A method for optimizing parameters of trajectory pursuit strategy under incomplete information

Abstract

The present invention discloses a method for optimizing the design of orbital pursuit strategy parameters under incomplete information, which specifically relates to the field of aerospace technology. A coordinate system is established at a virtual reference point to describe the motion state of a target spacecraft and a pursuing spacecraft. Secondly, a control weight coefficient of a non-cooperative target spacecraft is set to characterize its escape strategy. Next, a Kalman filter method is introduced to estimate the escape strategy of a non-cooperative target spacecraft. On this basis, the trajectory of the target spacecraft after a period of time is derived. Finally, a pursuit strategy for the pursuing spacecraft is designed based on a parameter optimization method, and a single time domain terminal indicator is used to judge the pros and cons of the pursuit strategy in the current time domain. The fuel and terminal mission requirements are comprehensively considered to achieve rapid approach to non-cooperative targets, thereby overcoming the shortcomings of poor scenario applicability of traditional empirical strategy parameter selection methods.

Description

Technical Field

[0001] The invention relates to the field of aerospace technology, and in particular to a method for optimizing design of trajectory pursuit strategy parameters under incomplete information. Background Art

[0002] The pursuit and escape problem between two non-cooperative spacecraft is a basic problem in space games. Unlike the space debris rendezvous problem, non-cooperative targets have maneuverability and their maneuvering strategies are unpredictable. If one spacecraft is required to complete the approach mission to the other spacecraft in the actual mission, then this scenario constitutes a type of incomplete information orbital pursuit problem. In the incomplete information orbital pursuit problem, the two parties have limited information and cannot reach a consistent cost function with each other. The optimal pursuit and escape control method based on the traditional zero-sum game assumption cannot guarantee the optimality of the result. Therefore, how to guess the maneuvering strategy of the non-cooperative target based on the existing information and design an effective pursuit strategy based on this is crucial to improving the success rate of the orbital pursuit mission.

[0003] The non-cooperative target pursuit problem under incomplete information conditions has difficulties such as unknown opponent strategies and difficult modeling description. The existing method of designing the control of the two parties of pursuit based on differential game research can ensure that the optimal control of each party is obtained under the premise that both parties have a unified cost function model. However, this method cannot guarantee that the optimal control of the party can be obtained when the cost function modeling of the two parties is inconsistent. In particular, for the actual space pursuit game problem, it is impossible to obtain the specific control strategy of the non-cooperative target. Therefore, the existing research is not conducive to the application in the actual pursuit game process.

[0004] The theory of differential game pursuit and escape game was first seen in the literature Isaacs, R. Differential Games: A Mathematical Theory with Applications to Warfare and Pursuit, Control and Optimization, 2nd ed.; Courier Corporation: New York, NY , USA, 1999; pp. 25–44. It was originally applied to linear dynamic models and assumed that the game participants knew each other's cost functions. Therefore, the control goal of each game participant was to minimize its own cost function while maximizing the game cost of the non-cooperative goal. In the process of designing each cost function, experience or trial and error is often used to add weights to various indicators in the cost function, which is somewhat subjective. Summary of the invention

[0005] To this end, the present invention provides a method for optimizing the design of orbital pursuit strategy parameters under incomplete information. Compared with the traditional method, the Kalman filter method is introduced to realize the estimation of the escape strategy of the non-cooperative target spacecraft. At the same time, a pursuit strategy based on parameter optimization is designed, and a single time domain terminal indicator is used to judge the pros and cons of the pursuit strategy in the current time domain, thereby reducing the influence of the selection of artificial strategy parameters and having strong applicability. In the process of estimating the non-cooperative target strategy, a new extended system state is established, which contains parameters that characterize the target escape strategy, thereby realizing a rapid estimation of the non-cooperative target strategy. In the process of designing the pursuit spacecraft strategy, the pursuit strategy is designed by establishing a parameter optimization control method, comprehensively considering the fuel and terminal mission requirements, and realizing a rapid approach to the non-cooperative target. This is to solve the problems raised in the background technology.

