Joint correction identification method for mass and thrust parameters of launch vehicle under thrust failure

Abstract

The application discloses a kind of joint correction identification method of mass and thrust parameter of launch vehicle under thrust failure, comprising: establishing the mass center motion equation and mass consumption equation for identification of launch vehicle;According to the visual acceleration information measured by the inertial sensitive device of launch vehicle as the observation of identification, the observation equation is established;In each calculation period, the rocket mass at next time is identified by Kalman filtering using the thrust parameter identification result at current time, and the thrust parameter at next time is estimated by fading factor recursive least square using the rocket mass identification result at next time.The identification method based on fading factor recursive least square and Kalman filtering combination of the present application can effectively identify the mass and thrust parameter of launch vehicle under thrust failure during rocket flight.

Description

Technical Field

[0001] This invention belongs to the field of online identification of thrust faults in typical dynamic systems during aircraft flight, and specifically relates to a joint correction and identification method for the mass and thrust parameters of a launch vehicle under thrust fault conditions. Background Technology

[0002] Launch vehicles are a major component of space transportation systems, the primary tool for humankind to access space, and the cornerstone of space technology development and security. Launch vehicle systems are complex in structure and have high launch costs, making their safety and reliability paramount. The propulsion system is one of the key systems of a launch vehicle; a significant portion of failed launches, both domestically and internationally, are due to propulsion system failures. Non-fatal propulsion system failures typically manifest as a decrease in thrust or engine shutdown. A decrease in engine thrust or shutdown can cause thrust imbalance, generating disruptive torques. Slow propellant consumption leads to uneven mass distribution within the rocket, reducing the control system's capabilities. In fact, such non-fatal propulsion system failures are not irreversible. Even after a decrease in thrust, the rocket is capable of continuing its mission. Therefore, timely diagnosis of the magnitude of the thrust failure and the rocket's current mass is crucial for subsequent control reconfiguration and mission adjustments.

[0003] Fault diagnosis methods mainly include model-based, signal-based, and knowledge-based methods. Model-based fault diagnosis methods have been extensively studied and can be further subdivided into parameter estimation methods, state estimation methods, and equivalent space methods. For example, Kalman filtering can be used to estimate the state, and augmented Kalman filtering can also be used to estimate parameters. However, this augmented Kalman filtering assumes that the parameter vector to be estimated is an invariant state vector, and it cannot accurately estimate changing parameters, especially when the parameter to be estimated undergoes abrupt changes. In the field of parameter identification, the least squares method is the most widely used estimation method and can be used for dynamic, static, linear, and nonlinear systems.

[0004] There are two main approaches to identifying launch vehicle thrust parameters: one is to estimate performance parameters using telemetry data of engine combustion chamber pressure and pre-established internal ballistic programs, aiming to understand the engine's true performance and support engine and rocket development; the other is to use apparent acceleration, rocket velocity, position, and mass information for identification, thereby providing information for subsequent control reconfiguration and flight mission adjustments. Current research on rocket thrust identification using apparent acceleration and other information assumes the rocket's mass is known; however, real-time mass information during rocket flight is impossible to measure. Especially when rocket thrust malfunctions, the fuel consumption rate deviates from the pre-set rate, leading to a corresponding deviation in rocket mass and affecting the stability of the control system. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention provides a joint correction and identification method for the mass and thrust parameters of a launch vehicle under thrust failure—an identification method based on a combination of fading factor recursive least squares and Kalman filtering, which can effectively identify the mass and thrust parameters of a launch vehicle under thrust failure.

[0006] The technical solution adopted by this invention to solve its technical problem is: a joint correction and identification method for the mass and thrust parameters of a launch vehicle under thrust failure, comprising: establishing the motion equation of the launch vehicle's center of mass and the mass consumption equation for identification; establishing the observation equation based on the apparent acceleration information measured by the rocket's inertial sensing device as the observation for identification; in each calculation cycle, using the thrust parameter identification result at the current moment to identify the rocket mass at the next moment through Kalman filtering, and simultaneously using the rocket mass identification result at the next moment to estimate the thrust parameters at the next moment through recursive least squares with a fading factor.

