedded electronic device of storage capacity, so as to reduce automobile
Cost.
(2) present invention only need not need to utilize sensor whole vehicle condition information transmissions into cloud computing platform
The sensor sample information of automobile suspension system is obtained, cloud computing server can use information On-line Estimation according to these sensors
Go out the status signal of system, it is convenient and reliable, meanwhile, decide whether the sample information of system to be sent to using event trigger mechanism
In network, different from time triggered mechanism, event trigger mechanism is staged by demand, i.e., only when automobile suspension system needs letter
Number transmission when, wave filter can just discharge sample information.This event trigger mechanism staged by demand, can not only ensure wave filter
Effect, moreover it is possible to inherently reduce network transmission burden.
(3) half car Active suspension is divided into front wheel suspension and rear wheel suspension by the present invention, so that half car Active suspension state
Estimating system is estimated using 2 independent state estimation subsystems to the relevant state variables of front wheel suspension and rear wheel suspension
Meter, the design of two subsystems wave filter is separate, and design more facilitates, and improves design half car active suspension system filter
The efficiency of ripple device；
(4) the distributed H that the present invention is designed∞Wave filter can suppress the road surface input disturbance of a variety of travelings and network is made an uproar
Sound.Compared with traditional Kalman filter, H∞Wave filter can enter suppress to extensive types of noise, such as be not white noise
Disturbance and noise it is uncertain disturbance etc., while wave filter of the present invention can meet different accuracy requirement, have a wide range of application, filter
The conservative of ripple device is low, for different Disturbance Rejection coefficient gammasi, filter parameter can be obtained from MATRIX INEQUALITIES, it is full
The different demand of foot.
Brief description of the drawings
Fig. 1 is the structured flowchart that cloud aids in half car Active suspension condition estimating system；
Fig. 2 is the structural representation of half car Active suspension；
Fig. 3 is the trigger interval figure of the first event trigger；
Fig. 4 is the trigger interval figure of second event trigger.
In figure, 11 be front wheel suspension, and 12 be rear wheel suspension, and 21 be the first onboard sensor, and 22 be the second onboard sensor,
31 be the first event trigger, and 32 be second event trigger, and 41 be the first wave filter, and 42 be the second wave filter, and 5 be network, 6
For Cloud Server.
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 cloud aids in half car Active suspension condition estimating system, the system includes：
Sensor sample unit：Vehicle-mounted sensing including being distributed in half car Active suspension front wheel suspension 11 and rear wheel suspension 12
Device, specifically, the corresponding onboard sensor of front wheel suspension 11 are the first onboard sensor 21, the corresponding vehicle-mounted biography of rear wheel suspension 12
Sensor is the second onboard sensor 22；
Event trigger element：Including 2 event triggers, respectively the first event trigger 31 and second event trigger
32,2 event triggers are connected respectively the onboard sensor of front wheel suspension 11 and rear wheel suspension 12；
Filter cell：Described filter cell is arranged on Cloud Server 6, and filter cell includes 2 wave filters,
2 wave filters are respectively by correspondence 1 event trigger of connection of network 5, and accordingly, wave filter is the first wave filter 41 and second
Wave filter 42；
The onboard sensor of front wheel suspension 11 and rear wheel suspension 12 is connected to by 1 event trigger respectively in the system
Wave filter 2 independent state estimation subsystems of formation, 2 wave filters pass through the sensor that is sent from event trigger respectively
Double of car Active suspension of sample information carries out state estimation.
The trigger condition of event trigger is：
Wherein,For the kth time event triggering moment of i-th of subsystem,For the kth time of i-th of subsystem
Jth time sampling instant after event triggering moment,For the after the kth time event triggering moment of i-th of subsystem
J sampled value,Sent for the kth time event triggering moment of i-th of subsystem to the sample information of wave filter, ΦiFor
The weighting matrix of event trigger, δ in i state estimation subsystemiGiven for event trigger in i-th of state estimation subsystem
Fixed event triggering threshold, i=1,2.
Described wave filter is distribution H∞Wave filter.
A kind of cloud aids in the design method of half car Active suspension condition estimating system, and this method comprises the following steps：
(1) build cloud as described above and aid in half car Active suspension condition estimating system；
(2) kinetics equation of half car Active suspension in 2 state estimation subsystems is set up；
(3) trigger condition of the event trigger in 2 state estimation subsystems is determined；
(4) the wave filter kinetic model triggered based on event is set up to 2 state estimation subsystems；
(5) designing filter parameter according to two kinetics equations of step (2) and step (4) causes wave filter to have H∞
Characteristic.
