Distributed ground simulation method for mars aerodynamic assisted deorbit

Abstract

A distributed ground simulation method for aerodynamically assisted Mars orbit descent belongs to the field of spacecraft system simulation. It employs a database to store aerodynamic data under high, medium, and low lift-to-drag ratio scenarios, covering the entire Martian flight path, thus having a wide applicability to various spacecraft types and strong universality. The method uses the principle of similarity equivalence to equivalence the spacecraft model, improving the realism of the ground simulation. By establishing a random disturbance model and adding random disturbance signals to the control system, it simulates disturbances in the real environment, more closely approximating the actual spacecraft environment and improving adaptability to complex dynamic environments. This invention is applicable to the field of spacecraft system simulation. By establishing a dynamic model, applying the principle of similarity equivalence, and designing a distributed data transmission framework, it realizes distributed ground simulation of aerodynamically assisted Mars orbit descent, improving the simulation's universality and anti-interference capability, and enhancing the safety of Mars orbit descent.

Description

Technical Field

[0001] This invention relates to a distributed ground simulation method for aerodynamically assisted Mars orbit descent, belonging to the field of spacecraft system simulation. Background Technology

[0002] Mars exploration is a crucial part of human deep space exploration, and orbit descent is the essential process for spacecraft to enter the Martian atmosphere from high orbit and land, traversing three flow regions: the rarefied flow region, the transitional flow region, and the continuous flow region. To ensure each mission achieves its intended objectives, conducting equivalent ground-based experiments is necessary. By simulating the entire orbit descent process, various problems can be identified and solutions developed.

[0003] The prior art [1] (see: He Zhaowei, Shi Peng, Ge Bing et al. Similarity analysis method for spacecraft ground experiments [J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, 38(4):502-508) gives a similarity analysis method for spacecraft ground equivalence, but does not carry out a universal ground equivalent experimental mission design. The prior art [2] (see: Lin Hanzheng, Hu Haixia, Tang Liang. Design platform for spacecraft control system scheme [J]. Computer Simulation, 2016, 33(02):73-77+243) gives a distributed framework design method for spacecraft control system and verifies the feasibility of the framework through numerical simulation, but does not consider how to make the spacecraft move in space on the ground equivalent, nor does it consider the impact of random disturbances on the control system.

[0004] Therefore, for the distributed ground simulation of Mars aerodynamic-assisted orbit descent, it is necessary to provide a spacecraft ground similarity equivalent method, design a distributed data transmission framework, consider the impact of random disturbances on the control system, and finally realize the distributed ground equivalent simulation of Mars aerodynamic-assisted orbit descent. Summary of the Invention

[0005] To address the shortcomings of existing technologies in the field of aerodynamic assisted orbit descent simulation, such as the lack of universality of ground-based equivalent experimental methods and the failure to consider random interference in ground simulations, the main objective of this invention is to propose a distributed ground simulation method for aerodynamic assisted orbit descent on Mars. By establishing a dynamic model, applying the principle of similarity equivalence, and designing a distributed data transmission framework, a distributed ground simulation of aerodynamic assisted orbit descent on Mars can be achieved, thereby improving the universality and anti-interference capability of the simulation and enhancing the safety of Mars orbit descent.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] This invention discloses a distributed ground simulation method for aerodynamically assisted descent of Mars. First, a polar coordinate dynamic model corresponding to the aerodynamically assisted descent process is established. Then, aerodynamic data and initial state parameters are obtained from an existing database. Next, based on the principle of dimensional equivalence, the initial state parameters are similarly equivalent to the prototype, yielding state parameters equivalent to the prototype. Then, a random disturbance model is constructed. Following this, based on the aforementioned state parameters, scaling factors, and random signals generated by the random disturbance model, the guidance and control system performs integrated control to obtain control commands. Finally, the control commands and state parameters are input into the aforementioned dynamic model to achieve dynamic recursion, obtaining new state parameters. Based on the recursively obtained state parameters, a dynamic simulation demonstration is performed. Based on the recursively obtained state parameters, new aerodynamic data is acquired for subsequent distributed ground simulation, improving the simulation's universality and anti-interference capability.

[0008] This invention discloses a distributed ground simulation method for aerodynamically assisted Mars orbit descent, comprising the following steps:

[0009] Step 1: Establish a polar coordinate dynamic model of the Mars aerodynamic assisted descent process;

[0010] The polar coordinate dynamic model of the Mars aerodynamic-assisted descent process is as follows:

[0011]

[0012] Where r is the radial distance from the Mars center to the spacecraft's center of mass; θ is the longitude; φ is the latitude; V is the spacecraft's velocity relative to Mars; γ is the trajectory angle of the velocity vector relative to Mars; ψ is the heading angle; σ is the roll angle; g r and g φ These represent the radial and latitudinal components of gravitational acceleration, respectively. L and D represent aerodynamic lift acceleration and drag acceleration, respectively.

