A joint attitude and orbit control method for dual spacecraft pursuit-escape game

Abstract

The present invention discloses a joint attitude and orbit control method for a dual-spacecraft pursuit game, wherein the dual-spacecraft include a tracking spacecraft and a target spacecraft, and the method includes the following steps: S1. Constructing the objective function of the tracking spacecraft for the orbital dynamics equation; S2. For the weight matrix of the objective function in step 1, using the untraceable particle filter method to estimate the Q and R matrices in the objective function, and solving the Nash equilibrium solution of the pursuit and escape parties under incomplete information; S3. Based on the weight matrix obtained by estimating the objective function by the untraceable particle filter, the Nash equilibrium strategy of the dual-spacecraft pursuit and escape problem under incomplete information is calculated; S4. When the distance between the pursuit and escape parties is less than or equal to the set threshold, the tracking spacecraft starts to adjust its attitude, uses the model predictive control to predict and control the target spacecraft, and adjusts the attitude of the tracking spacecraft according to the prediction and control results. This method can improve the autonomous decision-making ability of spacecraft in orbit.

Description

Technical Field

[0001] The present invention belongs to the field of spacecraft control technology and near-field positioning technology, and in particular relates to a method for joint attitude and orbit control in a dual-spacecraft pursuit-escape game. Background Art

[0002] With the development of aerospace technology, the exploration and development of the space environment has become the focus of competition among countries. Space confrontation has become increasingly fierce, which has posed new challenges to the intelligent technology of spacecraft. How spacecraft can make autonomous decisions and plan maneuvering trajectories in the face of sudden threats is a key technology to ensure the safe operation of spacecraft in orbit, and is of great significance to improving my country's ability to maintain space security. The study of the spacecraft pursuit and escape game problem can provide important technical support for the autonomous decision-making of spacecraft in the face of sudden threats, and improve the spacecraft's ability to respond in complex space environments.

[0003] Spacecraft pursuit and escape game generally refers to orbital pursuit and escape game, and the most typical example is the dual-spacecraft pursuit and escape game, that is, one spacecraft tries to track another spacecraft in orbit to achieve rendezvous or other purposes, while the other spacecraft tries to escape and avoid being tracked. In the close approach at the end of the game process, the pursuit spacecraft often needs to adjust its attitude so that the corresponding payload is aimed at the target spacecraft to complete a specific task.

[0004] In actual space pursuit scenarios, the two parties are in close-range game, and the pursuit satellite often needs to take certain capture measures to capture the escaped satellite. At this time, the pursuit satellite needs to dynamically adjust its own attitude so that its corresponding payload is aligned with the escaped satellite. Traditional spacecraft pursuit problems mainly focus on the design of orbital maneuvering strategies, lacking research on attitude and orbit joint game problems, and because it is difficult for both parties to obtain key information about the other party in the game, the game process between the two parties is actually an incomplete information game. For the above scenarios, the current research on spacecraft pursuit strategies still lacks effective solutions.

[0005] How to achieve orbit and attitude control during the game process is a key technology for spacecraft to complete complex space missions, cope with complex space environments, and ensure safe operation in orbit.

[0006] Traditional spacecraft game methods mainly focus on the design of orbital maneuvering strategies, but lack the design of attitude maneuvering strategies. In addition, since it is difficult for both parties to obtain each other's key information in real games, the game process between the two parties is actually an incomplete information game, and there is still a lack of effective solutions. Summary of the invention

[0007] In view of the problems existing in the prior art, the purpose of the present invention is to propose a joint attitude and orbit control method for the dual-spacecraft pursuit and escape game for the incomplete information spacecraft attitude and orbit joint game problem that is closer to the actual scene, which improves the autonomous decision-making ability of the spacecraft in orbit and makes the method more universal.

[0008] To achieve the above object, the present invention provides a method for joint attitude and orbit control in a dual-spacecraft pursuit-escape game, wherein the dual spacecraft include a pursuit spacecraft and a target spacecraft, and the method includes the following steps:

[0009] S1. Construct the objective function of tracking the spacecraft based on the orbital dynamics equation;

[0010] S2. For the weight matrix of the objective function in step 1, the unscented particle filter method is used to estimate the Q and R matrices in the objective function, and the Nash equilibrium solution of the pursuit and escape parties under incomplete information is solved;

[0011] S3. Based on the weight matrix obtained by unscented particle filtering to estimate the objective function, the Nash equilibrium strategy of the dual spacecraft pursuit problem under incomplete information is calculated;

[0012] S4. When the distance between the pursuer and the fugitive is less than or equal to the set threshold, the tracking spacecraft begins to adjust its attitude, uses model predictive control to predict and control the target spacecraft, and adjusts the attitude of the tracking spacecraft based on the prediction and control results.