[0006] The present invention provides the following technical solution: a method for optimizing the parameters of a trajectory pursuit strategy under incomplete information, comprising the following steps:

[0007] Step S1, taking the initial position of the non-cooperative target spacecraft as the origin of the orbital pursuit scene, setting a relative coordinate system, and describing the states of the pursuit spacecraft and the target spacecraft in this relative system;

[0008] Step S2, setting a non-cooperative target spacecraft control weight coefficient to characterize the target spacecraft escape strategy;

[0009] Step S3, taking the states of the target spacecraft and the pursuit spacecraft as observation quantities, and the augmented vector with the target control weight coefficient as the state quantity, using a Kalman filter, and obtaining a non-cooperative target escape strategy through observation quantity filtering and updating;

[0010] Step S4, taking the non-cooperative target escape strategy obtained by filtering at the current moment as a benchmark, assuming that the escape strategy of the target spacecraft remains unchanged in the next time domain, and obtaining the control output of the target spacecraft in the next time domain;

[0011] Step S5, optimizing the strategy parameters of the pursuit spacecraft to maximize the set performance index at the end of the next time domain, outputting the pursuit strategy parameters after the index converges, and obtaining the optimal control in the next time domain;

[0012] Repeat steps S3 to S5 until the task is completed.

[0013] Preferably, the step S1 comprises the following steps:

[0014] S1.1. The target spacecraft position at the initial moment is taken as the virtual reference center point. This reference center orbits around the earth under the action of the earth's gravity. The x-axis is the opposite direction of the position vector of the virtual reference center point. The z-axis direction unit vector is perpendicular to the orbital plane of the virtual reference center point. The y-axis is given by the right-hand rule.

[0015] S1.2. During the pursuit process, the distances between the pursuit spacecraft and the target spacecraft and the virtual reference center point are much greater than the distance between the virtual reference center point and the center of the earth. According to the Clohessy-Wiltshire equation, the motion equations of the two spacecraft in the above reference system are expressed as:

[0016] ;

[0017] in, is the system matrix, which represents the interaction relationship between the components of the system state vector. is the input matrix, which represents the relationship between the control input and the system state vector; represents the state vector of the pursuit spacecraft / target spacecraft, is the position and velocity difference between the spacecraft and the virtual reference center point, where is the position difference component, is the velocity difference component, represents the control vector of the pursuit spacecraft / target spacecraft, ; t represents time, As a subscript and When represent the pursuit spacecraft and the target spacecraft respectively; and The value is related to the orbit state of the virtual center point at the moment of description, and its calculation method is as follows:

[0018] ;

[0019] in, To describe the orbital true anomaly of the virtual reference center point at the moment, To describe the distance from the center of the earth to the virtual reference center point at that moment, is the gravitational constant of the central celestial body. If the virtual reference center point is taken as a circular orbit, then Does not change over time, Is a fixed value.

[0020] Preferably, the step S2 comprises the following steps:

[0021] S2.1. Definition of the pursuit problem: The optimal target state of both parties is expressed as the difference between the two spacecraft: ;

[0022] S2.2, set the optimal control payment function of non-cooperative target escape as:

[0023] ;

[0024] in, To characterize the control weight coefficients of the target spacecraft escape strategy, they are all diagonal matrices, and T is the transpose;

[0025] S2.3. According to the optimal control principle, the optimal escape control and target state of the target spacecraft under the optimal control payment function are obtained. Relationship between:

[0026] ;

[0027] in, is the Riccati matrix. According to the Riccati equation Find out.

[0028] Preferably, the step S3 comprises the following steps:

[0029] S3.1. The strategy parameters of the target spacecraft are represented as the state quantity of the filtering system. The specific description form is as follows:

[0030] ;

[0031] in, , Related to the target spacecraft escape strategy, which is related to The relationship is:

[0032] ;

[0033] in, is the identity matrix, and the number of rows and columns of the matrix is ​​shown in the following subscripts;

[0034] S3.2. The difference between the states of the two spacecraft Considered as an observation, the observation and augmented state vector are established The relationship between:

[0035] ;

[0036] in, Indicated by Determined Observables and augmentation relation, is the process noise, is the system noise, and the subscript is the number of rows and columns of the matrix;

[0037] S3.3, use Kalman filtering method to set the initial parameter guess value , perform filtering parameter estimation;

[0038] S3.4, in the filtering process, firstly according to the observed Solve for the last moment , and then substitute The single-step .

[0039] Preferably, the step S4 comprises the following steps:

[0040] S4.1. Strategy parameters obtained based on filtering , assuming that the next time domain The escape parameters of the target spacecraft do not change, and the motion of the target spacecraft in a time domain is predicted. The specific calculation method is as follows:

[0041] ;

[0042] in, for The derivative of the target spacecraft state in the time domain.

