A simulation method for the fuel-saving orbit change mission of "one-to-many" approach to space targets

Abstract

The invention relates to a simulation method for a fuel-saving orbit change task for "one-to-many" approach to space targets. The objective function is designed with the minimum velocity increment vector modulus required in the orbit change process as the goal; the optimization levels are set, the first level: setting approach sequence optimization variables, and determining the approach sequence of a service aircraft arriving at a target satellite through the variable size; the second and third levels: determining the value range of the approach orbit change end time optimization variable for each target satellite that satisfies both a measurement and control window and an illumination window; determining the value range of the approach orbit change start time optimization variable for each target satellite; the fourth level: under a given approach sequence, optimizing the given approach orbit change end time and approach orbit change start time according to the objective function to obtain an optimization result; according to the set optimization level order, starting the optimization by level, selecting the best result from the optimization results to obtain a fuel-saving orbit change strategy.

Description

Technical Field

[0001] The invention belongs to the technical field of on-orbit service and maintenance of spacecraft. Background Art

[0002] On-orbit service and maintenance is one of the hot spots at the forefront of aerospace technology. After the service vehicle approaches the space target satellite, it can carry out close observation, module replacement, fuel filling, auxiliary orbit change and other service operations, which can realize the on-orbit fault recovery and extend the life of the target satellite, so as to continue to play the value of on-orbit application and generate great economic benefits. In order to carry out service and maintenance operations on the space target satellite, the prerequisite is to approach the target satellite and dock at zero distance.

[0003] The service vehicle itself has a cost, and operations such as approaching, docking, and repairing target satellites also require a certain amount of cost. A service vehicle's approach to and repair of multiple target satellites is a necessary way to achieve the economic benefits of in-orbit servicing. Therefore, the research on the simulation method of the fuel-saving orbit change mission for a service vehicle's "one-to-many" approach to multiple target satellites has very important practical significance.

[0004] At present, there are no public literature reports or patents at home and abroad that are the same or similar to the simulation method of the most fuel-saving orbit change mission close to the "one-to-many" space approach. Summary of the invention

[0005] The technical problem solved by the present invention is to provide a method for simulating orbit change tasks for a "one-to-many" approach to space targets, which is suitable for optimizing the orbit change control strategy of an on-orbit service mission in which a high-orbit service aircraft approaches multiple space targets in the same high orbit within a given time to perform maintenance tasks, so as to achieve the purpose of minimizing fuel consumption during the approach process.

[0006] The technical solution of the present invention is: a method for simulating a fuel-saving orbit change mission for a "one-to-many" approach to a space target, comprising:

[0007] The service spacecraft uses a double-pulse orbit change strategy to approach each target satellite in sequence, and designs the objective function with the minimum velocity increment vector modulus required during the orbit change process as the goal;

[0008] Set the optimization level, where

[0009] First layer: setting approach sequence optimization variables, and determining the approach sequence of the service spacecraft to the target satellite by the size of the variables;

[0010] The second and third layers: within the total time limit for the service spacecraft to complete the approach to all target satellites, determine the value range of the optimization variable of the approach change end time that satisfies both the measurement and control window and the illumination window for each target satellite; determine the value range of the optimization variable of the approach change start time for each target satellite;

[0011] The fourth layer: Under a given approach sequence, the given approach change end time and approach change start time are optimized according to the above objective function to obtain the optimization result;

[0012] According to the set optimization level order, start optimization by level, and select the best result from the optimization results to obtain the most fuel-saving orbit change strategy.

[0013] Preferably, the approach sequence is set by:

[0014] Number the n target satellites as 1, 2, ..., n, and set the approach sequence S1, S2, ..., S n The value range is [0,1], and the service aircraft is divided into S1, S2, ..., S n The target satellites with corresponding numbers are approached from first to last in the order from small to large.

[0015] Preferably, the value range of the optimization variable of the approaching orbit change end time for each target satellite that satisfies both the measurement and control window and the illumination window is determined by the following method:

[0016] Within the total time limit for the service spacecraft to complete the approach to all target satellites, the tracking and control window interval of the target satellite is calculated based on the position of the ground tracking and control station, the trackable azimuth and pitch angle, and the orbital parameters of the target satellite;

[0017] The illumination window interval of the target satellite is calculated according to the orbital parameters of the target satellite;

[0018] The intersection of the measurement and control window interval and the illumination window interval is taken to obtain the window interval in which the service aircraft can approach the target satellite;

[0019] According to the order in which the target satellites are approached by the serviced spacecraft, the approach and orbit change end time optimization variable is set to a floating point number with a value greater than 1. The value range of the approach and orbit change end time optimization variable is determined according to the principle that the integer part represents which approach window the approach and orbit change end time belongs to the target satellite, and the decimal part represents the proportion of the approach and orbit change end time in the window.

