CN106354901B - A kind of carrier rocket mass property and dynamics key parameter on-line identification method - Google Patents
A kind of carrier rocket mass property and dynamics key parameter on-line identification method Download PDFInfo
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Abstract
The present invention relates to a kind of carrier rocket mass property and dynamics key parameter on-line identification methods, this method is based on six degree of freedom fight dynamics equation and unknown parameter dynamical equation, estimate while realization by construction augmented state equation to state and parameter, so as to obtain the parameter vector for needing to recognize and state vector simultaneously, this maximum advantage of method is to estimate unknown parameter according to output quantity, when system changes, the estimated result of mass property and dynamics key parameter can accurately be obtained, crucial technical foundation can be established for the adjustment of carrier rocket control parameter on-line optimization；The discrimination method may be implemented presence and parameter while estimate simultaneously, be of great significance to the conservative and raising rocket that reduce rocket system Deviation Design to the adaptability of malfunction, can provide beneficial reference for the following rocket design.
Description
Technical field
The present invention relates to a kind of carrier rocket mass property and dynamics key parameter on-line identification methods, belong to control system
System design field.
Background technique
China's carrier space vehicle control system design at present there is to system deviation bad adaptability, design level is low, design
The too conservative problem of surplus, especially Control System Design are carried out for nominal situation, to the adaptability of malfunction
It is poor, it can not accurately estimate the crucial kinetic parameter in flight course, and realize the on-line tuning of control parameter to answer accordingly
To various possible flight operating conditions.Therefore, it is badly in need of carrying out the on-line parameter identification technical research of carrier rocket key characteristic, a side
Face is conducive to find out system deviation, reduces the conservative of System Parameter Design, further excavation type for the use of active service rocket
On the other hand number potentiality can also realize that solid technical foundation is established in the adjustment of control parameter on-line optimization for the following rocket, can be with
The influence of flight deviation and non-catastrophic failure to aerial mission is greatly reduced.
The parameter that current carrier space vehicle control system is all based on nominal state carries out design, these parameters and material object produce
There is certain differences for product, flight operating condition, carry out envelope design often through wider deviation band, and Design of Attitude Control System is just
It is a set of parameter for capableing of the various operating conditions of envelope of design, the certain conservative of this inevitable bring.During Project R&D, warp
Often generation is due to the problems such as deviation band is wide, and Design of Attitude Control System is difficult.Although certain parameters can carry out precise measurement, or
It is accurately obtained by large number of ground test, still, there are two large problems for this method, first is that due to world otherness,
Certain parameters can not be obtained accurately, second is that the cost for obtaining accurate parameter is excessive, and the distribution of every hair product is larger, is difficult to do
To once and for all.Once the control parameter based on nominal state design will be unable to realize control in addition, breaking down in flight course
System processed is stablized, and needs that control parameter is adaptively adjusted according to practical flight characteristic, and everything all be unable to do without and obtains
Accurate carrier rocket mass property and dynamics key parameter.
Traditional carrier rocket key characteristic parameter identification technique is off-line identification technology, often in flight number of results
After acquisition, parameter identification, this method one side simulation calculation amount are carried out by optimization algorithm according to flight result data
It is larger, it on the other hand cannot achieve on-line parameter identification.
Therefore, be badly in need of carrying out the research of on-line parameter identification technique, especially mass property and kinetic characteristics parameter
Line identification technique research, these parameters are to determine the key characteristic parameter of Control System Design.Since there has been no do for current rocket
Method directly measures these key parameters (including quality, rotary inertia, mass center, pressure heart), needs to study high-performance parameter identification and calculates
Method carries out on-line identification to the above parameter.
Summary of the invention
It is an object of the invention to overcome the above-mentioned deficiency of the prior art, a kind of carrier rocket mass property and power are provided
Key parameter on-line identification method is learned, which may be implemented presence and parameter while estimating, to reduction rocket system
The conservative and raising rocket for Deviation Design of uniting are of great significance to the adaptability of malfunction, can mention for the following rocket design
For beneficial reference.