[0007] Furthermore, the specific steps include the following:

[0008] Establish the equations of motion for the center of mass of the launch vehicle and the equations of mass consumption:

[0009]

[0010] Where: r represents the vector distance of the rocket from the Earth's center, and r = [r x r y r z ] T v represents the rocket's velocity vector, and v = [v x v y v z ] T μ represents the gravitational constant; F represents the total thrust of the rocket; u represents the thrust direction vector input; m represents the mass of the rocket; C represents the process noise distribution matrix; w represents the process noise; I sp g represents the rocket's specific impulse; g0 represents the gravitational acceleration at sea level;

[0011] The observation equations for apparent acceleration are established as follows:

[0012] y = Fu / m + d

[0013] Where y represents the observed apparent acceleration information; d represents the measurement noise of the apparent acceleration. Let the observation matrix h(m,u) = u / m;

[0014] Choose state variable x = [r x r y r z v x v y v z m]T Then the mass of the rocket is m = x7. Transforming the above rocket equations and observation equations into state-space equations:

[0015]

[0016] Where f(x,u,F) and g(x,u,F) represent the corresponding functions;

[0017] Calculate the Jacobian matrix equations for f(x,u,F) and g(x,u,F).

[0018] Setting process noise variance Q w Observation noise variance R d The fading factor ρ;

[0019] Initialize state estimation initial value Initial mass value Initial values ​​for thrust parameter estimation The initial value of the initial state estimation covariance matrix is ​​P(0); the initial value of the initial least squares covariance matrix is ​​P. Ls (0);

[0020] Predict the state estimate at time k+1:

[0021]

[0022] Where Δt=t k+1 -t k ;

[0023] Calculate the transition matrix:

[0024] Φ(k+1|k)=e A(k+1)Δt

[0025] Calculate the discrete noise distribution matrix:

[0026] Γ(k+1|k)=Φ(k+1|k)C(k+1)Δt

[0027] Calculate the covariance matrix corresponding to the state prediction:

[0028]

[0029] Calculate the Kalman filter gain matrix:

[0030] K(k+1)=P(k+1|k)H T (k+1)[H(k+1)P(k+1|k)H T (k+1)+R d (k+1)] -1

[0031] Calculate the state estimation covariance matrix at time k+1:

[0032] P(k+1)=[IK(k+1)H(k+1)]P(k+1|k)

[0033] Where I represents the identity matrix corresponding to the state dimension;

[0034] Calculate the state filter value at time k+1:

[0035]

[0036] The quality estimate at time k+1 is obtained using the state filter value at time k+1. Right now:

[0037]

[0038] Calculate the least-squares gain matrix:

[0039]

[0040] Among them I M Represents the identity matrix corresponding to the observation dimension;

[0041] Calculate the least-squares covariance matrix at time k+1.

[0042]

[0043] Calculate the estimated thrust parameters at time k+1:

[0044]

[0045] If k < N, then k = k + 1, and return the estimated state value at time k + 1; otherwise, the loop ends and the identification results of the launch vehicle mass and thrust parameters are output.

[0046] Furthermore, A(k+1) in the transition matrix specifically represents the predicted state value at time k+1. Vector input u(k+1) at time k+1 and estimated thrust parameters at time k. Substituting this into matrix equation A, we get A(k+1).

[0047] Furthermore, H(k+1) in the Kalman filter gain matrix specifically represents the predicted state value at time k+1. Vector input u(k+1) at time k+1 and estimated thrust parameters at time k. Substituting this into the matrix equation H, we get H(k+1).

[0048] Furthermore, in the least squares gain matrix Specifically, this involves using the mass estimate at time k+1. Substituting the vector input u(k+1) into the observation matrix yields...

[0049] Furthermore, linear interpolation is performed on the apparent acceleration observations to increase the total number of sampling points and reduce the identification step size.

[0050] The beneficial effects of this invention include: proposing a joint correction and identification method combining least squares and Kalman filtering, which can effectively identify the mass and thrust parameters of a launch vehicle under thrust failure, and is also applicable to the online identification of the mass and thrust parameters of a spacecraft under normal flight conditions; the proposed interpolation improvement method for sampled observation data can effectively improve the tracking performance of the identification. By selecting appropriate fading factors and interpolation step sizes, the tracking performance of the parameter identification method can be improved within the acceptable range of parameter accuracy error.

[0051] This invention can identify thrust abrupt change faults in rockets and other aircraft. It features simple structure, concise design process, and high identification accuracy. It has application prospects in the identification of mass and thrust faults in launch vehicles and provides a reference for the identification of rocket mass and thrust parameters. Attached Figure Description

[0052] Figure 1 This is a diagram illustrating the joint correction and identification scheme for launch vehicle mass and other state quantities and thrust parameters according to the present invention.

