Double-satellite formation satellite lightweight autonomous task planning and scheduling method based on multi-objective optimization

Abstract

A double-satellite formation satellite lightweight autonomous task planning and scheduling method based on multi-objective optimization realizes double-satellite formation satellite autonomous task planning and scheduling with optimal comprehensive efficiency on the basis of on-satellite constraints, and ensures high-precision inter-satellite pointing and inter-satellite spacing maintenance during long-term operation of double satellites. According to the invention, the space between two satellites, the execution capability of a satellite thruster and the like are taken as constraints, a task planning mathematical model is established, formation reference satellite scheduling and orbit / attitude control strategy generation are carried out based on multiple optimization targets, and on-satellite full-autonomous, lightweight and optimal-comprehensive-efficiency rapid autonomous task scheduling is realized.

Description

Technical Field

[0001] The invention relates to a lightweight autonomous task planning and scheduling method for dual-satellite formation satellites based on multi-objective optimization, and belongs to the technical field of spacecraft control. Background Art

[0002] For low-low tracking gravity measurement satellites, a dual-satellite formation is required to operate for a long time, during which time inter-satellite pointing attitude control and inter-satellite distance to maintain orbit control are required. The task sequence involved in this process is complex and the attitude and orbit control accuracy requirements are high.

[0003] In order to improve the autonomous survivability of satellites, an autonomous task planning and scheduling method for dual-satellite formation satellites with optimal comprehensive performance was designed based on on-board constraints. Using the dual-satellite orbital parameters provided by the ground or the dual-satellite orbital information provided by intersatellite communication, the intersatellite pointing target angle calculation and satellite spacing prediction calculation are performed autonomously. Taking the dual-satellite spacing and satellite thruster execution capability as constraints, a task planning mathematical model is established, and the formation reference satellite scheduling and orbit control strategy generation are performed based on multiple optimization objectives. After the orbit control is completed, it is autonomously transferred to high-precision intersatellite pointing control, and the optimal attitude control task sequence and configuration parameters are selected according to the terminal input of the orbit control. Summary of the invention

[0004] This patent application proposes a lightweight autonomous task planning and scheduling method for dual-satellite formation satellites based on multi-objective optimization. It integrates the dual-satellite pointing performance and satellite spacing indicators during the formation process, considers the on-board resource constraints, and performs formation reference satellite scheduling and orbit / attitude control strategy generation based on multiple optimization objectives, thus achieving full autonomy, lightweight, and comprehensive performance-optimized rapid autonomous task scheduling on the satellite.

[0005] The technical solution of the present invention is: a lightweight autonomous task planning and scheduling method for dual-satellite formation satellites based on multi-objective optimization, comprising:

[0006] Using the binary satellite orbit parameters provided by the ground or the binary satellite orbit information provided by inter-satellite communication, calculate the inter-satellite pointing target angle and predict the satellite distance, and use the calculated values ​​as the input of mission planning;

[0007] Perform dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, and establish a mission planning model;

[0008] Based on the task planning model, dynamic optimization is used to generate attitude / orbit task strategies, that is, to obtain the optimal scheduling plan for the task sequence.

[0009] Preferably, the inter-satellite pointing target angle θ r , r The calculation formula is:

[0010]

[0011] r pvo =A OI r pv

[0012] Normalization pvo =r pvo / ||r pvo ||

[0013]

[0014] ψ r =arctan2(r pvo [1],r pvo [0])

[0015] Among them, x, y, and z are the three-dimensional positions of the star in the inertial system. T ,y T 、z T are the three-dimensional positions of the target star in the inertial system, A OI is the direction cosine matrix of the local orbital system relative to the inertial system;

[0016] || || is the norm of the matrix.

[0017] Preferably, the calculation formula for the star spacing is

[0018] D r =||r pv ||.

[0019] Preferably, dual-satellite pointing control is performed in the non-orbit control phase or position control is performed in the orbit control phase, target characteristics are analyzed and optimized, and a mission planning model is established:

[0020] min f=g(E1,E2,...,E n ) (1)

[0021]

[0022] Formula (1): is the planning optimization objective function, E i represents the total benefit of formation and pointing of satellite i, i∈{1,...,n}, n is the total number of satellites in the formation, and the multiplication weighted method is used to integrate the optimization objectives;

[0023] Formula (2): Decision variables represents the decision variable of task sequence j corresponding to optimization objective i. If the task is executed, then is 1, otherwise it is 0;

[0024] Formula (3): Decision variables The corresponding constraint set C includes thruster thrust constraints and satellite attitude constraints;

[0025] Formula (4): To optimize the execution start time of task sequence k corresponding to target i, w i It is the time zero point of the task sequence corresponding to the optimization target.

[0026] Preferably, an evaluation function of weighted product or weighted sum or a combination of the two is used to simplify the solution of the task planning model.

[0027] Preferably, the evaluation function combining the weighted product and the weighted sum is formally described as follows:

[0028]

[0029] Where η i1 is the weight coefficient of the task priority. The higher the priority, the greater the value assigned. Represents the total time the thruster is turned on. Represents the total time the distance between stars is maintained; is the number of task switches; Represents control accuracy; represents the robustness index, i.e., the tolerance envelope of the system to uncertainty; is the corresponding weight coefficient, which takes a value between 0 and 1, and the sum of the total weight coefficients is 1.

