The present application claims priority from Chinese Application Number 202011262295.1, filed Nov. 12, 2020, the disclosure of which is hereby incorporated by reference herein in its entirety.
The present invention relates to the technical field of launch vehicles under thrust drop fault, in particular to an optimal rescue orbital elements online decision-making method based on RBFNN for launch vehicles under thrust drop fault.
As a rocket power plant, the engine is a decisive factor for the reliability and safety of a rocket. Its reliability is related to the success or failure of the entire flight mission. In the actual flight mission, when the thrust drop fault of the launch vehicle engine occurs, it is difficult to complete the mission if the guidance and control scheme under nominal ballistic conditions is continued. In order to avoid the fall of the payload, it is necessary to decide the optimal rescue orbital elements online according to the fault state so that the payload enters into the rescue orbit.
At present, for the problem of online rescue under the thrust drop failure, the main idea is to couple the two problems of rescue trajectory decision and trajectory optimization based on the dynamic model for online optimization [1,2]. SONG [1] proposed an autonomous rescue strategy and algorithm for launch vehicle thrust descent failure, combined with the injection point geocentric angle estimation, convex optimization, adaptive collocation method, etc., to provide a good online solution for the original coupling optimization problem, online providing a good initial value for the original coupling optimization problem to improve the computational efficiency of online trajectory planning LI [2] proposed an online trajectory optimization algorithm for launch vehicles based on convex optimization to obtain a high-precision and high-efficiency online trajectory optimization method. The above method couples the two problems of rescue trajectory decision and flight trajectory together and optimizes them. Due to the unknown rescue orbit, the search space of the optimal solution is very large, which affects the calculation efficiency of the online part.
According to the problems exist in the prior art, the present invention provides an optimal rescue orbital elements online decision-making method based on RBFNN for launch vehicles under thrust drop fault, specifically comprising:
Furthermore, when constructing the optimization problems of maximum semi-major axis:
wherein r and v represent the position and the velocity vector of launch vehicles; μ=GM is an earth's gravitational constant; m represents a total mass of the launch vehicles, and Isp represents a specific impulse of the engine of the launch vehicles; u=[ux,uy,uz]T is a component of a thrust unit vector of the engine; when the engine fault occurs, a percentage of thrust drop is η; a thrust magnitude is (1−η)Tnom, wherein Tnom is a nominal thrust of the engine; in the case of a thrust drop fault, the specific impulse of the engine remains unchanged, a propellant consumption per second decreased η, and a total flight time exceeds a nominal flight time; assuming the engine thrust drop failure occurs at t0, so the constraint condition of the starting point is expressed as follow:
x(t0)=x0 (4)
wherein x0 is the state of the starting point, a nonlinear relationship from the number of orbital elements to a terminal state is expressed as
[af,ef,if,Ωf,ωf]T=ψ(r(tf),v(tf)) (5)
wherein tf is a terminal moment; af,ef,if,Ωf,ωf is the orbital elements of the target orbit including the semi-major axis, eccentricity, inclination, longitude of the ascending node, argument perigee of the terminal point.
The orbital inclination ifres and the longitude of the ascending node Ωfres is in accordance with the fault states of launch vehicles. Due to the above technical scheme, the present invention provides an optimal rescue orbital elements online decision-making method based on RBFNN for launch vehicles under thrust drop fault, which has the following beneficial effects:
In order to explain more clearly the embodiment of this application or the technical scheme in the prior art, a brief description of the attached drawings required in the specific embodiment or the existing technical description is given below. Obviously, the attached drawings in the following description are only some of the embodiments recorded in this application. For general technical personnel in this field, other drawings can be obtained without creative work.