Half car owner moves suspension system dynamics model：
Wherein, xT(t)=[x1(t) x2(t) x3(t) x4(t) x5(t) x6(t) x7(t) x8(t)]TIt is dynamic for half car owner
The state of suspension system, x1(t)=zsf(t)-zuf(t) it is front wheel suspension stroke, x2(t)=zuf(t)-zof(t) it is front tyre
The displacement of up-down vibration,For front suspension up-down vibration speed,For front tyre up-down vibration
Speed, x5(t)=zsr(t)-zur(t) it is rear wheel suspension stroke, x6(t)=zrf(t)-zor(t) it is rear tyre up-down vibration position
Move,For rear wheel suspension up-down vibration displacement,For rear tyre up-down vibration displacement, u (t)
For the input of half car active suspension system, w (t) is that road surface inputs and has w (t) ∈ L2(0 ,+∞), A, B, BwFor constant matrices.
In order to improve the design efficiency of wave filter, Vehicle Active Suspension System has been divided into 2 subsystems, therefore, step
(2) kinetics equation of half car Active suspension is in i-th of state estimation subsystem in：
Wherein,The state of half car Active suspension becomes in respectively i-th and j-th of state estimation subsystem
Amount, i=1,2, j=1,2, and i ≠ j, wi(t) it is road surface disturbance input in i-th of state estimation subsystem,For i-th
The sample information of sensor, z in state estimation subsystemi(t) half car owner to be estimated in i-th of state estimation subsystem is represented
The state variable of dynamic suspension, Ai、Bi、Ci、LiFor constant matrices, LiFor unit battle array, HijFor j-th pair of state estimation subsystem pair
The influence matrix of i-th of state estimation subsystem.
The trigger condition of event trigger is in step (3)：
Wherein,For the kth time event triggering moment of i-th of subsystem,For the kth time of i-th of subsystem
Jth time sampling instant after event triggering moment,For the after the kth time event triggering moment of i-th of subsystem
J sampled value,Sent for the kth time event triggering moment of i-th of subsystem to the sample information of wave filter, ΦiFor
The weighting matrix of event trigger, δ in i state estimation subsystemiGiven for event trigger in i-th of state estimation subsystem
Fixed event triggering threshold, i=1,2.
The wave filter kinetic model of 2 state estimation subsystems is in step (4)：
Wherein, i=1,2,For the state variable of i-th of state estimation subsystem median filter,For i-th of state
Estimate the state estimation of half car Active suspension of subsystem median filter output,For in i-th of state estimation subsystem
The sample information for the sensor that wave filter is received, Afi、BfiAnd LfiIt is the gain square of i-th of state estimation subsystem median filter
Battle array.
Step (4) is specially：
(401) according to the kinetics equation and wave filter dynamics of half car Active suspension in i-th of state estimation subsystem
Model sets up the evaluated error model of i-th of state estimation subsystem median filter：
Wherein, τi(t) represent i-th
Network transfer delay in state estimation subsystem；
(402) constraints of evaluated error model is determined；
(403) being set up using Liapunov stability analytic approach causes error model to meet constraint bar in step (402)
The MATRIX INEQUALITIES of part；
(404) solution matrix inequality obtains design parameter in each state estimation subsystem, including filter gain square
Battle array Afi、BfiAnd LfiAnd the weighting matrix Φ of event part triggeri。
Constraints is in step (402)：
(A) w is worked asi(t) when=0, the evaluated error model in step (401) is asymptotically stable；
(B) under zero input condition, for given performance parameter γi＞ 0, arbitrary non-zero wi(t) ＞ 0,It is full
Foot：
Step (403) is specially：
(403a) is for i-th of state estimation subsystem selection liapunov function：
For the network transfer delay τ in i-th of state estimation subsystemi(t) higher limit, Pi、QiAnd RiRespectively just
Set matrix；
(403b) is tried to achieve so that liapunov function meets step (402) constraint bar to liapunov function derivation
The MATRIX INEQUALITIES of part：
Wherein,
γiFor given performance parameter, I
For suitable dimension unit matrix, F=[I 0].