[0013] Further, in step S1, the objective function is as follows:

[0014]

[0015] Among them, Q and S represent the weight matrix of the S system, and are the weight matrices of the two parties in pursuit and escape, represents the relative state vector of the pursuer P and the escaper E, T represents the time parameter of the pursuit-escape game process, represents the transposed vector of the relative state at the end time, Indicates the relative state at the end moment, represents the transposed vector of the relative state during the game, Indicates the relative state during the game, represents the transpose of the thrust input to the tracking spacecraft, represents the thrust input of the tracking spacecraft, represents the transpose of the thrust input of the escape spacecraft, represents the thrust input of the escape spacecraft, Indicates the time of the game.

[0016] Furthermore, in step S2, the steps for solving the Nash equilibrium solution of the pursuit and escape parties under incomplete information are:

[0017] S2.1, considering the process noise and measurement noise in the actual observation model, the system model is constructed as follows:

[0018]

[0019] in, and They represent the process noise and measurement noise of the system respectively, represents the state equation of the system, represents the observation equation of the system, Represents the control input; the principle of particle filtering is based on the hidden Markov model, and its state satisfies the assumption of Markov process;

[0020] Step S2.2, the weight matrix in the objective function is estimated by using the unscented particle filter algorithm. After initializing the distribution of particles, the unscented Kalman filter algorithm is used to predict and update the particles so that the probability density distribution of the particles is close to the true density.

[0021] Further, in step S2, based on the errors in the observed position and velocity information, the unscented particle filter method is used to estimate the actual information, wherein the observed estimated state information of the escape spacecraft is expanded into a 9-dimensional vector to increase the description of the implicit weight matrix, and the actual weight matrix information of the escape spacecraft is approximated by the estimation of the state quantity. Further, in step S3, the Nash equilibrium strategy for the dual spacecraft pursuit problem under incomplete information is calculated as follows:

[0022]

[0023] in, and is the weight matrix estimated by the unscented particle filter algorithm.

[0024] Furthermore, in step S4, in the model predictive control, it is necessary to determine the objective function and constraints of the posture control, so as to optimize the objective function. The specific optimization model is as follows:

[0025]

[0026] in, Represents the state output at time t+k, represents the state output at time t, represents the lower bound of the output, represents the upper bound of the output, represents the lower bound of the input, represents the upper bound of the input, the objective function Indicates the status of the system To keep the expected state as close as possible in the next N time steps , and is subject to the control input in the above formula and state quantity The upper and lower limit constraints are set to obtain the mathematical model of the posture control optimization problem based on model predictive control, and the action of the controller input is obtained by solving the optimization model.

[0027] Beneficial effects:

[0028] (1) This method studies the spacecraft orbit pursuit problem based on incomplete information, which is closer to actual application scenarios.

[0029] (2) This method takes into account both the orbital maneuvering and attitude control of the spacecraft, thus expanding the application prospects of the spacecraft pursuit problem. It is suitable for the active approach and capture mission of the spacecraft and has more practical application significance.

[0030] (3) Based on the advantages of the model predictive control method, this method converts the external constraints of the spacecraft into mathematical descriptions, which can adapt to more complex dynamic models. In addition, the feedback calculation of the next few time steps is considered in the control, which enables this method to compensate for the uncertainty of the estimated state over time to a certain extent, making the attitude control of the spacecraft more robust.