In order to make the technical scheme and advantages of the invention clearer, a clear and complete description of the technical scheme in the embodiments of the invention will be described in conjunction with drawings attached in the embodiments of the invention hereinafter.
wherein r and v represent the position and the velocity vector of launch vehicles; μ=GM is an earth's gravitational constant; m represents a total mass of the launch vehicles, and Isp represents a specific impulse of the engine of the launch vehicles; u=[ux,uy,uz]T is a component of a thrust unit vector of the engine; when the engine fault occurs, a percentage of thrust drop is η; a thrust magnitude is (1−η)Tnom, wherein Tnom is a nominal thrust of the engine; in the case of a thrust drop fault, the specific impulse of the engine remains unchanged, a propellant consumption per second decreased η, and a total flight time exceeds a nominal flight time; assuming the engine thrust drop failure occurs at t0, so the constraint condition of the starting point is expressed as follow:
x(t0)=x0 (4)
wherein x0 is the state of the starting point, a nonlinear relationship from the number of orbital elements to a terminal state is expressed as
[af,ef,if,Ωf,ωf]T=ψ(r(tf),v(tf)) (5)
wherein tf is a terminal moment; af,ef,if,Ωf,ωf is the orbital elements of the target orbit including the semi-major axis, eccentricity, inclination, longitude of the ascending node, argument perigee of the terminal point.
The orbital inclination ifres and the longitude of the ascending node Ωfres is in accordance with the fault states of launch vehicles.
The hidden unit is activated by the basis function, the Gaussian basis function is used in present application. The output of j-th hidden layer is:
wherein xRBFNN(i) is the i-th input data. μj is the center of the basis function of the hidden node, and σj is the expansion speed of the radial basis function. The center of the basis function and the expansion speed are determined by the training of the network. The output layer linearly combines the output of the hidden layer of the radial basis function to generate the expected output. The output of the k th node in the output layer is
Wherein wjk is the weight from the j th hidden layer neuron to the k output neuron. In order to achieve appropriate approximation accuracy, the following parameters are determined through training: the number of hidden layer neurons, the center of the basis function of each hidden layer neuron, and the weight of the radial basis function output passed to the summation layer.
In this section, simulation verification is conducted in the entire second stage flight phase of a launch vehicle, and the parameters are from reference [1]. The state distribution of the faults established by the sample set is based on the fault time is incremented by 1 s from 0 s to 375 s, and the proportion of thrust-drop is incremented by 1% from 13% to 40%. In the sample set, 90% of the data is randomly selected as the training set, and the remaining 10% of the data is used as the test set. A radial basis function neural network is used to establish a nonlinear mapping from the fault state to the optimal rescue orbital elements. The spread factor of RBF neural network training is 1, and the final number of trained hidden layer neurons is 50. In the test set, the optimal rescue orbital elements are determined by the RBF neural network are shown in
Table 1 is a comparison of the root mean square error (RMSE) of the results of different machine learning methods. The RMSE obtained by linear regression is larger than that of the nonlinear regression method, because the relationship from the fault state to the optimal rescue orbital elements are nonlinear, and it is suitable to use the nonlinear function approximation method. Compared with several other machine models, the RBF neural network model has a smaller RMSE, which means that it has a better function approximation effect in mapping the relationship from the fault state to the optimal rescue orbital elements.
The above is the preferred implement approach of the invention, the protection range is not limited to what mentioned above. Any technician who is familiar with this technical field replaces or revises technical schemes and inventive created on the basis of the present invention within the technical scope disclosed in this invention, schemes and inventive should be involved in the protection range of this invention.
Number | Date | Country | Kind |
---|---|---|---|
202011262295.1 | Nov 2020 | CN | national |
Number | Name | Date | Kind |
---|---|---|---|
9849785 | Ross | Dec 2017 | B1 |
11292617 | Gilkey | Apr 2022 | B1 |
20140379176 | Ross | Dec 2014 | A1 |
20210403182 | Weiss | Dec 2021 | A1 |
Entry |
---|
Z. Song et al.; Joint dynamic optimization of the target orbit and flight trajectory of a launch vehicle based on state-triggered indices; Acta Astronautica, 174(2020); pp. 82-93; Apr. 18, 2020. |
Y Li et al.; Online trajectory optimization for power system fault of launch vehicles via convex programming; Aerospace Science and Technology; ,98(2020)p. 1; pp. 1-10; Jan. 8, 2020. |
Number | Date | Country | |
---|---|---|---|
20220147820 A1 | May 2022 | US |