Design parameter is specially in each state estimation subsystem of solution matrix inequality acquisition in step (404)：
OrderMATRIX INEQUALITIES is carried out
Linearisation is obtained：
Wherein,
The distributed H of the car active suspension system of automobile half can be solved by using the LMI tool boxes of matlab instruments∞Filter
The gain matrix of ripple device is： And the weighting matrix Φ of 2 event triggers1And Φ2。
As shown in Fig. 2 M represents body quality, IαTo pitch the rotary inertia on axle, uf(t) front wheel suspension system is represented
Actuator is exported, ur(t) be rear wheel suspension system actuator output, mufFor the quality of front tyre, murFor rear tyre matter
Amount, zuf(t) front wheel positions, z are representedur(t) it is rear wheel position, a represents front axle to the distance at center, b is back axle to center
Distance,It is the vehicle body elevation angle, zsfAnd z (t)sr(t) position of fore suspension and rear suspension system, z are represented respectivelys(t) it is vehicle body position,
zofAnd z (t)or(t) inputted for front and back wheel road disturbance.According to Newton's second law, the power of half car active suspension system is obtained
Learning model is：
Define x1(t)=zsf(t)-zuf(t) it is front wheel suspension stroke, x2(t)=zuf(t)-zof(t) it is to be shaken above and below tire
Dynamic displacement,For vehicle body up-down vibration speed,For tire up-down vibration speed, x5(t)=zsr
(t)-zur(t) it is rear wheel suspension stroke, x6(t)=zrf(t)-zor(t) it is tire up-down vibration displacement,To be rear
Suspension up-down vibration displacement is taken turns,For rear tyre up-down vibration displacement.The then power of half car active suspension system
Learning model is：
Wherein, xT(t)=[x1(t) x2(t) x3(t) x4(t) x5(t) x6(t) x7(t) x8(t)]T,
And then 2 state estimation subsystem dynamics sides are obtained according to the kinetic model of above-mentioned half car active suspension system
Journey.The design method of half car active suspension system is aided in based on above cloud, suspension parameter, design point are moved according to half car owner in table 1
Cloth H∞Wave filter and event trigger, so as to realize the state estimation of half car active suspension system.
The car owner of table 1 half moves suspension parameter table
| Parameter | Value | Unit | Parameter | Value | Unit |
| M | 690 | kg | csf | 1 | kN.s/m |
| Iα | 1222 | kg | csr | 1 | kN.s/m |
| muf | 40 | kg | kuf | 200 | kN/m |
| mur | 45 | kg | kur | 22 | kN/m |
| ksf | 18 | kN/m | a1 | 1.3 | m |
| ksr | 22 | kN/m | a2 | 1.5 | m |
The optimal solution of Disturbance Rejection coefficient gamma is solved by matlab and one group of filter gain and event triggering power is solved
Matrix is again：
Half car Active suspension condition estimating system is aided in carry out emulation experiment in above-mentioned cloud.By simulation result it can be seen that filter
Ripple device was in 1.5 seconds or so the states with regard to that can track upper half car active suspension system.Fig. 3 and Fig. 4 are respectively the first event trigger
31 and the trigger interval figure of second event trigger 32.Abscissa in figure is time shaft, and ordinate is between the triggering of event twice
Every.The value of ordinate is bigger, and event trigger interval is bigger twice, and the Internet resources of saving are more.It will thus be seen that in network
Cloud aids in half car Active suspension condition estimating system also to can be good at tracking half car Active suspension under conditions of having communication limited
State.
Claims (10)
1. a kind of cloud aids in half car Active suspension condition estimating system, it is characterised in that the system includes：
Sensor sample unit：Onboard sensor including being distributed in half car Active suspension front wheel suspension and rear wheel suspension；
Event trigger element：Including 2 event triggers, 2 event triggers are connected respectively front wheel suspension and trailing wheel is outstanding
The onboard sensor of frame；
Filter cell：Described filter cell is arranged on Cloud Server, and filter cell includes 2 wave filters, 2 filters
Ripple device passes through network correspondence 1 event trigger of connection respectively；
The onboard sensor of front wheel suspension and rear wheel suspension is connected to wave filter shape by 1 event trigger respectively in the system
Into 2 independent state estimation subsystems, 2 wave filters are respectively by the sample information of the sensor sent from event trigger
Double of car Active suspension carries out state estimation.
2. a kind of cloud according to claim 1 aids in half car Active suspension condition estimating system, it is characterised in that event is touched
Hair device trigger condition be：
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Wherein,For the kth time event triggering moment of i-th of subsystem,Touched for the kth time event of i-th of subsystem
The jth time sampling instant after the moment is sent out,Adopted for the jth time after the kth time event triggering moment of i-th of subsystem
Sample value,Sent for the kth time event triggering moment of i-th of subsystem to the sample information of wave filter, ΦiFor i-th of shape
The weighting matrix of event trigger, δ in state estimation subsystemiGiven for event trigger in i-th of state estimation subsystem
Event triggering threshold, i=1,2.