What above-mentioned purpose of the invention was mainly achieved by following technical solution:
A kind of carrier rocket mass property and dynamics key parameter on-line identification method, include the following steps:
(1), according to carrier rocket six degree of freedom fight dynamics equation, state equation and measurement equation are obtained；
(2), the state equation and measurement equation are converted into State-Vector Equation；
(3), augmented state side is obtained according to the State-Vector Equation and after needing the parameter vector equation recognized combination
Journey；
(4), augmented state equation is subjected to discretization, is converted into discrete iteration equation；
(5), according to the discrete iteration equation, the parameter vector for needing to recognize is obtained using filtering algorithm, is obtained simultaneously
State vector.
State in above-mentioned carrier rocket mass property and dynamics key parameter on-line identification method, in step (1)
Equation is as follows:
It is as follows to measure equation:
Wherein: wiFor state-noise；viTo measure noise, Xa,Ya,ZaFor the coordinate under launch inertial coordinate system, Vax,Vay,
VazFor the speed in x, y, z direction under launch inertial coordinate system,ψ, γ are the attitude angle under launch inertial coordinate system,For under launch vehicle coordinate system around the attitude angular velocity of x, y, z axis；δψ,δγFor engine pivot angle；gax,gay,gazFor
The gravitational acceleration in x, y, z direction under launch inertial coordinate system；XrFor motor power position and rocket theory cusp away from
From ZrIt is motor power position at a distance from rocket axis；M is Rocket mass, Jx1,Jy1,Jz1For under launch vehicle coordinate system around
The rocket rotary inertia of x, y, z axis, XzIt is rocket mass center at a distance from rocket theory cusp, P is a single engine thrust.
In above-mentioned carrier rocket mass property and dynamics key parameter on-line identification method, in step (2) state to
It is as follows to measure equation:
Z (t)=h (x, t)+v (t) (17)
Wherein:For the shape of system
State vector, λ (t)=(λ1λ2)TFor the unknown parameter vector of system, λ1=M, λ2=P,For the output vector of system, h (x, t)
=I12×12X (t), I12×12For with unknown parameter vector it is incoherent 12 dimension * 12 dimension unit matrixs, f (x, λ, t) be with it is unknown
The relevant nonlinear function vector of parameter vector, expression formula are as follows:
Wherein: fx(λ1),fy(λ1),fz(λ1),fXz(λ1) be and λ1Relevant known function, wx(t) it makes an uproar for system mode
Sound vector；V (t) is systematic survey noise vector.
In above-mentioned carrier rocket mass property and dynamics key parameter on-line identification method, system mode noise vector
wx(t) mean value is zero, and variance matrix isWhite Gaussian noise；The systematic survey noise vector v (t)
Mean value is zero, and variance matrix is R=E [v (t) vT(t)] white Gaussian noise.
In above-mentioned carrier rocket mass property and dynamics key parameter on-line identification method, according to shape in step (3)
The parameter vector equation that state vector equation and needs recognize obtains augmented state equation, and the specific method is as follows:
The parameter vector equation for needing to recognize is as follows:
Wherein: g (t)=(fM(t)fP(t))TFor the known function under various operating conditions, wλIt (t) is parametric noise vector, fM
(t) function, f are changed over time for qualityP(t) function is changed over time for thrust；
In conjunction with (16) and (18) formula, it is the state vector of augmented system by the unknown parameter vector extensions in system, obtains
The state vector of augmented system are as follows:
Thus augmented state equation is obtained are as follows:
In above-mentioned carrier rocket mass property and dynamics key parameter on-line identification method, parametric noise vector wλ
(t) mean value is zero, and variance matrix isWhite Gaussian noise.
In above-mentioned carrier rocket mass property and dynamics key parameter on-line identification method, by augmentation in step (4)
The specific method is as follows for state equation discretization:
Augmented state equation discretization can be obtained:
Wherein: T is the sampling period, k=1,2 ...,
Z (k)=h (x (k))+v (k) (22)
Wherein: f (x (k), λ (k)) obtains to substitute into x (k) and λ (k) after f (x, λ, t) discretization；H (x (k)) be h (x,
T) x (k) substitution is obtained after discretization；G (k) is that g (t) discretization obtains.