[0043] Preferably, the step S5 comprises the following steps:

[0044] S5.1. Based on the predicted motion of the target spacecraft, integrate to obtain the next time domain Trajectory of internal target spacecraft;

[0045] S5.2. Set the optimal control payoff function of the pursuit spacecraft to:

[0046] ;

[0047] According to the optimal control principle, the optimal control of the pursuit spacecraft under the strategy parameters is obtained as:

[0048] ;

[0049] S5.3. Set the pursuit index fitness function that the pursuit spacecraft expects to achieve in the next time domain. Its specific expression is as follows:

[0050] ;

[0051] ;

[0052] in, is the control consumption term, is the integral value of the spacecraft control quantity in a single time domain, is the relative distance between the two spacecraft at the end of the next time domain, is the relative speed of the two spacecraft at the end of the next time domain, are the control cost of the pursuit spacecraft in the next time domain, the relative distance between the two spacecraft terminals, and the weighted terms of the relative speed between the two spacecraft terminals, respectively. is the strategy parameter for the pursuit spacecraft, which can be expressed as:

[0053] ;

[0054] S5.4. Setting weight coefficients , using genetic algorithms to optimize the independent variables , so that the fitness function set in the next time domain Minimum, output at this time ;

[0055] S5.5. Optimize the pursuit strategy parameters Substitution , the pursuit spacecraft control in the next time domain is obtained and output, the pursuit spacecraft control in a single time domain is solved, the pursuit spacecraft and the target spacecraft trajectory are output, and steps S3 to S5 are continued until the pursuit success judgment condition is met and the process jumps out.

[0056] The present invention has the following advantages:

[0057] The present invention proposes a parameter optimization method to generate orbit pursuit control under incomplete information. Firstly, in the process of guessing the non-cooperative target strategy, an augmented state quantity is established to complete the mapping from the orbit state quantity to the non-cooperative target strategy parameters. On this basis, the Kalman filter method is used to realize the estimation of the non-cooperative target escape strategy. In the process of designing the pursuit strategy of the pursuit spacecraft, the optimal parameters of the current optimal control of the pursuit spacecraft are obtained by designing optimization indicators. At the same time, the design of the optimization indicators enables the pursuit spacecraft to meet with the target spacecraft at different relative speeds to meet different mission requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of a method for optimizing the parameters of a trajectory pursuit strategy under incomplete information provided by the present invention;

[0059] Figure 2 Schematic diagram of the control of the incomplete information trajectory pursuit problem based on parameter optimization.

[0060] Figure 3 It is a control amplitude variation curve diagram of the method used in the present invention and the traditional method in the test case.

[0061] Figure 4 It is a curve diagram of total speed increment change between the method used by the present invention and the traditional method in the test case.

[0062] Figure 5 It is a statistical diagram of the total speed increment ratio between the method used in the present invention and the traditional method in Monte Carlo simulation.

[0063] Figure 6It is a box plot of the statistical results of the total speed increment ratio between the method used in the present invention and the traditional method in Monte Carlo simulation. DETAILED DESCRIPTION

[0064] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are 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.

[0065] like Figure 1 As shown, this embodiment provides a method for optimizing parameters of track pursuit strategy under incomplete information, comprising the following steps:

[0066] Step S1, taking the initial position of the non-cooperative target spacecraft as the origin of the orbital pursuit scene, setting a relative coordinate system, and describing the states of the pursuit spacecraft and the target spacecraft in this relative system;

[0067] S1.1. The target spacecraft position at the initial moment is taken as the virtual reference center point. This reference center orbits around the earth under the action of the earth's gravity. The x-axis is the opposite direction of the position vector of the virtual reference center point. The z-axis direction unit vector is perpendicular to the orbital plane of the virtual reference center point. The y-axis is given by the right-hand rule.