[0053] Figure 2 This is a flowchart of the method for joint correction and identification of launch vehicle mass and thrust parameters under thrust failure proposed in this invention. Detailed Implementation

[0054] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0056] This invention proposes a joint correction and identification method combining least squares and Kalman filtering for identifying the mass and thrust parameters of launch vehicles. This method is particularly suitable for online identification of the mass and thrust parameters of aircraft under thrust failure conditions, and is also suitable for online identification of the mass and thrust parameters of aircraft under normal flight conditions.

[0057] The method for jointly identifying launch vehicle mass and thrust parameters includes the following steps:

[0058] To address the joint identification problem of launch vehicle mass and thrust, we establish the motion equations of the rocket's center of mass and mass consumption equations for identification.

[0059] The apparent acceleration information measured by the rocket's inertial sensing device is used as the identification observation equation;

[0060] To improve the tracking performance of the identification, the sampled visual acceleration information can be interpolated, thereby reducing the identification step size and improving the method's ability to track sudden changes.

[0061] The rocket mass is identified by using Kalman filtering based on the thrust parameter identification results. Simultaneously, the thrust parameters are estimated by recursive least squares based on the rocket mass identification results, thereby achieving joint identification of mass and thrust parameters.

[0062] Example 1

[0063] This embodiment studies the motion of the center of mass during the rocket's vacuum phase, assuming that the engine's specific impulse is known throughout the entire process. The joint identification method for the launch vehicle's mass and thrust parameters includes the following steps:

[0064] Step 1: Establish the rocket's center of mass motion equation (taking the vacuum phase as an example) and mass consumption equation for the joint identification problem of launch vehicle mass and thrust:

[0065]

[0066] Where: r represents the vector distance of the rocket from the Earth's center, and r = [r x r y r z ] T v represents the rocket's velocity vector, and v = [v x v y v z ] T μ represents the gravitational constant; F represents the total thrust of the rocket; u represents the thrust direction vector input, and u = [u1 u2 u3] T m represents the rocket's mass; C represents the process noise distribution matrix; w represents the process noise; I sp g represents the rocket's specific impulse; g0 represents the gravitational acceleration at sea level.

[0067] Step 2: Using the apparent acceleration information measured by the inertial sensing device as the observation for identification, the observation equation is established as follows:

[0068] y=Fu / m+d (2)

[0069] Where y represents the observed apparent acceleration information; d represents the measurement noise of the apparent acceleration. Let the observation matrix h(m,u) = u / m, then the observation equation can also be written as:

[0070] y=h(m,u)F+d (3)

[0071] Step 3: Select the state variable x = [r x r y r z v x v y v z m] T Therefore, the mass of the rocket is m = x7. Transforming the above rocket equations and observation equations into state-space equations gives:

[0072]

[0073] Where f(x,u,F) and g(x,u,F) represent the corresponding functions, and their specific forms are as follows:

[0074]

[0075]

[0076] Step 4: Calculate the Jacobian matrix equations for f(x,u,F) and g(x,u,F).

[0077] The non-zero elements in matrix equations A and H are represented as follows:

[0078] A(1,4)=1, A(2,5)=1, A(3,6)=1,

[0079]

[0080]

[0081] A(4,7)=-Fu1m -2 ,

[0082]

[0083] A(5,7)=-Fu2m -2 ,

[0084]

[0085]

[0086] A(6,7)=-Fu3m -2(7)

[0087] H(1,7)=-Fu1m -2

[0088] H(2,7)=-Fu2m -2 ,

[0089] H(3,7)=-Fu3m -2 (8)

[0090] Step 5: Set the process noise variance Q w Observation noise variance R d The fading factor ρ.

[0091] Step 6: Initialize initial values ​​for state estimation Then there is an initial value for the mass. Initial values ​​for thrust parameter estimation Let P denote the state estimation covariance matrix, then initialize the initial value of the state estimation covariance matrix to P(0); let P Ls Let P represent the least squares covariance matrix. Then, initialize the least squares covariance matrix with the initial value P. Ls (0), and we have:

[0092]

[0093] Steps 7 to 20 constitute one calculation cycle of the joint correction and identification method. The joint correction and identification method is a process that continuously cycles through the following calculation cycles based on the above steps. The total number of sampling points is N.

[0094] Preferably, by performing linear interpolation on the original sampled data, i.e. the apparent acceleration observation, the total number of sampling points can be increased, thereby reducing the identification step size and improving the identification and tracking capability of the method.

[0095] Step 7: Based on the state estimate at time k and thrust parameter estimates Predict the state estimate at time k+1:

[0096]

[0097] Where Δt=t k+1 -t k .