In above-mentioned carrier rocket mass property and dynamics key parameter on-line identification method, using filter in step (5)
Wave algorithm obtains needing the parameter vector that recognizes and state vector that the specific method is as follows:
(1), it is initialized according to -1 step calculated result of kthPλ(k | k), Px(k | k), V (k), wherein
K=1,2 ...；
(2), Φ (k) is calculated, Ψ (k)；
Wherein:
(3) ,+1 step parameter optimum prediction of kth is calculated:
The prediction of+1 step state optimization of kth is calculated simultaneously:
Wherein:It will for f (x, λ, t) discretizationWithSubstitution obtains；
(4) ,+1 step parameter prediction error covariance matrix of kth is calculated:
Pλ(k+1 | k)=Pλ(k|k)+Qλ (25)
Calculate+1 step status predication error covariance matrix of kth simultaneously:
Px(k+1 | k)=Φ (k) Px(k|k)ΦT(k)+Ψ(k)Pλ(k|k)ΨT(k)+Qx (26)
(5), H (k+1) is calculated, Φ (k+1), Ψ (k+1):
Wherein:
(6), state optimization gain matrix is calculated:
Kx(k+1)=Px(k+1|k)HT(k+1)[H(k+1)Px(k+1|k)HT(k+1)+R]-1 (30)
(7), weighting matrix is calculated:
U (k+1)=Φ (k+1) V (k)+Ψ (k+1) (31)
S (k+1)=H (k+1) U (k+1) (32)
V (k+1)=U (k+1)-Kx(k+1)S(k+1) (33)
K (k+1)=Kx(k+1)+V(k+1)Kλ(k+1) (34)
(8), calculating parameter optimum gain matrix:
(9), calculating parameter optimal estimation value:
Wherein: z (k) indicates the measured value of kth step；
Calculate state optimization estimated value simultaneously:
Wherein:It will for h (x, t) discretizationSubstitution obtains；
(10), calculating parameter filtering error variance matrix:
Pλ(k+1 | k+1)=[I-Kλ(k+1)S(k+1)]Pλ(k+1|k) (38)
Calculate state filtering error covariance matrix simultaneously:
Px(k+1 | k+1)=[I-Kx(k+1)H(k+1)]Px(k+1|k) (39)
(11), the discreet value for+1 step of kth for calculating above-mentioned kth stepPλ(k+1|
k+1)、Px(k+1 | k+1), V (k+1) initializationPλ(k | k), Px(k | k), V (k), wherein k=1,
2 ..., return step (2).
Compared with prior art, the present invention has the following advantages:
(1), carrier rocket mass property and dynamics proposed by the present invention based on state and Decoupled estimation are crucial
On-line parameter identification method, this method may be implemented presence and parameter while estimating, to reduction rocket system Deviation Design
Conservative and improve rocket and be of great significance to the adaptability of malfunction, can be designed for the following rocket and beneficial ginseng be provided
It examines；
(2), carrier rocket mass property and dynamics proposed by the present invention based on state and Decoupled estimation are crucial
On-line parameter identification method, this method are based on six degree of freedom fight dynamics equation and unknown parameter dynamical equation, pass through construction
Augmented state equation is estimated while realization to state and parameter, so as to obtain the parameter vector for needing to recognize and state simultaneously
Vector, this maximum advantage of method is to estimate unknown parameter according to output quantity, when system changes,
The estimated result of mass property and dynamics key parameter can be accurately obtained, can be carrier rocket control parameter on-line optimization tune
It is whole to establish crucial technical foundation；
(3), research achievement of the present invention can be applied in the various carrier space vehicle control system development in China, can also be applied to each
In the design of class flight control, in addition, the method for the present invention has generality, general control system also may extend to
It is practical in design.