[0068] S1.2. During the pursuit process, the distances between the pursuit spacecraft and the target spacecraft and the virtual reference center point are much greater than the distance between the virtual reference center point and the center of the earth. According to the Clohessy-Wiltshire equation, the motion equations of the two spacecraft in the above reference system are expressed as:

[0069] ;

[0070] in, is the system matrix, which represents the interaction relationship between the components of the system state vector. is the input matrix, which represents the relationship between the control input and the system state vector; represents the state vector of the pursuit spacecraft / target spacecraft, is the position and velocity difference between the spacecraft and the virtual reference center point, where is the position difference component, is the velocity difference component, represents the control vector of the pursuit spacecraft / target spacecraft, ; t represents time, As a subscript and When represent the pursuit spacecraft and the target spacecraft respectively; and The value is related to the orbit state of the virtual center point at the moment of description, and its calculation method is as follows:

[0071] ;

[0072] in, To describe the orbital true anomaly of the virtual reference center point at the moment, To describe the distance from the center of the earth to the virtual reference center point at that moment, is the gravitational constant of the central celestial body. If the virtual reference center point is taken as a circular orbit, then Does not change over time, Is a fixed value.

[0073] Step S2, setting a non-cooperative target spacecraft control weight coefficient to characterize the target spacecraft escape strategy;

[0074] S2.1. Definition of the pursuit problem: The optimal target state of both parties is expressed as the difference between the two spacecraft: ;

[0075] S2.2, set the optimal control payment function of non-cooperative target escape as:

[0076] ;

[0077] in, To characterize the control weight coefficients of the target spacecraft escape strategy, they are all diagonal matrices, and T is the transpose;

[0078] S2.3. According to the optimal control principle, the optimal escape control and target state of the target spacecraft under the optimal control payment function are obtained. Relationship between:

[0079] ;

[0080] in, is the Riccati matrix. According to the Riccati equation Find out.

[0081] Step S3, taking the states of the target spacecraft and the pursuit spacecraft as observation quantities, and the augmented vector with the target control weight coefficient as the state quantity, using a Kalman filter, and obtaining a non-cooperative target escape strategy through observation quantity filtering and updating;

[0082] S3.1. The strategy parameters of the target spacecraft are represented as the state quantity of the filtering system. The specific description form is as follows:

[0083] ;

[0084] in, , Related to the target spacecraft escape strategy, which is related to The relationship is:

[0085] ;

[0086] in, is the identity matrix, and the number of rows and columns of the matrix is ​​shown in the following subscripts;

[0087] S3.2. The difference between the states of the two spacecraft Considered as an observation, the observation and augmented state vector are established The relationship between:

[0088] ;

[0089] in, Indicated by Determined Observables and augmentation relation, is the process noise, is the system noise, and the subscript is the number of rows and columns of the matrix;

[0090] S3.3, use Kalman filtering method to set the initial parameter guess value , perform filtering parameter estimation;

[0091] S3.4, in the filtering process, firstly according to the observed Solve for the last moment , and then substitute The single-step .

[0092] Step S4: Figure 2 As shown, taking the non-cooperative target escape strategy obtained by filtering at the current moment as the benchmark, assuming that the escape strategy of the target spacecraft remains unchanged in the next time domain, the control output of the target spacecraft in the next time domain is obtained;

[0093] S4.1. Strategy parameters obtained based on filtering , assuming that the next time domain The escape parameters of the target spacecraft do not change, and the motion of the target spacecraft in a time domain is predicted. The specific calculation method is as follows:

[0094] ;

[0095] in, for The derivative of the target spacecraft state in the time domain.

[0096] Step S5, optimizing the strategy parameters of the pursuit spacecraft to maximize the set performance index at the end of the next time domain, outputting the pursuit strategy parameters after the index converges, and obtaining the optimal control in the next time domain;

[0097] S5.1. Based on the predicted motion of the target spacecraft, integrate to obtain the next time domain Trajectory of internal target spacecraft;

[0098] S5.2. Set the optimal control payoff function of the pursuit spacecraft to:

[0099] ;

[0100] According to the optimal control principle, the optimal control of the pursuit spacecraft under the strategy parameters is obtained as:

[0101] ;

[0102] S5.3. Set the pursuit index fitness function that the pursuit spacecraft expects to achieve in the next time domain. Its specific expression is as follows:

[0103] ;

[0104] ;

[0105] in, is the control consumption term, is the integral value of the spacecraft control quantity in a single time domain, is the relative distance between the two spacecraft at the end of the next time domain, is the relative speed of the two spacecraft at the end of the next time domain, are the control cost of the pursuit spacecraft in the next time domain, the relative distance between the two spacecraft terminals, and the weighted terms of the relative speed between the two spacecraft terminals, respectively. is the strategy parameter for the pursuit spacecraft, which can be expressed as:

[0106] ;

[0107] S5.4. Setting weight coefficients , using genetic algorithms to optimize the independent variables , so that the fitness function set in the next time domain Minimum, output at this time ;

[0108] S5.5. Optimize the pursuit strategy parameters Substitution , the pursuit spacecraft control in the next time domain is obtained and output, the pursuit spacecraft control in a single time domain is solved, the pursuit spacecraft and the target spacecraft trajectory are output, and steps S3 to S5 are continued until the pursuit success judgment condition is met and the process jumps out.

[0109] In the simulation results, the target spacecraft is set to be in the geosynchronous orbit at the initial moment, which is a circular orbit with a radius of 42,000 km. The state of the target spacecraft at the initial moment is used as a virtual reference point, and the state of the pursuit spacecraft at the initial moment is , set the target spacecraft strategy parameters: , set the pursuit spacecraft optimization parameters: , the pursuit spacecraft uses the parameter optimization method to achieve a relative distance of less than 1km with the target spacecraft at 1420s, and is considered to have completed the pursuit. Figure 3-Figure 4 As shown in Figure 2, in the pursuit-escape game, the maximum amplitude of the pursuit spacecraft control is 0.5213 m / s 2 The total speed increment is 2.1290 km / s. The maximum control amplitude of the typical differential game method that directly uses the target spacecraft's strategy value as the pursuit spacecraft's strategy value without using parameter optimization is 0.4811 m / s 2 , the total velocity increment is 3.4929 km / s, such as Figure 5-Figure 6 As shown in the figure, the total speed increment of the method proposed in the present invention is 60.95% of that of the direct method. To verify the universality of the method, 100 Monte Carlo simulations were performed with the initial position state of the pursuit satellite between [-50km, 50km] and the initial speed state between [-0.1km / s, 0.1km / s]. The Monte Carlo numerical simulation results show that this technology can effectively reduce the total speed increment consumption of the pursuit spacecraft. In all test scenarios, for the same initial situation, the total speed increment consumption of the method proposed in the present invention is reduced by more than 23% compared with the total speed increment consumption of the traditional typical differential game method.

[0110] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made on the basis of the present invention. Therefore, these modifications or improvements made on the basis of not departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for optimizing parameters of trajectory pursuit strategy under incomplete information, characterized by: The following steps are involved: Step S1, taking the initial position of the non-cooperative target spacecraft as the origin of the orbital pursuit scene, setting a relative coordinate system, and describing the states of the pursuit spacecraft and the target spacecraft in this relative system; Step S2, setting a non-cooperative target spacecraft control weight coefficient to characterize the target spacecraft escape strategy; Step S3, taking the states of the target spacecraft and the pursuit spacecraft as observation quantities, and the augmented vector with the target control weight coefficient as the state quantity, using a Kalman filter, and obtaining a non-cooperative target escape strategy through observation quantity filtering and updating; Step S4, taking the non-cooperative target escape strategy obtained by filtering at the current moment as a benchmark, assuming that the escape strategy of the target spacecraft remains unchanged in the next time domain, and obtaining the control output of the target spacecraft in the next time domain; Step S5, optimizing the strategy parameters of the pursuit spacecraft to maximize the set performance index at the end of the next time domain, outputting the pursuit strategy parameters after the index converges, and obtaining the optimal control in the next time domain; Repeat steps S3 to S5 until the task is completed.

2. The method for optimizing parameters of trajectory pursuit strategy under incomplete information according to claim 1, characterized in that: The step S1 comprises the following steps: S1.

1. The target spacecraft position at the initial moment is taken as the virtual reference center point. This reference center orbits around the earth under the action of the earth's gravity. The reference system takes the x-axis in the opposite direction of the position vector of the virtual reference center point as the x-axis, the z-axis direction unit vector is perpendicular to the orbital plane of the virtual reference center point, and the y-axis is given by the right-hand rule. S1.