[0098] Step 8: Predict the state estimate at time k+1. Vector input u(k+1) at time k+1 and estimated thrust parameters at time k. Substituting this into matrix equation A, we get A(k+1).

[0099] Step 9: Calculate the transition matrix:

[0100] Φ(k+1|k)=e A(k+1)Δt (11)

[0101] Step 10: Calculate the discrete noise distribution matrix:

[0102] Γ(k+1|k)=Φ(k+1|k)C(k+1)Δt (12)

[0103] Step 11: Calculate the covariance matrix corresponding to the state prediction value based on the state estimation covariance matrix P(k) at time k:

[0104]

[0105] Step 12: Predict the state estimate at time k+1. Vector input u(k+1) at time k+1 and estimated thrust parameters at time k. Substituting this into the matrix equation H, we get H(k+1).

[0106] Step 13: Calculate the Kalman filter gain matrix:

[0107] K(k+1)=P(k+1|k)H T (k+1)[H(k+1)P(k+1|k)H T (k+1)+R d (k+1)] -1 (14)

[0108] Step 14: Calculate the state estimation covariance matrix at time k+1:

[0109] P(k+1)=[IK(k+1)H(k+1)]P(k+1|k) (15)

[0110] Where I represents the identity matrix corresponding to the state dimension.

[0111] Step 15: Correct the predicted state based on the observation at time k+1 to obtain the state filter value at time k+1:

[0112]

[0113] Step 16: Obtain the quality estimate at time k+1 using the state filter value at time k+1. Right now,

[0114]

[0115] Step 17: Use the mass estimate at time k+1 Substituting the vector input u(k+1) into the observation matrix in step 2, we obtain... And calculate the least-squares gain matrix:

[0116]

[0117] Among them I M This represents the identity matrix corresponding to the observation dimension.

[0118] Step 18: Calculate the least-squares covariance matrix at time k+1.

[0119]

[0120] Step 19: Calculate the estimated thrust parameters at time k+1:

[0121]

[0122] Step 20: If k < N, then k = k + 1, return to step 8; otherwise, the loop ends and the rocket mass and thrust parameter identification results are output.

[0123] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for joint calibration and identification of launch vehicle mass and thrust parameters under thrust failure, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: ； where: represents the vector from the center of the Earth to the rocket, and ; represents the velocity vector of the rocket, and ; represents the gravitational constant; represents the total thrust of the rocket; represents the thrust direction vector input, represents the mass of the rocket; represents the process noise distribution matrix; represents the process noise; represents the specific impulse of the rocket; represents the acceleration due to gravity at sea level; The method comprises the following steps: ； wherein represents observed visual acceleration information; represents measurement noise of visual acceleration; and represents an observation matrix Selecting state variables , the mass of the rocket The rocket equation and the observation equation are converted into state space equations as follows: ； wherein and denote the respective functions; Computing and the Jacobian matrix equation , ; Setting process noise variance , observation noise variance , fading factor ; initializing state estimate initial values , mass initial values , thrust parameter estimate initial values ; initializing state estimate covariance matrix initial values ; initializing least squares covariance matrix initial values ; Prediction State estimate at time instant: ； wherein ; The method comprises the following steps: ； The method comprises the following steps: ； The method comprises the following steps: ； The method comprises the following steps: ； Calculations State estimation covariance matrix at time instant: ； wherein denotes the identity matrix of the corresponding state dimension; Calculations State filter values at time instant: ； Utilizing The state filter value at the time instant is obtained The quality estimate value at the time instant That is: ； The method comprises the following steps: ； wherein denotes the identity matrix for the corresponding observation dimension; Computing Least-squares covariance matrix of time instants: ； Calculations momentary thrust parameter estimate: ； If , then , return the predicted state estimation at the time instant; otherwise, the loop ends and the launch vehicle mass and thrust parameter identification results are output.

2. The method of claim 1, wherein, in the transition matrix Specifically, the predicted state estimate at time , vector input at time and thrust parameter estimate at time are substituted into the matrix equation to obtain .

3. The method of claim 1, wherein, The Kalman filter gain matrix is Specifically, the predicted state estimate at time t , vector input at time t and thrust parameter estimate at time t are substituted into the matrix equation to obtain .

4. The method of claim 1, wherein, in the least squares gain matrix Specifically, the method comprises the following steps: The quality estimation value at the moment And the vector input Substituted into the observation matrix to obtain .

5. The method of claim 1, wherein, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the

Images