Detailed description of the invention
Fig. 1 is carrier rocket mass property of the present invention and dynamics key parameter on-line identification method flow diagram；
Fig. 2 is X-direction location estimation result in the embodiment of the present invention；
Fig. 3 is Y-direction location estimation result in the embodiment of the present invention；
Fig. 4 is Z-direction location estimation result in the embodiment of the present invention；
Fig. 5 is pitch attitude angular estimation result in the embodiment of the present invention；
Fig. 6 is yaw-position angular estimation result in the embodiment of the present invention；
Fig. 7 is roll attitude angular estimation result in the embodiment of the present invention；
Fig. 8 is quality estimation results in the embodiment of the present invention；
Fig. 9 is thrust estimated result in the embodiment of the present invention.
Specific embodiment
The present invention is described in further detail in the following with reference to the drawings and specific embodiments:
The present invention first has to establish each before carrying out carrier rocket mass property and kinetic characteristics on-line parameter identification
Kinetics relation between a characteristic quantity, this is the key that realize parameter identification.Then estimate using based on state with Decoupled
The expanded Kalman filtration algorithm of meter recognizes unknown mass property and kinetic characteristics parameter.It asks for simplifying the analysis
The difficulty of topic carries out relevant technical research by taking extra-atmospheric flight section as an example.
It is as shown in Figure 1 carrier rocket mass property of the present invention and dynamics key parameter on-line identification method flow diagram；
Six degree of freedom fight dynamics equation and unknown parameter dynamical equation are initially set up, then converts unknown parameter identification problem to
The problem of state and Decoupled are estimated, obtains unknown matter finally by the Kalman filtering algorithm of state and parameter estimation
The estimated result of flow characteristic and dynamics key parameter, concrete methods of realizing are as follows:
The first step establishes fight dynamics equation.
According to basic flight dynamics principle, be not added derivation directly to the rocket flight dynamics side set out under used system
Journey:
Wherein,
Rocket rotation around center of mass kinetics equation under rocket body system:
Mass property relational expression:
There are following dynamical equations for Rocket mass in flight course:
There are following dynamical equations for rocket thrust in flight course:
Wherein: Xa,Ya,ZaFor the coordinate under launch inertial coordinate system, Vax,Vay,VazFor x, y, z under launch inertial coordinate system
The speed in direction, Aax,Aay,AazFor the apparent acceleration in x, y, z direction under launch inertial coordinate system, Ax1,Ay1,Az1For launch vehicle coordinate
It is the apparent acceleration in lower x, y, z direction,ψ, γ are the attitude angle in x, y, z direction under launch inertial coordinate system,For
The angular speed in x, y, z direction under launch vehicle coordinate system can be calculated by navigation and be obtained；δψ,δγIt, can for engine pivot angle
To be obtained by control instruction；gax,gay,gazFor the gravitational acceleration in x, y, z direction under launch inertial coordinate system；XrTo start
Machine thrust point is at a distance from rocket theory cusp, ZrIt is motor power position at a distance from rocket axis；M is rocket
Quality, Jx1,Jy1,Jz1For the rocket rotary inertia in x, y, z direction under launch vehicle coordinate system, XzFor rocket mass center and rocket theory point
The distance of point, P are a single engine thrust, are the amount for needing to recognize；fM(t) function, f are changed over time for qualityP(t) it is
Thrust changes over time function, fXz(M), fx(M), fy(M), fz(M) for the function of mass change, above-mentioned function is known
Function.
In view of there are certain noise and perturbations for state and measurement parameter, above-mentioned equation can be written as following form:
State equation:
Measure equation:
Wherein, wiFor state-noise, it is zero mean Gaussian white noise under normal flight operating condition, is non-under fault condition
Zero-mean coloured noise can be modeled by fault mode；viIt is zero mean Gaussian white noise to measure noise.
Second step is converted to state space equation.