[0013]

[0014] Where ρ is the atmospheric density; S is the reference area of ​​the aircraft; C L C D These are the lift coefficient and drag coefficient, respectively; m is the spacecraft mass.

[0015] Step 2: Establish an initial database based on aerodynamic data and reference trajectory data corresponding to different flow regions in the Martian environment;

[0016] Step 3: Generate random disturbance sequences based on the normal distribution model and establish a basic model of random disturbances;

[0017] The basic model of random disturbance is:

[0018]

[0019] Where x1 is a random variable; μ1 and σ1 are the mean and variance of the normal distribution, respectively; A is the generated random number; the random sequence generated based on this model is [k1A1; k2A2...k...]. n A n ],k1,k2...k n These are the coefficients of the n random numbers generated, where n is a positive integer.

[0020] Step 4: Establish a guidance and control model;

[0021] 4.1 Establish the mathematical model for the angle of attack: α = f(Ma);

[0022] 4.2 Establish the mathematical model for the tilt angle: σ = g(t);

[0023] 4.3. Optimize to obtain guidance commands α and σ;

[0024] 4.4 Select state observations and establish the state observation matrix x′;

[0025] 4.5 Select the control variable u = [α, σ], where α and σ are the angle of attack and the heel angle, respectively;

[0026] 4.6 Establish a control model Where d(t) represents the random disturbance at different times.

[0027] Step 5: Obtain pneumatic data C through database interpolation. L C D ;

[0028] 5.1 Given state parameters

[0029] 5.2. α is calculated based on the mathematical model of angle of attack in 4.1;

[0030] 5.3. Based on the state parameters given in 5.1, calculate Ma corresponding to the initial state parameter state;

[0031] Ma = V / V c

[0032] Where V c This represents the speed of sound in the Martian environment.

[0033] 5.4. Based on α and Ma obtained in 5.2 and 5.3, interpolate C in the database. L C D .

[0034] Step 6: Use dimensional analysis to obtain the similarity conditions between the model and the prototype, obtain the equivalent scaling factor between the prototype and the model, and thus obtain the equivalent initial state parameters.

[0035] 6.1. Based on the dynamic model established in step 1, obtain the prototype motion variables that need to be equivalent;

[0036] The prototype variables are shown in the table below:

[0037]

[0038] 6.2. The dimensions of the orbital semi-major axis s, spacecraft mass m, and motion time P in the prototype variables in 6.1 are taken as the basic dimensions, and the dimensions of other prototype variables are derived from the basic dimensions.

[0039] 6.3. Determine the equivalent scaling factors corresponding to the three basic dimensions, and then determine the equivalent scaling factors corresponding to the other parameters. The equivalent scaling factors corresponding to the three basic dimensions are obtained according to the following formula:

[0040]

[0041] i = 1, 2, 3, representing the semi-major axis of the orbit s, the mass of the spacecraft m, and the motion time P, respectively;

[0042] Based on the equivalent scaling factor of the basic dimensions, the equivalent scaling factors of other prototype variables are obtained using the following formula:

[0043]

[0044] Equivalent scaling factor matrix [λ s ,λ m ,λ P ,λ v ,λ F ,λ a ,λ Ω ,λ α ]; where λ s ,λ m ,λ P ,λ v ,λ F ,λ a ,λ Ω ,λ α These are the effective scaling factors for the orbital semi-major axis s, spacecraft mass m, motion time P, force F, velocity v, acceleration a, angular velocity Ω, and angular acceleration α, respectively.

[0045] 6.4. Using the equivalent scaling factor obtained in 6.3, obtain the equivalent initial state parameters.

[0046]

[0047] 6.5. The equivalent initial state parameters are demonstrated through a visualization system.

[0048] Step 7: Based on the equivalent initial state parameters and equivalent scaling factor matrix obtained in Step 6, generate guidance commands through the guidance control model;

[0049] 7.1 Obtain the reference trajectory state parameters according to step 2.

[0050] 7.2 Based on the equivalent initial state parameters and equivalent scaling factor matrix obtained in step 6, the guidance commands α and σ are obtained from the dynamic model and guidance model in step 1.

[0051] 7.3 α and σ are input into the control model as reference control variables;

[0052] 7.4 Input the random interference signal generated in step 3 into the control model;

[0053] 7.5. Generate control commands based on the control model in step 5, and generate control commands that can actually be achieved.