[0031] (4) This method can make the designed strategy have the characteristics of guided control through the design of the objective function, and perform rolling optimization calculations at each sampling moment to solve the optimal control sequence in the future. It can adjust the objective function and constraints in a timely manner, making the control strategy solution more accurate and the control effect closer to the global optimum. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The figure is a flow chart of the attitude-orbit joint control method for the dual-spacecraft pursuit-escape game according to the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0035] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0036] The following combination Figure 1 The specific embodiments of the present invention are described in detail. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0037] Spacecraft are constrained by orbital dynamics in space. Spacecraft game mainly focuses on the orbital maneuvering process. For tracking spacecraft, attitude control will be performed to adjust the load direction at the end moment close to the target spacecraft, which has the characteristics of attitude-orbit joint game. Therefore, the present invention combines the characteristics of the pursuit-escape game problem, does not adopt the integrated relative dynamic model of attitude-orbit coupling, but independently studies orbital control and attitude control based on orbital equations and attitude dynamics equations, thereby simplifying the dynamic model of the problem. When the distance between the two spacecraft is close, the present invention simplifies the complex dynamic model of the spacecraft into a classic general linear CW equation. Under the constraints of the dynamic model, it better takes into account the effects of the spacecraft's on-orbit calculation speed and control accuracy.

[0038] The present invention controls the orbit and attitude independently based on the orbit dynamics equation and attitude dynamics equation respectively. When the tracking spacecraft is relatively close to the target spacecraft, the dynamic model of the tracking spacecraft is simplified to a linear CW equation, that is:

[0039]

[0040] In the formula, represents the average angular velocity of the two spacecraft, where is the Earth's gravitational constant, represents the orbital radius of the approximately circular orbit, is the relative active control force of the two spacecraft, i.e., the input acceleration. Represents the thrust acceleration of the tracking spacecraft and its components in the x, y, and z directions. Similarly, Represents the thrust acceleration of the escaping spacecraft and its components in the x, y, and z directions.

[0041] The spacecraft attitude is described by modified Rodriguez parameters, and the corresponding attitude dynamics models of both parties are:

[0042]

[0043] In the formula, To correct the Logarithm parameters, describe the attitude of the satellite system relative to the geocentric inertial system. is a diagonal positive definite matrix, representing the moment of inertia matrix of the spacecraft; is the angular velocity of the spacecraft body relative to the inertial system; and Represent the control input of the spacecraft and the external environment disturbance respectively. Among them, .

[0044] Based on the above analysis, the attitude and orbit joint control method of the dual-spacecraft pursuit-escape game according to the present invention is as follows:

[0045] Phase 1, orbital maneuvering phase, includes the following steps S1-S3:

[0046] Step S1, construct the objective function of tracking the spacecraft based on the orbital dynamics equation:

[0047]

[0048] Among them, Q and S represent the weight matrices of the tracking spacecraft regarding fuel and distance constraints, respectively. and They are the weight matrices of the pursuer and the fugitive respectively. By constructing this objective function, it is possible to quantitatively evaluate the performance of both parties. This information needs to be further estimated to provide prior conditions for the subsequent optimization of the following performance of the tracking spacecraft. represents the relative state vector of the pursuer P and the escaper E, T represents the time parameter of the pursuit-escape game process, represents the transposed vector of the relative state at the end time, Indicates the relative state at the end moment, represents the transposed vector of the relative state during the game, Indicates the relative state during the game, represents the transpose of the thrust input to the tracking spacecraft, represents the thrust input of the tracking spacecraft, represents the transpose of the thrust input of the escape spacecraft, represents the thrust input of the escape spacecraft, Represents the time of the game. The objective function is used to calculate the optimal strategy of both parties under given conditions. Its specific formulation is based on the unified setting of comprehensive consideration of constraints and optimization objectives. For an objective function, it is an optimization problem. The purpose is to solve the maneuver control strategy that minimizes the objective function given the input observation state. The weight matrices such as Q and S are used to measure the impact of different states on the objective function. They are usually set in the form of diagonal matrices, and the values ​​on the diagonal matrices are unified values, that is, the two matrices Q and S are the two parameter values ​​set. However, in practice, the weight settings of the escape party to measure the fuel and distance constraints cannot be known, so it is necessary to estimate the values ​​of these two parameters. However, since the observation sensor can only observe the speed and position of the escape spacecraft, and indirectly calculate the maneuver acceleration information, it is impossible to directly observe the weight matrix information. Therefore, it is necessary to continuously calculate the opponent's maneuver strategy based on the changes in its speed and position, and based on the assumed escape party objective function information, by guessing the iterative weight factor information, and compare it with the actual maneuver strategy. In the estimation and iteration process, the solved maneuver strategy is close to the opponent's true value, thereby estimating the specific form of the escape party's objective function. In this process, the weight matrix is ​​associated with the position and velocity information obtained by the objective function and the observations of both parties.