It, will be upper for the ease of realizing carrier rocket mass property and dynamics key parameter on-line identification using filtering algorithm
It states kinetics equation and is rewritten as the form of following state space equation and (directly obtain formula by formula (8), (9), (10), (11)
(16), formula (17) are directly obtained by formula (12), (13), (14), (15):
Z (t)=h (x, t)+v (t) (17)
Wherein:For the shape of system
State vector, λ (t)=(λ1λ2)TFor the unknown parameter vector of system, λ1=M, λ2=P,For the output vector of system, h (x, t)
=I12×12X (t), I12×12For with unknown parameter vector it is incoherent 12 dimension * 12 dimension unit matrixs, f (x, λ, t) be with it is unknown
The relevant nonlinear function vector of parameter vector, expression formula are as follows:
Wherein: fx(λ1),fy(λ1),fz(λ1),fXz(λ1) be and λ1Relevant known function, such as in the present embodiment
Are as follows:
fx(λ1)=5.7978 × 10-3λ1+3.5436×104
fy(λ1)=2.523 × 10-9λ1 3-5.764×10-4λ1 2+55.3322λ1+9.28155×105
fz(λ1)=2.523 × 10-9λ1 3-5.764×10-4λ1 2+55.3322λ1+9.28155×105
fXz(λ1)=1.000 × 10-14λ1 3-2.7956×10-9λ1 2+2.5586×10-4λ1+11.5529
wx(t) it is system mode noise vector, be mean value is zero, variance matrix isWhite Gaussian
Noise, v (t) are systematic survey noise vector, be mean value are zero, and variance matrix is R=E [v (t) vT(t)] white Gaussian noise.
Third step establishes augmented system equation.
In above-mentioned state space equation, λ is the parameter recognized.Unknown parameter λ is accurately obtained, is needed first
The dynamic law of λ is obtained, augmentation is carried out to original system, then it is recognized using filtering algorithm.
λ (t) can be by its change procedure of following differential equation:
Wherein: g (t)=(fM(t)fP(t))TFor the known function under various operating conditions, wλ(t) it is parametric noise vector, is
Mean value is zero, and variance matrix isWhite Gaussian noise.
fM(t) function, f are changed over time for qualityP(t) function is changed over time for thrust；
In conjunction with (16) and (18) formula, the unknown parameter in system can be extended to the state of augmented system, obtain augmentation shape
State is
Thus obtaining augmented state equation is
Therefore, the essence of system unknown parameter estimation is exactly unknown parameter to be extended to the state of system, then using each
Kind Extended Kalman filter method carries out the estimation of augmented state, to obtain the estimated value of unknown parameter.
4th step, by augmented system model discretization.
Augmented system discretization can be obtained:
Wherein: T is the sampling period, k=1,2 ...,
Z (k)=h (x (k))+v (k) (22)
Wherein: f (x (k), λ (k)) obtains to substitute into x (k) and λ (k) after f (x, λ, t) discretization；H (x (k)) be h (x,
T) discretization obtains x (k) substitution；G (k) is that g (t) discretization obtains.
So far, it as long as being augmented system state by parameter spread to be estimated, then can be obtained using spreading kalman algorithm
The estimated value of unknown parameter.
5th step, the expanded Kalman filtration algorithm that adoption status is estimated with Decoupled estimate unknown parameter.
The maximum difficulty of the above problem is due to being augmented system state by parameter spread to be estimated, so as to cause system
Calculation amount increase, this is totally unfavorable for high order system as carrier rocket, it is therefore necessary to adoption status and parameter
The filtering algorithm of decoupling estimation is solved.It not only can be with the unknown parameter in real-time estimation system, and adjustment is estimated accordingly
Meter state, but also decoupling estimation can be carried out to state and parameter by two parallelism wave filters, to reach in terms of reduction system
The purpose of calculation amount.
The filtering algorithm based on state and Decoupled estimation is given below, when starting to calculate, Pλ
(k | k), Px(k | k), the initial value of V (k) is previously given, when calculating later, calculates using the calculated result of previous step as next step
Initial value, below by kth step calculate for,
(1) it is initialized according to -1 step calculated result of kthPλ(k | k), Px(k | k), V (k)；K=1,
2,...