[0020] Preferably, the value range of the optimization variable of the end time of approaching orbit change of the i-th target satellite is:

[0021]

[0022] Among them, the number of approachable windows of the i-th target satellite is n wi , n represents the total number of target satellites; b1 ranges from [0.5,1], and b2 ranges from (1,1.5].

[0023] Preferably, the value range of the optimization variable for the approaching orbit change start time of each target satellite is [τ i ,λ i ];

[0024] τ i =λ i-1 +h i-1

[0025] In the formula, λ i -1 is the time when the service vehicle approaches the i-1th target satellite and ends the orbit change; i h is the end time of the service spacecraft approaching the i-th target satellite; i-1 The operating time used by the service vehicle to provide maintenance services to the i-1th target.

[0026] Preferably, in the optimization iteration of the fourth layer, an optimization algorithm is used to iteratively simulate the double-pulse velocity increment of the service spacecraft approaching the orbit change. After the simulation, the angle between the sunlight to the service spacecraft vector and the target satellite to the service spacecraft vector is calculated when the service spacecraft approaches. If the angle is less than the front-light constraint angle, it is determined that the front-light observation condition does not meet the requirements, and the sum of the double-pulse velocity increment moduli is set to a very large number, which is larger than the order of magnitude of the result after optimization by the optimization algorithm.

[0027] Preferably, the optimization algorithm adopts Lambert algorithm.

[0028] Preferably, optimization is started in the following manner by level:

[0029] The first layer of optimization variables obtains fixed values ​​within the value range, and the second layer iterates and optimizes according to the fixed values ​​of the first layer. During the second layer optimization process, the third layer needs to iterate according to the fixed values ​​of the optimization variables of the second layer. During the third layer optimization process, the fourth layer needs to iterate according to the fixed values ​​of the optimization variables of the third layer.

[0030] Preferably, the optimization order is as follows:

[0031] When the fourth layer completes the iteration, the third layer changes the value of the optimization variable, and the fourth layer iterates again according to the value of the third layer until the third layer completes the iteration; when the third layer completes the iteration, the second layer changes the value of the optimization variable, and the third layer iterates again according to the value of the second layer until the second layer completes the iteration; when the second layer completes the iteration, the first layer changes the value of the optimization variable, and the second layer iterates again according to the value of the first layer until the first layer completes the iteration of all values, at which point the optimization simulation is completed.

[0032] Preferably, the condition for completing iteration at each layer is a preset maximum number of iterations.

[0033] The beneficial effects of the present invention compared with the prior art are:

[0034] (1) The present invention provides a trajectory change strategy for a high-orbit service vehicle to reach and rendezvous with multiple space targets in the same high orbit, and provides a simulation calculation method for the trajectory change sequence with the least fuel for the service vehicle to sequentially approach n targets, which can realize the "one-to-many" trajectory change rendezvous with the least fuel under a given time constraint;

[0035] (2) The present invention comprehensively considers the constraints of measurement and control, illumination, time, etc. during the process of the service aircraft changing orbits to multiple targets, and optimizes the orbital maneuvering strategy of the service aircraft changing orbits to multiple targets in succession by using genetic, ant colony, particle swarm and other optimization algorithms. There are many optimization design variables, including the approach order of the service aircraft to multiple targets, the start approach time, the end approach time, and the vector value of the orbit change speed increment. If the number of target satellites is set to n, the number of optimization variables reaches 9n. The increase in the number of variables leads to a large scale of optimization design and difficulty in convergence. In response to this problem, the present invention proposes a four-level optimization strategy, the first level optimizes the approach order, the second level optimizes the end approach time, the third level optimizes the start approach time, and the fourth level optimizes the vector value of the orbit change speed increment. The optimization goal is to minimize the orbit change speed increment consumed by the service aircraft to complete the approach task to all targets, that is, to save the most fuel. In the optimization search process, the objective function is simulated and calculated under the four-level optimization strategy, which effectively improves the convergence speed. The four-level optimization of the present invention avoids the problem of too many optimization variables and difficulty in convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Service vehicle begins orbital maneuvers

[0037] Figure 2 Service vehicle approaches target satellite 3

[0038] Figure 3 Service spacecraft approaches target satellite 4

[0039] Figure 4 Service vehicle approaches target satellite 2

[0040] Figure 5 Service vehicle approaches target satellite 1 DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the embodiments.