[0054] Step 8: Input the control command obtained in Step 7 and the state parameters obtained in Step 6 into the dynamic model of Step 1 to perform dynamic recursion and obtain new state parameters;

[0055] Step 9: Demonstrate the state parameters obtained in Step 8 using a visualization model;

[0056] Step 10: Repeat steps 5, 7, 8, and 9 until the simulation terminal constraint is reached, thus completing the distributed simulation of Mars aerodynamic assisted orbit descent.

[0057] Beneficial effects:

[0058] 1. The present invention discloses a distributed ground equivalent simulation method for aerodynamic-assisted Mars orbit descent. It uses a database to store aerodynamic data under three scenarios with high, medium and low lift-drag ratios, covering the entire Mars flight domain. Therefore, it has a wide range of applicability to different types of spacecraft and has the advantage of strong universality.

[0059] 2. The present invention discloses a distributed ground equivalent simulation method for Mars aerodynamic-assisted orbit descent, which uses the principle of similarity equivalence to make the spacecraft model equivalent, thereby improving the realism of the ground simulation.

[0060] 3. The distributed ground equivalent simulation method for Mars aerodynamic-assisted orbit descent disclosed in this invention establishes a random disturbance model and adds random disturbance signals to the control system to simulate disturbances in the real environment, which can more closely approximate the actual situation of the spacecraft environment and improve the adaptability to complex dynamic environments. Attached Figure Description

[0061] Figure 1 This is a flowchart of a distributed ground simulation method for aerodynamically assisted Mars orbit descent disclosed in this invention;

[0062] Figure 2 This is the distributed framework of the Mars aerodynamic-assisted orbit descent simulation system in this embodiment;

[0063] Figure 3 This is a schematic diagram illustrating the change of height h with time t in this embodiment;

[0064] Figure 4 This is a schematic diagram illustrating the variation of the track inclination angle γ with altitude h in this embodiment; Specific implementation methods

[0065] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. The technical problems solved by the present invention and its beneficial effects are also described. It should be noted that the described embodiments are only intended to facilitate understanding of the present invention and do not constitute any limitation thereof.

[0066] This embodiment discloses a distributed ground-based equivalent simulation method for aerodynamically assisted Mars orbit descent. It employs four interconnected computers, all operating in a Windows environment, with the code running in the Visual Studio simulation environment for equivalent simulation. Figure 1 As shown, the specific steps are as follows:

[0067] Step 1: Establish the polar coordinate dynamic equations

[0068]

[0069] Where r is the radial distance from the Mars center to the spacecraft's center of mass; θ is longitude; φ is latitude; V is the spacecraft's velocity relative to Mars; γ is the trajectory angle of the velocity vector relative to Mars; ψ is the heading angle (positive when projecting the relative velocity vector from due north to Earth onto the local horizontal plane); σ is the tilt angle; g r and g φ These are the radial and latitudinal components of gravitational acceleration, respectively.

[0070] Step 2: Input the aerodynamic data parameters corresponding to different flow regions under the Martian environment into the database to establish the initial database, and import the reference trajectory data;

[0071] 2.1 Import aerodynamic data and construct the initial database. This embodiment uses a low lift-to-drag ratio aircraft for simulation.

[0072] 2.2 Import reference trajectory data. In this embodiment, trajectory data based on smoothing filtering is imported.

[0073] Step 3: Generate random disturbance sequences based on the normal distribution model and establish a basic model of random disturbance;

[0074]

[0075] Step 4: Establish a guidance and control model;

[0076] 4.1 Mathematical Model of Angle of Attack: This embodiment uses a pre-fitted velocity angle of attack profile.

[0077]

[0078] 4.2 Mathematical Model of Tilt Angle

[0079]

[0080] Where t s This refers to the tilt angle command switching time;

[0081] 4.3 Iterative optimization yields t s Thus, the σ instruction is obtained;

[0082] 4.4 Select the state observation as x'=[h,V,γ];

[0083] 4.5 Select the control variable as u = σ;

[0084] 4.6 Establishing a control model

[0085] Step 5: Obtain pneumatic data C through database interpolation. L C D ;

[0086] 5.1 Given state parameters

[0087] 5.2 α is calculated based on the mathematical model of angle of attack in 4.1.

[0088] 5.3 Calculate Ma corresponding to the initial state parameters according to 5.1. The calculation formula is as follows:

[0089] Ma = V / V c (5)

[0090] Where V c This represents the speed of sound in the Martian environment.