(2) Φ (k) is calculated, Ψ (k):
Wherein:
(3)+1 step parameter optimum prediction of kth is calculated:
The prediction of+1 step state optimization of kth is calculated simultaneously:
Wherein: will be obtained with substitution for f (x, λ, t) discretization；
(4)+1 step parameter prediction error covariance matrix of kth is calculated:
Pλ(k+1 | k)=Pλ(k|k)+Qλ (25)
Calculate+1 step status predication error covariance matrix of kth simultaneously:
Px(k+1 | k)=Φ (k) Px(k|k)ΦT(k)+Ψ(k)Pλ(k|k)ΨT(k)+Qx (26)
(5) H (k+1) is calculated, Φ (k+1), Ψ (k+1):
Wherein:
(6) state optimization gain matrix is calculated:
Kx(k+1)=Px(k+1|k)HT(k+1)[H(k+1)Px(k+1|k)HT(k+1)+R]-1 (30)
(7) weighting matrix is calculated:
U (k+1)=Φ (k+1) V (k)+Ψ (k+1) (31)
S (k+1)=H (k+1) U (k+1) (32)
V (k+1)=U (k+1)-Kx(k+1)S(k+1) (33)
K (k+1)=Kx(k+1)+V(k+1)Kλ(k+1) (34)
(8) calculating parameter optimum gain matrix:
(9) calculating parameter optimal estimation value:
Wherein: z (k) indicates the measured value of kth step,
Including angular speed, angle, position, speed measured value；
Calculate state optimization estimated value simultaneously:
Wherein:It will for h (x, t) discretizationSubstitution obtains；
(10) calculating parameter filtering error variance matrix:
Pλ(k+1 | k+1)=[I-Kλ(k+1)S(k+1)]Pλ(k+1|k) (38)
Calculate state filtering error covariance matrix simultaneously:
Px(k+1 | k+1)=[I-Kx(k+1)H(k+1)]Px(k+1|k) (39)
(11), it completes kth step to calculate, the discreet value for+1 step of kth that above-mentioned kth step is calculated
Pλ(k+1|k+1)、Px(k+1 | k+1), V (k+1) initialization Pλ(k | k), Px(k | k), V (k), wherein k
=1,2 ..., return step (2), start the calculating of+1 step of kth.
Pλ(k+1|k+1)→Pλ(k | k), Px(k+1|k+1)→Px(k | k), V (k+1) → V (k).
State estimation and parameter estimation result are finally obtained, that is, the parameter vector for needing to recognizeAnd state vectorAs the design considerations for carrying out the adjustment of control parameter on-line optimization.
Above-mentioned parameter discrimination method is theoretically a kind of double estimation filtering methods of state and parameter estimation in fact,
State and parameter i.e. to unknown dynamical system alternately estimate that this method estimates signal using model, and utilizes estimation
Signal removes correction model.
Embodiment 1
Following initial value is used according to formula (23)-(39), carrier rocket mass property and dynamics key parameter can be realized
On-line identification, specific identification result are shown in Fig. 2-Fig. 9.
Pλ(0 | 0)=100I2×2, Px(0 | 0)=100I12×12, V (0)=I12×12。
Wherein, x0For state of flight initial value, λ0To estimate initial parameter values.
As Fig. 2 be in the embodiment of the present invention X-direction location estimation as a result, Fig. 3 is that Y-direction position is estimated in the embodiment of the present invention
Count result；Fig. 4 is Z-direction location estimation result in the embodiment of the present invention；Fig. 5 is pitch attitude angular estimation in the embodiment of the present invention
As a result；Fig. 6 is yaw-position angular estimation result in the embodiment of the present invention；Fig. 7 is roll attitude angular estimation in the embodiment of the present invention
As a result；Fig. 8 is quality estimation results in the embodiment of the present invention；Fig. 9 is thrust estimated result in the embodiment of the present invention.It is tied from identification
Fruit is it is found that the carrier rocket mass property estimated based on state with Decoupled and dynamics key parameter that are proposed are distinguished online
Knowing algorithm may be implemented the accurate estimation of state of flight and unknown parameter, and estimated accuracy can satisfy design objective demand.