3. a kind of cloud according to claim 1 aids in half car Active suspension condition estimating system, it is characterised in that described
Wave filter is distribution H∞Wave filter.
4. a kind of cloud aids in the design method of half car Active suspension condition estimating system, it is characterised in that this method includes as follows
Step：
(1) build cloud as claimed in claim 1 and aid in half car Active suspension condition estimating system；
(2) kinetics equation of half car Active suspension in 2 state estimation subsystems is set up；
(3) trigger condition of the event trigger in 2 state estimation subsystems is determined；
(4) the wave filter kinetic model triggered based on event is set up to 2 state estimation subsystems；
(5) designing filter parameter according to two kinetics equations of step (2) and step (4) causes wave filter to have H∞It is special
Property.
5. a kind of cloud according to claim 4 aids in the design method of half car Active suspension condition estimating system, its feature
It is, the kinetics equation of half car Active suspension is in i-th of state estimation subsystem in step (2)：
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<mover>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mo>&CenterDot;</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>A</mi>
<mi>i</mi>
</msub>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
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<mo>(</mo>
<mi>t</mi>
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<msub>
<mi>H</mi>
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<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
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<mi>x</mi>
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</mover>
<mi>j</mi>
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<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>B</mi>
<mi>i</mi>
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<msub>
<mi>w</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
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</mtd>
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<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>y</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
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<mo>=</mo>
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<mi>C</mi>
<mi>i</mi>
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</mover>
<mi>i</mi>
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<mrow>
<mo>(</mo>
<mi>t</mi>
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</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>z</mi>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
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<mo>=</mo>
<msub>
<mi>L</mi>
<mi>i</mi>
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<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>,</mo>
</mrow>
Wherein,The state variable of half car Active suspension, i in respectively i-th and j-th of state estimation subsystem
=1,2, j=1,2, and i ≠ j, wi(t) it is road surface disturbance input in i-th of state estimation subsystem,For i-th of state
Estimate the sample information of sensor in subsystem, zi(t) represent that half car owner to be estimated in i-th of state estimation subsystem is dynamic outstanding
The state variable of frame, Ai、Bi、Ci、LiFor constant matrices, LiFor unit battle array, HijIt is j-th pair of state estimation subsystem to i-th
The influence matrix of state estimation subsystem.
6. a kind of cloud according to claim 5 aids in the design method of half car Active suspension condition estimating system, its feature
It is, the trigger condition of event trigger is in step (3)：
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<mrow>
<mo>&lsqb;</mo>
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<mi>y</mi>
<mo>&OverBar;</mo>
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</mover>
<mi>i</mi>
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<mo>&rsqb;</mo>
</mrow>
<mi>T</mi>
</msup>
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<mi>&Phi;</mi>
<mi>i</mi>
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<mi>y</mi>
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</mover>
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<mi>t</mi>
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<mo>&rsqb;</mo>
<mo>&GreaterEqual;</mo>
<msub>
<mi>&delta;</mi>
<mi>i</mi>
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<msubsup>
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<mi>y</mi>
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<mi>i</mi>
<mi>T</mi>
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<mi>t</mi>
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<mi>i</mi>
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<msub>
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<mi>y</mi>
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<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
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<mi>t</mi>
<mi>k</mi>
<mi>i</mi>
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<mi>h</mi>
<mo>+</mo>
<mi>j</mi>
<mi>h</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>}</mo>
<mo>,</mo>
</mrow>
Wherein,For the kth time event triggering moment of i-th of subsystem,Touched for the kth time event of i-th of subsystem
The jth time sampling instant after the moment is sent out,Adopted for the jth time after the kth time event triggering moment of i-th of subsystem
Sample value,Sent for the kth time event triggering moment of i-th of subsystem to the sample information of wave filter, ΦiFor i-th of shape
The weighting matrix of event trigger, δ in state estimation subsystemiGiven for event trigger in i-th of state estimation subsystem
Event triggering threshold, i=1,2.