[0091] 5.4 Based on α and Ma obtained in 5.2 and 5.3, C is obtained by interpolation in the database. L C D ;

[0092] Step 6: Use dimensional analysis to obtain the similarity conditions between the model and the prototype, obtain the equivalent scaling factor between the prototype and the model, and thus obtain the equivalent initial state parameters.

[0093] 6.1 Considering the dynamic equations are polar coordinate equations, the spacecraft mass M is 950 kg, length is 1.8 m, and the total descent time t is 170 s; the semi-major axis of the orbital flight is R, which is 3537 km; the semi-major axis of the experimental environment is L, which is 6 m; the experimental spacecraft mass m is 5 kg; and the time is 1 minute, then the length and time scaling factors are:

[0094]

[0095] According to the theory of the perfect similarity model, the equivalent scaling factor matrix is:

[0096] [λ s ,λ m ,λ P ,λ v ,λ F ,λ a ,λ Ω ,λ α = [1965000,190,2.83,694396.3,46616888.7,245352,0.353,0.125]

[0097] 6.2 Initial state variables:

[0098] x0=[3537000,-116.5,-46.7,6020,-5.91,0,0] T

[0099] Initial state variables after equivalent transformation:

[0100] x0=[6,-116.5,-46.7,0.00087,-5.91,0,0] T

[0101] 6.3 The equivalent initial state parameters will be demonstrated through a visualization system.

[0102] Step 7: Based on the equivalent initial state parameters and equivalent scaling factor matrix obtained in Step 6, generate guidance control commands through the guidance control model;

[0103] 7.1 Obtain the reference trajectory state parameters according to step 2.

[0104] 7.2 Obtain the aerodynamic data C based on step 5. L C D ;

[0105] 7.3 Based on the equivalent initial state parameters and equivalent scaling factor matrix obtained in step 6, the guidance commands α and σ are obtained from the dynamic model and guidance model in step 1;

[0106] 7.4 α and σ are used as reference control inputs into the control model;

[0107] 7.5 Generate a random disturbance signal according to step 3 and input it into the control model;

[0108] 7.6 Generate control commands based on the control model in step 5. Generate control commands that can actually be achieved.

[0109] Step 8: Based on the control commands and pneumatic data C obtained in Step 7 L C D The equivalent state parameters obtained in step 6 are input into the dynamic model in step 1 to perform dynamic recursion and obtain new state parameters.

[0110] Step 9: Demonstrate using a visualization model based on the state parameters obtained in Step 8.

[0111] Step 10: Repeat steps 5, 7, 8, and 9 above until the simulation terminal constraint is reached, completing the distributed simulation of Mars aerodynamic assisted orbit descent.

[0112] The distributed framework includes: a database module, a dynamics module, a guidance and control module, a random interference module, and a visual demonstration module.

[0113] The module consists of several modules: a database module for storing aerodynamic data and state parameters; a dynamics module for performing dynamic recursion; a control program module for storing guidance and control programs and generating control commands; a random disturbance module for generating random signals; and a visual demonstration module for demonstrating the simulation process.

[0114] First, a dynamic model, a random disturbance model, a database model, and a control and guidance model are established. Second, initial state parameters are input for similarity equivalence. Third, the equivalent initial state parameters and random disturbances are input into the guidance and control model to generate control commands. Finally, based on the control commands and equivalent state parameters, dynamic recursion and visualization are realized to achieve distributed ground simulation of Mars aerodynamic-assisted orbit descent.

[0115] Appendix Figure 3 , 4 The figures show the altitude change curve over time and the relationship between altitude and track angle in the simulation results of this embodiment, respectively, which verify the feasibility of the method proposed in this invention and the stability of the system under environmental interference.

[0116] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is used to explain the present invention. It is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed ground simulation method for aerodynamically assisted Mars orbit descent, characterized in that: Includes the following steps, Step 1: Establish a polar coordinate dynamic model of the Mars aerodynamic-assisted orbit descent process; Step 2: Establish an initial database based on aerodynamic data and reference trajectory data corresponding to different flow regions in the Martian environment; Step 3: Generate random disturbance sequences based on the normal distribution model and establish a basic model of random disturbance; Step 4: Establish a guidance and control model; Step 5: Obtain aerodynamic data through database interpolation. , These are the lift coefficient and the drag coefficient, respectively. Step 6: Use dimensional analysis to obtain the similarity conditions between the model and the prototype, obtain the equivalent scaling factor between the prototype and the model, and thus obtain the equivalent initial state parameters. Step 7: Based on the equivalent initial state parameters and equivalent scaling factor matrix obtained in Step 6, generate guidance commands through the guidance control model; Step 8: Input the guidance command obtained in Step 7 and the initial state parameters obtained in Step 6 into the dynamic model of Step 1 to perform dynamic recursion and obtain new state parameters; Step 9: Demonstrate the new state parameters obtained in Step 8 using a visualization model; Step 10: Repeat steps 5, 7, 8, and 9 until the simulation terminal constraint is reached, completing the distributed simulation of Mars aerodynamic assisted orbit descent.