The above, optimal specific embodiment only of the invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.
The content that description in the present invention is not described in detail belongs to the well-known technique of professional and technical personnel in the field.
Claims (7)
1. a kind of carrier rocket mass property and dynamics key parameter on-line identification method, it is characterised in that: including walking as follows
It is rapid:
(1), according to carrier rocket six degree of freedom fight dynamics equation, state equation and measurement equation are obtained；
(2), the state equation and measurement equation are converted into State-Vector Equation；
(3), augmented state equation is obtained according to the State-Vector Equation and after needing the parameter vector equation recognized combination；
(4), augmented state equation is subjected to discretization, is converted into discrete iteration equation；
(5), according to the discrete iteration equation, the parameter vector for needing to recognize is obtained using filtering algorithm, while obtaining state
Vector；
State equation in the step (1) is as follows:
It is as follows to measure equation:
Wherein: wiFor state-noise；viTo measure noise, Xa,Ya,ZaFor the coordinate under launch inertial coordinate system, Vax,Vay,VazFor
The speed in x, y, z direction under launch inertial coordinate system,ψ, γ are the attitude angle under launch inertial coordinate system,For
Around the attitude angular velocity of x, y, z axis under launch vehicle coordinate system；δψ,δγFor engine pivot angle；gax,gay,gazTo emit inertial coordinate
It is the gravitational acceleration in lower x, y, z direction；XrIt is motor power position at a distance from rocket theory cusp, ZrFor engine
Thrust point is at a distance from rocket axis；M is Rocket mass, Jx1,Jy1,Jz1For under launch vehicle coordinate system around the rocket of x, y, z axis
Rotary inertia, XzIt is rocket mass center at a distance from rocket theory cusp, P is a single engine thrust.
2. a kind of carrier rocket mass property according to claim 1 and dynamics key parameter on-line identification method,
Be characterized in that: State-Vector Equation is as follows in the step (2):
Z (t)=h (x, t)+v (t) (17)
Wherein:For system state to
Amount, λ (t)=(λ1 λ2)TFor the unknown parameter vector of system, λ1=M, λ2=P,For the output vector of system, h (x, t)
=I12×12X (t), I12×12For with unknown parameter vector it is incoherent 12 dimension * 12 dimension unit matrixs, f (x, λ, t) be with it is unknown
The relevant nonlinear function vector of parameter vector, expression formula are as follows:
Wherein: fx(λ1),fy(λ1),fz(λ1),fXz(λ1) be and λ1Relevant known function, wx(t) for system mode noise to
Amount；V (t) is systematic survey noise vector.
3. a kind of carrier rocket mass property according to claim 2 and dynamics key parameter on-line identification method,
It is characterized in that: the system mode noise vector wx(t) mean value is zero, and variance matrix isWhite Gaussian
Noise；Systematic survey noise vector v (t) mean value is zero, and variance matrix is R=E [v (t) vT(t)] white Gaussian noise.
4. a kind of carrier rocket mass property according to claim 2 and dynamics key parameter on-line identification method,
It is characterized in that: augmented state side being obtained according to the parameter vector equation that State-Vector Equation and needs recognize in the step (3)
The specific method is as follows for journey:
The parameter vector equation for needing to recognize is as follows:
Wherein: g (t)=(fM(t) fP(t))TFor the known function under various operating conditions, wλIt (t) is parametric noise vector, fM(t) it is
Quality changes over time function, fP(t) function is changed over time for thrust；
In conjunction with (16) and (18) formula, it is the state vector of augmented system by the unknown parameter vector extensions in system, obtains augmentation
The state vector of system are as follows:
Thus augmented state equation is obtained are as follows:
5. a kind of carrier rocket mass property according to claim 4 and dynamics key parameter on-line identification method,
It is characterized in that: the parametric noise vector wλ(t) mean value is zero, and variance matrix isGauss white noise
Sound.