[0042] The present invention relates to a method for simulating an orbit change mission for a "one-to-many" approach to a space target, and is suitable for optimizing the orbit change control strategy for an on-orbit service mission in which a high-orbit service aircraft approaches a plurality of space targets in the same high orbit within a given time to perform a maintenance mission, so as to achieve the purpose of minimizing fuel consumption during the approach process.

[0043] A simulation method for a fuel-saving orbit change mission for a "one-to-many" approach to a space target, the implementation steps of which are as follows:

[0044] (1) Design objective function

[0045] The purpose of the present invention is to find a simulation method for the most fuel-saving orbit change mission for a space "one-to-many" approach mission. First, the objective function needs to be designed.

[0046] The number of target satellites for the “one-to-many” approach mission is set to n. The service spacecraft uses a double-pulse orbit change strategy to approach each target satellite in sequence. The velocity increment vector modulus required to start the orbit change for approaching the i-th target satellite is Δv i1 , the velocity increment vector modulus required to end the track change is Δv i2 , then the objective function of the “one-to-many” approach task is:

[0047]

[0048] (2) Design optimization variables

[0049] The optimization variables include:

[0050] 1) The approach sequence of the service spacecraft to n target satellites is S1, S2, ..., S n , the number of variables is n;

[0051] 2) The starting orbit change time t of the service spacecraft approaching the i-th target satellite i1 , start the track change speed increment Δv ix1 , Δv iy1 , Δv iz1 , end track change time t i2 , end the track change speed increment Δvix2, Δv iy2 , Δv iz2 , the number of variables for each target satellite is 8, and the number of variables for n target satellites is 8n.

[0052] The total number of optimization variables is 9n. It can be seen that with the increase in the number of target satellites, the number of optimization variables increases sharply, resulting in great difficulties in the optimization convergence of the "one-to-many" approach strategy.

[0053] (3) Four-layer optimized design

[0054] In order to solve the problem of difficulty in optimization convergence due to the large number of optimization variables, a four-layer optimization design method is adopted:

[0055] 1) First layer: Generate approach sequence

[0056] Number the n target satellites as 1, 2, ..., n, and set the approach sequence S1, S2, ..., Sn The value range is [0,1]. During the optimization iteration, the service aircraft is divided into S1, S2, …, S n The target satellites with corresponding numbers are approached from first to last in the order from small to large.

[0057] 2) The second layer: Generate the end time of approaching orbit change for each target satellite

[0058] When the first-layer approach sequence is given, the approach change end time for each target satellite is generated according to the sequence.

[0059] It is a very complex operation for a service spacecraft to implement close docking with a target satellite, which usually needs to be carried out under the support of ground measurement and control. At the same time, the service spacecraft needs to perform precise measurements of the target satellite during the approach process, which requires the support of direct sunlight observation conditions. Therefore, the end time of the approach and orbit change of the target satellite must meet the requirements of being in both the measurement and control area and the direct sunlight observation area.

[0060] Within the total time limit for the service aircraft to complete the approach to all target satellites, the target satellite's measurement and control window interval can be calculated based on the position of the ground measurement and control station, the measurable azimuth and pitch angle, and the orbital parameters of the target satellite. The target satellite's illumination window interval can be calculated based on the orbital parameters of the target satellite. The measurement and control window interval and the illumination window interval are intersected to obtain the service aircraft's approach window interval for the target satellite. Let the number of approach windows for the i-th target satellite be n wi , then the approachable window range can be expressed as:

[0061]

[0062] Where t is1 Indicates the start time of the first window, t is2 Indicates the end time of the second window, and so on.

[0063] According to the order in which the target satellites are approached by the serviced spacecraft, the value range of the optimization variable for the end time of the approach of the i-th target satellite is:

[0064]

[0065] In the formula, b1 can be a decimal close to 1, such as 0.75; b2 can be a decimal greater than 1, such as 1.25.