2. The distributed ground simulation method for Mars aerodynamic-assisted orbit descent as described in claim 1, characterized in that: The implementation method for step 1 is as follows: The polar coordinate dynamic model of the Mars aerodynamic-assisted descent process is as follows: in, This represents the radial distance from the fire core to the spacecraft's center of mass. Longitude; Latitude; The velocity of the spacecraft relative to Mars; The trajectory angle is the velocity vector relative to Mars. For heading angle; It is the tilt angle; and These represent the radial and latitudinal components of gravitational acceleration, respectively; L and D represent aerodynamic lift acceleration and drag acceleration, respectively. in, Where is atmospheric density; S is the reference area of ​​the aircraft; These are the lift coefficient and drag coefficient, respectively; m is the spacecraft mass.

3. The distributed ground simulation method for Mars aerodynamic-assisted orbit descent as described in claim 2, characterized in that: The implementation method for step 3 is as follows: The basic model of random disturbance is: in It is a random variable; and Let A and V be the mean and variance of the normal distribution, respectively; A is the generated random number; the random sequence generated based on this model is: , These are the coefficients of the n random numbers generated, where n is a positive integer.

4. The distributed ground simulation method for Mars aerodynamic-assisted orbit descent as described in claim 3, characterized in that: The implementation method for step 4 is as follows: 4.1 Establishing a mathematical model for the angle of attack ; 4.2 Establishing a mathematical model for the tilt angle ; 4.3 Optimization to obtain guidance commands ; 4.4 Select state parameters and establish the state observation matrix. ; 4.5 Selecting the control variable ,in These are the angle of attack and the angle of roll, respectively. 4.6 Establish a control model ,in This represents random disturbances at different times.

5. The distributed ground simulation method for Mars aerodynamic-assisted orbit descent as described in claim 4, characterized in that: The implementation method for step 5 is as follows: 5.1 Given the state parameter matrix ; 5.

2. Based on the mathematical model of angle of attack in 4.1, the following calculations were performed. ; 5.

3. Based on the state parameters given in 5.1, calculate Ma corresponding to the initial state parameter state; in This represents the speed of sound in the Martian environment. 5.

4. Obtained from 5.2 and 5.3 And Ma, interpolated in the database to obtain .

6. The distributed ground simulation method for Mars aerodynamic-assisted orbit descent as described in claim 5, characterized in that: The implementation method for step 6 is as follows: 6.

1. Based on the dynamic model established in step 1, obtain the prototype motion variables that need to be equivalent; The prototype variables are shown in the table below: 6.

2. The dimensions of the orbital semi-major axis s, spacecraft mass m, and motion time P in the prototype variables in 6.1 are taken as the basic dimensions, and the dimensions of other prototype variables are derived from the basic dimensions. 6.

3. Determine the equivalent scaling factors corresponding to the three basic dimensions, and then determine the equivalent scaling factors corresponding to the other parameters; the equivalent scaling factors corresponding to the three basic dimensions are obtained according to the following formula: i = 1, 2, 3, representing the orbital semi-major axis s, spacecraft mass m, and motion time P, respectively; Based on the equivalent scaling factor of the basic dimensions, the equivalent scaling factors of other prototype variables are obtained using the following formula: Equivalent scaling factor matrix ;in These are the equivalent scaling factors for the orbital semi-major axis s, spacecraft mass m, motion time P, force F, velocity v, acceleration a, angular velocity Ω, and angular acceleration α, respectively. 6.

4. Using the equivalent scaling factor obtained in 6.3, obtain the equivalent initial state parameters. ； 6.

5. The equivalent initial state parameters are demonstrated through a visualization system.

7. The distributed ground simulation method for Mars aerodynamic-assisted orbit descent as described in claim 6, characterized in that: The implementation method for step 7 is as follows: 7.1 Obtain the reference trajectory state parameters according to step 2. 7.

2. Based on the equivalent initial state parameters and equivalent scaling factor matrix obtained in step 6, the guidance command is obtained from the dynamic model and guidance model in step 1. ; 7.3 It is input into the control model as a reference control quantity; 7.4 Input the random interference signal generated in step 3 into the control model; 7.

5. Generate control commands based on the control model in step 5, generating control commands that can actually be achieved.

Images