6. a kind of carrier rocket mass property according to claim 4 and dynamics key parameter on-line identification method,
Be characterized in that: by augmented state equation discretization, the specific method is as follows in the step (4):
Augmented state equation discretization can be obtained:
Wherein: T is the sampling period, k=1,2 ...,
Z (k)=h (x (k))+v (k) (22)
Wherein: f (x (k), λ (k)) obtains to substitute into x (k) and λ (k) after f (x, λ, t) discretization；H (x (k)) be h (x, t) from
X (k) substitution is obtained after dispersion；G (k) is that g (t) discretization obtains.
7. a kind of carrier rocket mass property according to claim 6 and dynamics key parameter on-line identification method,
It is characterized in that: obtaining needing the specific method of the parameter vector recognized and state vector in the step (5) using filtering algorithm
It is as follows:
(1), it is initialized according to -1 step calculated result of kthPλ(k | k), Px(k | k), V (k), wherein k=
1,2,...；
(2), Φ (k) is calculated, Ψ (k)；
Wherein:
(3) ,+1 step parameter optimum prediction of kth is calculated:
The prediction of+1 step state optimization of kth is calculated simultaneously:
Wherein:It will for f (x, λ, t) discretizationWithSubstitution obtains；
(4) ,+1 step parameter prediction error covariance matrix of kth is calculated:
Pλ(k+1 | k)=Pλ(k|k)+Qλ (25)
Calculate+1 step status predication error covariance matrix of kth simultaneously:
Px(k+1 | k)=Φ (k) Px(k|k)ΦT(k)+Ψ(k)Pλ(k|k)ΨT(k)+Qx (26)
(5), H (k+1) is calculated, Φ (k+1), Ψ (k+1):
Wherein:
(6), state optimization gain matrix is calculated:
Kx(k+1)=Px(k+1|k)HT(k+1)[H(k+1)Px(k+1|k)HT(k+1)+R]-1 (30)
(7), weighting matrix is calculated:
U (k+1)=Φ (k+1) V (k)+Ψ (k+1) (31)
S (k+1)=H (k+1) U (k+1) (32)
V (k+1)=U (k+1)-Kx(k+1)S(k+1) (33)
K (k+1)=Kx(k+1)+V(k+1)Kλ(k+1) (34)
(8), calculating parameter optimum gain matrix:
(9), calculating parameter optimal estimation value:
Wherein: z (k) indicates the measured value of kth step；
Calculate state optimization estimated value simultaneously:
Wherein:It will for h (x, t) discretizationSubstitution obtains；
(10), calculating parameter filtering error variance matrix:
Pλ(k+1 | k+1)=[I-Kλ(k+1)S(k+1)]Pλ(k+1|k) (38)
Calculate state filtering error covariance matrix simultaneously:
Px(k+1 | k+1)=[I-Kx(k+1)H(k+1)]Px(k+1|k) (39)
(11), the discreet value for+1 step of kth for calculating above-mentioned kth stepPλ(k+1|k+1)、
Px(k+1 | k+1), V (k+1) initializationPλ(k | k), Px(k | k), V (k), wherein k=1 2 ..., are returned
It returns step (2).
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| CN107589668A (en) * | 2017-08-31 | 2018-01-16 | 中国航空工业集团公司沈阳飞机设计研究所 | A kind of vertically taking off and landing flyer mass property measurement method of parameters |
| CN107933965B (en) * | 2017-11-09 | 2019-09-06 | 北京航天自动控制研究所 | A kind of carrier rocket servo mechanism linear smoothing resetting method |
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| CN109870272A (en) * | 2019-01-31 | 2019-06-11 | 西北工业大学 | Spacecraft mass in-orbit identification method based on the conservation of momentum |
| CN111443726B (en) * | 2020-03-02 | 2023-08-15 | 北京空天技术研究所 | Trajectory reconstruction method based on flight test data |
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| CN115204063B (en) * | 2022-09-16 | 2022-11-18 | 中国空气动力研究与发展中心计算空气动力研究所 | Rocket aerodynamic coefficient space-ground difference correction method |
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