[0049] Step S2, since the objective functions of both parties in the actual scenario are unknown, it is assumed that the objective functions of both parties are in the same form, but the weight matrices are different. The tracking spacecraft uses the unscented particle filter method to estimate the Q and R matrices in the objective function of the escaping spacecraft. The S matrix is ​​omitted here because as time goes by, the objective function contains the Q and R matrices in the form of integral terms, which become larger and larger. In comparison, the quadratic term of the S matrix can be ignored. Based on the estimated information, the Nash equilibrium solution of the chasing and escaping parties under incomplete information is solved, and then the optimal maneuvering strategy of the tracking spacecraft against the equilibrium strategy of the escaping spacecraft is solved.

[0050] The solution steps are:

[0051] Step S2.1, considering the process noise and measurement noise in the actual observation model, the system model is constructed as follows:

[0052]

[0053] in, and They represent the process noise and measurement noise of the system respectively, represents the state equation of the system, represents the observation equation of the system, Represents the control input. Under the linear dynamic equation model, the relative dynamic model can be expressed in the form of a matrix equation, where A is the state equation, and the specific form is shown below. X is the state quantity, y is the observed quantity, and the observation equation is a nonlinear equation, which is the equation for each sensor to observe the speed and position information. The specific form is determined by the sensor. In the present invention, it is considered that these observation data are known, but there may be noise and observation errors. The principle of particle filtering is based on the hidden Markov model, and its state satisfies the assumption of the Markov process.

[0054]

[0055] Step S2.2, the weight matrix in the objective function is estimated by the unscented particle filter algorithm (only the weight matrix of the escape spacecraft is estimated). After initializing the distribution of particles, the unscented Kalman filter algorithm is used to predict and update the particles so that the probability density distribution of the particles is closer to the true density. The time update equation and measurement update equation are:

[0056]

[0057]

[0058] In the formula, represents the state design in the observation estimation, which is a nine-dimensional vector, Respectively represent the weight factors in the corresponding weight matrices Q and R, represents the state of the ath particle at the k-1th moment in the i-th cycle, is the total number of particles, represents the mean, are the parameters related to the traceless transformation settings, represents the covariance. represents the state equation of the system, represents the observation equation of the system (same as the observation equation above). represents the weight value of the i-th particle, are the observed values ​​(the observed values ​​refer to the observed position and velocity of the escaping spacecraft). is the updated state value, which contains the weight factor information after the observation value processing and comparison. Since it is included in the speed, position and other information, it is an implicit relationship from which the updated weight factor is obtained. , that is, the estimated weight matrix is ​​finally obtained. After the above calculation, we get and The weight matrix information of .

[0059] Step S3, based on the weight matrix obtained by estimating the objective function through unscented particle filtering, calculate the Nash equilibrium strategy for the dual spacecraft pursuit problem under incomplete information.

[0060]

[0061] in, and is the weight matrix estimated by the unscented particle filter algorithm. and are the estimated control strategy information of the tracking spacecraft and the escaping spacecraft, is the corresponding system matrix, To solve the Riccati equation, is the relative state between the two spacecraft.

[0062] Phase 2, the terminal moment posture adjustment phase, includes step S4:

[0063] Step S4: When the distance between the pursuer and the escaping spacecraft is close (e.g., less than 500m), the tracking spacecraft begins to adjust its attitude and uses model predictive control to predict and control the escaping spacecraft (i.e., the target spacecraft). In model predictive control, it is necessary to determine the objective function and constraints of attitude control in order to optimize the objective function. The specific optimization model is as follows:

[0064]

[0065] in, Represents the state output at time t+k, represents the state output at time t, represents the lower bound of the output, represents the upper bound of the output, represents the lower bound of the input, represents the upper bound of the input, the objective function Indicates the status of the system To keep the expected state as close as possible in the next N time steps , and is subject to the control input in the above formula and state quantity The upper and lower limit constraints are obtained from the above, and the mathematical model of the attitude control optimization problem based on model predictive control is obtained. The action of the controller input is obtained by solving the optimization model. For the desired attitude approach state, the attitude adjustment strategy of the tracking spacecraft itself is calculated and solved, so that the tracking spacecraft approaches the escaping spacecraft with the desired attitude.