7. a kind of cloud according to claim 6 aids in the design method of half car Active suspension condition estimating system, its feature
It is, the wave filter kinetic model of 2 state estimation subsystems is in step (4)：
<mrow>
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
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<mover>
<mi>x</mi>
<mo>~</mo>
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<mi>f</mi>
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<mi>f</mi>
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<mi>i</mi>
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<mi>f</mi>
<mi>i</mi>
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<mi>y</mi>
<mo>^</mo>
</mover>
<mi>i</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>z</mi>
<mo>~</mo>
</mover>
<mrow>
<mi>f</mi>
<mi>i</mi>
</mrow>
</msub>
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<mi>t</mi>
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<mo>=</mo>
<msub>
<mi>L</mi>
<mrow>
<mi>f</mi>
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</mrow>
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<mi>f</mi>
<mi>i</mi>
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<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
<mo>,</mo>
</mrow>
Wherein, i=1,2,For the state variable of i-th of state estimation subsystem median filter,For i-th of state estimation
The state estimation of half car Active suspension of subsystem median filter output,To be filtered in i-th of state estimation subsystem
The sample information for the sensor that device is received, Afi、BfiAnd LfiIt is the gain matrix of i-th of state estimation subsystem median filter.
8. a kind of cloud according to claim 7 aids in the design method of half car Active suspension condition estimating system, its feature
It is, step (4) is specially：
(401) according to the kinetics equation and wave filter kinetic model of half car Active suspension in i-th of state estimation subsystem
Set up the evaluated error model of i-th of state estimation subsystem median filter：
Wherein, τi(t) represent i-th
Network transfer delay in state estimation subsystem；
(402) constraints of evaluated error model is determined；
(403) being set up using Liapunov stability analytic approach causes error model to meet constraints in step (402)
MATRIX INEQUALITIES；
(404) solution matrix inequality obtains design parameter in each state estimation subsystem, including filter gain matrix Afi、
BfiAnd LfiAnd the weighting matrix Φ of event part triggeri。
9. a kind of cloud according to claim 8 aids in the design method of half car Active suspension condition estimating system, its feature
It is, constraints is in step (402)：
(A) w is worked asi(t) when=0, the evaluated error model in step (401) is asymptotically stable；
(B) under zero input condition, for given performance parameter γi＞ 0, arbitrary non-zero wi(t) ＞ 0,Meet：
10. a kind of cloud according to claim 8 aids in the design method of half car Active suspension condition estimating system, its feature
It is, step (403) is specially：
(403a) is for i-th of state estimation subsystem selection liapunov function：
For the network transfer delay τ in i-th of state estimation subsystemi(t) higher limit, Pi、QiAnd RiRespectively positive definite square
Battle array；
(403b) is tried to achieve so that liapunov function meets step (402) constraints to liapunov function derivation
MATRIX INEQUALITIES：
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<mi>&Pi;</mi>
<mi>i</mi>
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<mfenced open = "[" close = "]">
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<mi>i</mi>
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<mrow>
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<mi>&Omega;</mi>
<mrow>
<mi>i</mi>
<mn>3</mn>
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<mi>T</mi>
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<mtd>
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<mrow>
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<mi>I</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo><</mo>
<mn>0</mn>
<mo>,</mo>
</mrow>
Wherein,
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<mi>&Omega;</mi>
<mrow>
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<mfenced open = "[" close = "]">
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<mi>Y</mi>
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<mi>P</mi>
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<mi>i</mi>
<mn>1</mn>
</mrow>
</msub>
<mi>F</mi>
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</mrow>
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<mtd>
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<mi>P</mi>
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</mover>
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<mi>P</mi>
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</mrow>
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</mtd>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mo>-</mo>
<msubsup>
<mi>&gamma;</mi>
<mi>i</mi>
<mn>2</mn>
</msubsup>
<mi>I</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>,</mo>
</mrow>
<mrow>
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<mrow>
<mi>i</mi>
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<mrow>
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<mn>3</mn>
</mrow>
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<mi>w</mi>
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<mtd>
<mn>0</mn>
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</mtd>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
</mtable>
<mo>&rsqb;</mo>
<mo>,</mo>
</mrow>
γ i are given performance ginseng
Number, I is suitable dimension unit matrix, F=[I 0]. 3
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| CN109795277A (en) * | 2018-10-17 | 2019-05-24 | 南京林业大学 | The method of Active suspension Control for Dependability when a kind of network between controller and actuator is by DoS attack |
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| JP2010195323A (en) * | 2009-02-26 | 2010-09-09 | Nissan Motor Co Ltd | Vehicular state estimating device, vehicular state estimating method, vehicular suspension control device, and automobile |
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