[0066] It can be seen from the above formula that in the optimization iteration, the optimization variable of the approach change end time will take the value of a floating point number greater than 1. Let its integer part be k, which means that the approach change end time belongs to the kth approach window of the target satellite, and the decimal part is p, which means the proportion of the approach change end time in the window. For example, the optimization variable value of 2.6 means that the approach change end time is t is2 +0.6×(t ie2 -t is2 ).

[0067] 3) The third layer: Generate the start time of approaching orbit change for each target satellite

[0068] Assume that the time it takes for the service vehicle to complete the service operation on the i-th target satellite is τ i , which is calculated as:

[0069] τ i =λ i-1 +h i-1

[0070] Where λ i-1 The time when the service vehicle approaches the i-1th target satellite and ends the orbit change. i-1 The operation time used by the service vehicle to provide maintenance services to the i-1th target is used as the input value of the trajectory change mission simulation.

[0071] The value range of the optimization variable for the start time of approaching orbit change for each target satellite is:

[0072] [τ i ,λ i ]

[0073] 4) The fourth layer: Simulate and generate the double-pulse velocity increments for approaching and changing orbits of each target satellite

[0074] According to the conditions given by the previous three levels of optimization, the start time and end time of the service spacecraft approaching the orbit change can be known. The orbit parameters at the beginning of the service spacecraft's orbit change can be extrapolated from the orbit parameters of the service spacecraft's previous state, and the orbit parameters at the end of the service spacecraft's orbit change can be extrapolated from the orbit parameters of the target satellite at the initial moment. Then, the time and orbit parameters of the start of the orbit change, and the time and orbit parameters of the end of the orbit change are used as the initial point and end point of the orbit change. The Lambert algorithm can be used to iteratively simulate the double pulse velocity increment of the service spacecraft approaching the orbit change. The optimization goal is to minimize the sum of the moduli of the two pulse velocity increments.

[0075] When the spacecraft approaches the target satellite, it needs to conduct optical detection of the target. This requires that the spacecraft should be in a state of direct sunlight observation when approaching the target satellite. When the service spacecraft approaches, the angle between the vector from sunlight to the service spacecraft and the vector from the target satellite to the service spacecraft is calculated. If the angle is less than the direct sunlight constraint angle (for example, 90°), it is determined that the direct sunlight observation condition does not hold. The sum of the two pulse velocity increment moduli is set to a large number (for example, 1×10 16 ).

[0076] 5) Four-level optimization method

[0077] After the first-layer optimization variables obtain fixed values ​​within the value range, the second layer iterates and optimizes according to the fixed values ​​of the first layer. During the second-layer optimization process, the third layer needs to iterate according to the fixed values ​​of the optimization variables of the second layer. During the third-layer optimization process, the fourth layer needs to iterate according to the fixed values ​​of the optimization variables of the third layer.

[0078] When the fourth layer completes the iteration, the third layer changes the value of the optimization variable, and the fourth layer iterates again according to the value of the third layer, until the third layer completes the iteration of all values; when the third layer completes the iteration, the second layer changes the value of the optimization variable, and the third layer iterates again according to the value of the second layer, until the second layer completes the iteration of all values; when the second layer completes the iteration, the first layer changes the value of the optimization variable, and the second layer iterates again according to the value of the first layer, until the first layer completes the iteration of all values. At this time, the optimization simulation is completed, and the fuel-saving and optimal orbit change strategy for the service vehicle approaching multiple target satellites can be obtained.

[0079] Example

[0080] According to this method, an optimization simulation example of the track change control strategy is given, such as Figure 1-5 shown.

[0081] The service spacecraft’s “one-to-four” approach and orbit change to four target satellites within five days was simulated and optimized.

[0082] At the initial time (recorded as time 0), the orbital elements of the service spacecraft and the target satellite are shown in the following table:

[0083] Table 1 Orbital elements of the service vehicle and target satellite

[0084]

[0085] After optimization calculation, the approach sequence of the service spacecraft to the four target satellites is: target satellite 3, target satellite 4, target satellite 2, and target satellite 1.