[0066] In a specific example, in a relative coordinate system, the escape spacecraft is at the origin of the coordinate system, and the initial state information is [0,0,0,0,0,0], which respectively represent the position and velocity of the spacecraft in the relative coordinate system. The first three elements represent the components of the position vector in the x, y, and z directions, and the last three elements represent the components of the velocity vector in the x, y, and z directions. The initial state information of the tracking spacecraft is set to [15,3,7,0,0,0.1]. At the initial moment, the escape spacecraft and the tracking spacecraft construct the target payment function in the game based on their own state information. That is, step S1.

[0067] Afterwards, since the tracking spacecraft sets the objective function for the escaping spacecraft based on its own information, its specific parameters are inaccurate, so it is necessary to use the tracking spacecraft to estimate the observation information of the escaping spacecraft. In step S2, due to the existence of uncertain factors such as process error and measurement error, there are errors based on the observed position and velocity information, so the unscented particle filter method is used to estimate the actual information, in which the observed estimated state information of the escaping spacecraft is expanded into a 9-dimensional vector. , the description of implicit weight matrix is ​​added, and the actual weight matrix information of the escaping spacecraft is approximated by estimating the state quantity.

[0068] After the tracking spacecraft obtains the estimated weight information, step S3 is performed to update the objective function of the escaping spacecraft in step S1 according to the actual weight information, thereby calculating the optimal control strategy for the tracking spacecraft based on two different objective functions, the tracking spacecraft's own payment function and the actual payment function estimated by the escaping spacecraft, so that the tracking spacecraft can approach the escaping spacecraft faster.

[0069] When the distance between the pursuit spacecraft and the escaping spacecraft approaches within 500 meters, the attitude pointing direction of the pursuit spacecraft is set according to the observed attitude of the escaping spacecraft (since the distance to the escaping spacecraft is relatively close at this stage and the observation error is small, the observation value is directly used for calculation in the second stage). Through step S4, the degree of proximity between the current spacecraft attitude pointing and the expected pointing is optimized, and finally the attitude thrust acceleration for the pursuit spacecraft is obtained, thereby realizing the overall maneuver and attitude joint control pursuit method.

[0070] The technical effects of the present invention are as follows:

[0071] (1) This method studies the spacecraft orbit pursuit problem based on incomplete information, which is closer to actual application scenarios.

[0072] (2) This method takes into account both the orbital maneuvering and attitude control of the spacecraft, thus expanding the application prospects of the spacecraft pursuit problem. It is suitable for the active approach and capture mission of the spacecraft and has more practical application significance.

[0073] (3) Based on the advantages of the model predictive control method, this method converts the external constraints of the spacecraft into mathematical descriptions, which can adapt to more complex dynamic models. In addition, the feedback calculation of the next few time steps is considered in the control, which enables this method to compensate for the uncertainty of the estimated state over time to a certain extent, making the attitude control of the spacecraft more robust.

[0074] (4) This method can make the designed strategy have the characteristics of guided control through the design of the objective function, and perform rolling optimization calculations at each sampling moment to solve the optimal control sequence in the future. It can adjust the objective function and constraints in a timely manner, making the control strategy solution more accurate and the control effect closer to the global optimum.

[0075] Any process or method description in the flowchart of the present invention or described in other ways herein can be understood as a module, segment or part of a code including one or more executable instructions for implementing the steps of a specific logical function or process, which can be implemented in any computer-readable medium for use by an instruction execution system, device or equipment, and the computer-readable medium can be any medium containing storage, communication, propagation or transmission programs for use by execution systems, devices or equipment, including read-only memories, magnetic disks or optical disks, etc.

[0076] In the description of this specification, the description with reference to the terms "embodiment", "example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. In addition, those skilled in the art can combine or combine different embodiments or examples described in this specification and the features therein without causing any contradiction.