2. Since the distances between the pursuit spacecraft and the target spacecraft and the virtual reference center point are much greater than the distance between the virtual reference center point and the center of the earth during the pursuit process, the motion equations of the two spacecraft in the S1.1 reference frame are expressed as follows according to the Clohessy-Wiltshire equation: ； in, is the system matrix, which represents the interaction relationship between the components of the system state vector. is the input matrix, which represents the relationship between the control input and the system state vector; represents the state vector of the pursuit spacecraft / target spacecraft, is the position and velocity difference between the spacecraft and the virtual reference center point, where is the position difference component, is the velocity difference component, represents the control vector of the pursuit spacecraft / target spacecraft, ; t represents time, As a subscript and When represent the pursuit spacecraft and the target spacecraft respectively; and The value is related to the orbit state of the virtual center point at the moment of description, and its calculation method is as follows: ； in, To describe the orbital true anomaly of the virtual reference center point at the moment, To describe the distance from the center of the earth to the virtual reference center point at that moment, is the gravitational constant of the central celestial body. If the virtual reference center point is taken as a circular orbit, then Does not change over time, Is a fixed value.

3. The method for optimizing parameters of trajectory pursuit strategy under incomplete information according to claim 2, characterized in that: The step S2 comprises the following steps: S2.

1. Definition of the pursuit problem: The optimal target state of both parties is expressed as the difference between the two spacecraft: ; S2.2, set the optimal control payment function of non-cooperative target escape as: ； in, To characterize the control weight coefficients of the target spacecraft escape strategy, they are all diagonal matrices, and T is the transpose; S2.

3. According to the optimal control principle, the optimal escape control and target state of the target spacecraft under the optimal control payment function are obtained. Relationship between: ； in, is the Riccati matrix. According to the Riccati equation Find out.

4. The method for optimizing parameters of trajectory pursuit strategy under incomplete information according to claim 3 is characterized in that: The step S3 comprises the following steps: S3.

1. The strategy parameters of the target spacecraft are represented as the state quantity of the filtering system. The specific description form is as follows: ； in, , Related to the target spacecraft escape strategy, which is related to The relationship is: ； in, is the identity matrix, and the number of rows and columns of the matrix is ​​shown in the following subscripts; S3.

2. The difference between the states of the two spacecraft Considered as an observation, the observation and augmented state vector are established The relationship between: ； in, Indicated by Determined Observables and augmentation relation; is the process noise, is the system noise, and the subscript is the number of rows and columns of the matrix; S3.3, use Kalman filtering method to set the initial parameter guess value , perform filtering parameter estimation; S3.4, in the filtering process, firstly according to the observed Solve for the last moment , and then substitute The single-step .

5. The method for optimizing parameters of trajectory pursuit strategy under incomplete information according to claim 4, characterized in that: The step S4 comprises the following steps: S4.

1. Strategy parameters obtained based on filtering , assuming that the next time domain The escape parameters of the target spacecraft do not change, and the motion of the target spacecraft in a time domain is predicted. The specific calculation method is as follows: ； in, for The derivative of the target spacecraft state in the time domain.

6. The method for optimizing parameters of trajectory pursuit strategy under incomplete information according to claim 5, characterized in that: The step S5 comprises the following steps: S5.

1. Based on the predicted motion of the target spacecraft, integrate to obtain the next time domain Trajectory of internal target spacecraft; S5.

2. Set the optimal control payoff function of the pursuit spacecraft to: ； According to the optimal control principle, the optimal control of the pursuit spacecraft under the strategy parameters is obtained as: ； S5.

3. Set the pursuit index fitness function that the pursuit spacecraft expects to achieve in the next time domain. Its specific expression is as follows: ； ； in, is the control consumption term, is the integral value of the spacecraft control quantity in a single time domain, is the relative distance between the two spacecraft at the end of the next time domain, is the relative speed of the two spacecraft at the end of the next time domain, are the control cost of the pursuit spacecraft in the next time domain, the relative distance between the two spacecraft terminals, and the weighted terms of the relative speed between the two spacecraft terminals, respectively. is the strategy parameter for the pursuit spacecraft, which can be expressed as: ； S5.

4. Setting weight coefficients , using genetic algorithms to optimize the independent variables , so that the fitness function set in the next time domain Minimum, output at this time ; S5.

5. Optimize the pursuit strategy parameters Substitution , the pursuit spacecraft control in the next time domain is obtained and output, the pursuit spacecraft control in a single time domain is solved, the pursuit spacecraft and the target spacecraft trajectory are output, and steps S3 to S5 are continued until the pursuit success judgment condition is met and the process jumps out.

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