[0086] The orbit change time and orbit change speed increment of the service spacecraft approaching the four target satellites are:

[0087] Table 2 Orbit change time and orbit change speed increment of service aircraft

[0088]

[0089]

[0090] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for simulating a fuel-saving orbit change mission for a "one-to-many" approach to a space target, characterized in that include: The service spacecraft uses a double-pulse orbit change strategy to approach each target satellite in sequence, and designs the objective function with the minimum velocity increment vector modulus required during the orbit change process as the goal; Set the optimization level, where First layer: setting approach sequence optimization variables, and determining the approach sequence of the service spacecraft to the target satellite by the size of the variables; The second and third layers: within the total time limit for the service spacecraft to complete the approach to all target satellites, determine the value range of the optimization variable of the approach change end time that satisfies both the measurement and control window and the illumination window for each target satellite; determine the value range of the optimization variable of the approach change start time for each target satellite; The fourth layer: Under a given approach sequence, the given approach change end time and approach change start time are optimized according to the above objective function to obtain the optimization result; According to the set optimization level order, start optimization by level, and select the best result from the optimization results to obtain the most fuel-saving orbit change strategy; The value range of the optimization variable of the approaching orbit change end time for each target satellite that satisfies both the measurement and control window and the illumination window is determined by the following method: Within the total time limit for the service spacecraft to complete the approach to all target satellites, the tracking and control window interval of the target satellite is calculated based on the position of the ground tracking and control station, the trackable azimuth and pitch angle, and the orbital parameters of the target satellite; The illumination window interval of the target satellite is calculated according to the orbital parameters of the target satellite; The intersection of the measurement and control window interval and the illumination window interval is taken to obtain the window interval in which the service aircraft can approach the target satellite; According to the order in which the target satellites are approached by the serviced spacecraft, the approach and orbit change end time optimization variable is set to a floating point number with a value greater than 1, and the value range of the approach and orbit change end time optimization variable is determined according to the principle that the integer part represents which approach window the approach and orbit change end time belongs to the target satellite, and the decimal part represents the proportion of the approach and orbit change end time in the window; The value range of the optimization variable for the end time of approaching orbit change of the i-th target satellite is: Among them, the number of approachable windows of the i-th target satellite is n wi , n represents the total number of target satellites; b1 ranges from [0.5,1], and b2 ranges from (1,1.5]; The value range of the optimization variable for the start time of approaching orbit change of each target satellite is [τ i ,λ i ]; t i =λ i-1 +h i-1 In the formula, λ i-1 The time when the service vehicle approaches the i-1th target satellite and ends the orbit change; i h is the end time of the service spacecraft approaching the i-th target satellite; i-1 The operating time used by the service aircraft to provide maintenance services to the i-1th target; In the fourth-level optimization iteration, an optimization algorithm is used to iteratively simulate the double-pulse velocity increment of the service spacecraft approaching the orbit change. After the simulation, the angle between the vector from sunlight to the service spacecraft and the vector from the target satellite to the service spacecraft is calculated when the service spacecraft approaches. If the angle is less than the front-light constraint angle, it is determined that the front-light observation condition does not meet the requirements, and the sum of the double-pulse velocity increment moduli is set to a very large number, which is larger than the order of magnitude of the result after optimization by the optimization algorithm.

2. The simulation method according to claim 1, characterized in that: Start optimizing by level as follows: The first layer of optimization variables obtains fixed values ​​within the value range, and the second layer iterates and optimizes according to the fixed values ​​of the first layer. During the second layer optimization process, the third layer needs to iterate according to the fixed values ​​of the optimization variables of the second layer. During the third layer optimization process, the fourth layer needs to iterate according to the fixed values ​​of the optimization variables of the third layer.

3. The simulation method according to claim 2, characterized in that: The optimization order is as follows: When the fourth layer completes the iteration, the third layer changes the value of the optimization variable, and the fourth layer iterates again according to the value of the third layer until the third layer completes the iteration; when the third layer completes the iteration, the second layer changes the value of the optimization variable, and the third layer iterates again according to the value of the second layer until the second layer completes the iteration; when the second layer completes the iteration, the first layer changes the value of the optimization variable, and the second layer iterates again according to the value of the first layer until the first layer completes the iteration of all values, at which point the optimization simulation is completed.

4. The simulation method according to claim 3, characterized in that: The condition for completing iteration at each layer is the preset maximum number of iterations.

5. The simulation method according to claim 1, characterized in that: The approach sequence is set up in the following way: Number the n target satellites as 1, 2, ..., n, and set the approach sequence S1, S2, ..., S n The value range is [0,1], and the service aircraft is divided into S1, S2, ..., S n The target satellites with corresponding numbers are approached from first to last in the order from small to large.

6. The simulation method according to claim 1, characterized in that: The optimization algorithm adopts Lambert algorithm.

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