[0077] Although the above contents have shown and described the embodiments of the present invention, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in the field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A joint attitude and orbit control method for dual-spacecraft pursuit-escape game, characterized in that: The dual spacecraft include a tracking spacecraft and a target spacecraft, and the method includes the following steps: S1. Construct the objective function of tracking the spacecraft based on the orbital dynamics equation; S2. For the weight matrix of the objective function in step S1, the unscented particle filter method is used to estimate the Q and R matrices in the objective function, and the Nash equilibrium solution of the pursuit and escape parties under incomplete information is solved; S3. Based on the weight matrix obtained by unscented particle filtering to estimate the objective function, the Nash equilibrium strategy of the dual spacecraft pursuit problem under incomplete information is calculated; S4. When the distance between the pursuer and the fugitive is less than or equal to the set threshold, the tracking spacecraft begins to adjust its attitude, uses model predictive control to predict and control the target spacecraft, and adjusts the attitude of the tracking spacecraft based on the prediction and control results; In step S2, the steps for solving the Nash equilibrium solution of the two parties in pursuit and escape under incomplete information are: S2.1, consider the process noise and measurement noise in the actual observation model and construct the system model; S2.2, the weight matrix in the objective function is estimated by the unscented particle filter algorithm. After initializing the distribution of particles, the unscented Kalman filter algorithm is used to predict and update the particles so that the probability density distribution of particles is close to the true density; Due to process errors and measurement errors, there are errors in the position and velocity information of the escape spacecraft obtained based on observations. The actual position and velocity of the escape spacecraft are estimated using an unscented particle filter method based on the errors. In the process of estimating the actual position and velocity of the escaping spacecraft using the unscented particle filter method, the observed estimated state information of the escaping spacecraft is expanded to a 9-dimensional vector, and the description of the implicit weight matrix is ​​added. The actual weight matrix information of the escaping spacecraft is approximated by estimating the state quantity.

2. The attitude and orbit joint control method for the dual-spacecraft pursuit-escape game according to claim 1 is characterized in that: In step S1, the objective function is as follows: Among them, Q and S represent the weight matrices of the tracking spacecraft regarding fuel and distance constraints, respectively. and are the weight matrices of the two parties in pursuit and escape, represents the relative state vector of the pursuer P and the escaper E, T represents the time parameter of the pursuit-escape game process, represents the transposed vector of the relative state at the end time, Indicates the relative state at the end moment, represents the transposed vector of the relative state during the game, Indicates the relative state during the game, represents the transpose of the thrust input to the tracking spacecraft, represents the thrust input of the tracking spacecraft, represents the transpose of the thrust input of the escape spacecraft, represents the thrust input of the escape spacecraft, Indicates the time of the game.

3. The attitude and orbit joint control method for the dual-spacecraft pursuit-escape game according to claim 1 is characterized in that: In the relative coordinate system, the position and velocity of the escaping spacecraft in the relative coordinate system are observed and acquired, and the position and velocity of the tracking spacecraft in the relative coordinate system are collected. At the initial moment, the escaping spacecraft and the tracking spacecraft construct the objective function in the game based on their own state information.

4. The attitude and orbit joint control method for the dual-spacecraft pursuit-escape game according to claim 1 is characterized in that: In step S2.1, the system model is as follows: in, and They represent the process noise and measurement noise of the system respectively, represents the state equation of the system, represents the observation equation of the system, Represents the control input; the principle of particle filtering is based on the hidden Markov model, and its state satisfies the assumption of Markov process.

5. The attitude and orbit joint control method for the dual-spacecraft pursuit-escape game according to claim 1 is characterized in that: In step S2, based on the errors in the observed position and velocity information, the unscented particle filtering method is used to estimate the actual information, wherein the observed estimated state information of the escaping spacecraft is expanded to a 9-dimensional vector to increase the description of the implicit weight matrix, and the actual weight matrix information of the escaping spacecraft is approximated by the estimation of the state quantity.

6. The attitude and orbit joint control method for the dual-spacecraft pursuit-escape game according to claim 1 is characterized in that: In step S3, the Nash equilibrium strategy for the dual spacecraft pursuit problem under incomplete information is calculated as follows: in, and is the weight matrix estimated by the unscented particle filter algorithm, and are the estimated control strategy information of the tracking spacecraft and the escaping spacecraft, is the corresponding system matrix, To solve the Riccati equation, is the relative state between the two spacecraft.

7. The attitude and orbit joint control method for the dual-spacecraft pursuit-escape game according to claim 1 is characterized in that: In step S4 model predictive control, it is necessary to determine the objective function and constraints of the attitude control, so as to optimize the objective function. The optimization model is: ;in, Represents the state output at time t+k, represents the state output at time t, represents the lower bound of the output, represents the upper bound of the output, represents the lower bound of the input, represents the upper bound of the input, the objective function Indicates the status of the system To keep the expected state as close as possible in the next N time steps , and is subject to the control input in the above formula and state quantity The upper and lower limit constraints are set to obtain the mathematical model of the posture control optimization problem based on model predictive control, and the action of the controller input is obtained by solving the optimization model.

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