[0030] A dual-satellite formation lightweight autonomous mission planning and scheduling system based on multi-objective optimization, including:

[0031] The input determination module uses the binary satellite orbit parameters provided by the ground or the binary satellite orbit information provided by inter-satellite communication to calculate the inter-satellite pointing target angle and the predicted satellite spacing, and uses the calculated values ​​as the input of the mission planning;

[0032] Model building module, which performs dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, and establishes a mission planning model;

[0033] The dynamic optimization module uses the calculation results of the input determination module as the input of task planning. According to the constructed task planning model, it uses dynamic optimization to generate attitude / orbit task strategies, that is, to obtain the optimal scheduling plan for the task sequence.

[0034] Preferably, the inter-satellite pointing target angle θ r , r The calculation formula is:

[0035]

[0036] r pvo =AOI r pv

[0037] Normalization pvo =r pvo / ||r pvo ||

[0038]

[0039] ψ r =arctan2(r pvo [1],r pvo [0])

[0040] Among them, x, y, and z are the three-dimensional positions of the star in the inertial system. T ,y T 、z T are the three-dimensional positions of the target star in the inertial system, A OI is the direction cosine matrix of the local orbital system relative to the inertial system;

[0041] || || is the norm of the matrix;

[0042] The calculation formula of the star spacing is

[0043] D r =||r pv ||.

[0044] Preferably, dual-satellite pointing control is performed in the non-orbit control phase or position control is performed in the orbit control phase, target characteristics are analyzed and optimized, and a mission planning model is established:

[0045] min f=g(E1,E2,...,E n ) (1)

[0046]

[0047] Formula (1): is the planning optimization objective function, E i represents the total benefit of formation and pointing of satellite i, i∈{1,...,n}, n is the total number of satellites in the formation, and the multiplication weighted method is used to integrate the optimization objectives;

[0048] Formula (2): Decision variables represents the decision variable of task sequence j corresponding to optimization objective i. If the task is executed, then is 1, otherwise it is 0;

[0049] Formula (3): Decision variables The corresponding constraint set C includes thruster thrust constraints and satellite attitude constraints;

[0050] Formula (4): To optimize the execution start time of task sequence k corresponding to target i, w i It is the time zero point of the task sequence corresponding to the optimization target.

[0051] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for lightweight autonomous task planning and scheduling of dual-satellite formation satellites based on multi-objective optimization.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] (1) This patent application proposes a lightweight autonomous task planning and scheduling method for dual-satellite formation satellites based on multi-objective optimization. It is a novel fully autonomous dynamic optimization solution method for the coordinated control of the attitude and orbit of low-low tracking formation satellites. It solves the problem of fully autonomous task planning for high-precision gravity measurement satellites that need to perform intersatellite pointing in a high-precision formation configuration. Similar methods of this patent application have not been reported in domestic or foreign literature or published patents.

[0054] (2) Compared with the existing technology, the technical method adopted by this patent application fully exploits the mission planning characteristics of low-gravity tracking formation satellites. Through the design of dynamic planning and optimal strategy, it cleverly solves the problem of fast and efficient mission scheduling of formation dual satellites. The algorithm is simple and feasible, and can meet the requirements of high-precision indicators. The entire algorithm design is simple and the workload of parameter debugging is small.

[0055] (3) This patent application proposes a new solution to the mission planning problem of the entire process of low-to-low-gravity tracking satellite formation. It does not require additional data input and is simple to calculate. The algorithm can be adapted to a large class of military and civilian satellite systems with similar formation control requirements and has strong engineering practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is the algorithm flow chart.

[0057] Figure 2 This is the star spacing variation curve.

[0058] Figure 3 This is the double-satellite pointing accuracy curve during the non-orbit control period. DETAILED DESCRIPTION

[0059] To make the purpose, technical solutions and advantages of the present invention more clear, the following will be combined with the attached Figure 1 The embodiments of the present invention are described in further detail.

[0060] The present invention relates to a dual-satellite formation satellite lightweight autonomous task planning and scheduling method based on multi-objective optimization, comprising the following steps, respectively obtaining a task planning input, a task planning model and a final optimization scheduling scheme:

[0061] Step 1: Calculate the inter-satellite pointing target angle and predict the inter-satellite distance;

[0062] Using the binary satellite orbit parameters provided by the ground or the binary satellite orbit information provided by inter-satellite communication, the binary satellite orbit extrapolation calculation, inter-satellite pointing target angle calculation and satellite spacing prediction calculation are independently performed.

[0063] Intersatellite pointing target angle θ r , r The calculation formula is:

[0064]

[0065] r pvo =A OI r pv

[0066] Normalization pvo =r pvo / ||r pvo ||

[0067]

[0068] ψ r =arctan2(r pvo [1],r pvo [0])

[0069] Among them, x, y, and z are the three-dimensional positions of the star in the inertial system. T ,y T 、z T are the three-dimensional positions of the target star in the inertial system, A OI is the direction cosine matrix of the local orbital system relative to the inertial system.

[0070] The calculation formula of the star spacing is

[0071] D r =||r pv ||

[0072] Step 2: Establish the task planning model;

[0073] Perform dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, conduct in-depth analysis of the optimization target (highest accuracy or most fuel-efficient or highest sensor relaxation), and establish a mission planning model:

[0074] min f=g(E1,E2,...,En ) (1)

[0075]

[0076] Formula (1): is the planning optimization objective function, E i represents the total benefit of formation and pointing of satellite i, i∈{1,...,n}, n is the total number of satellites in the formation, and the multiplication weighted method is used to integrate the optimization objectives;

[0077] Formula (2): Decision variables represents the decision variable of task sequence j corresponding to optimization objective i. If the task is executed, then is 1, otherwise it is 0;

[0078] Formula (3): Decision variables The corresponding constraint set C, such as thrust constraint of thruster, attitude constraint of satellite, etc.;

[0079] Formula (4): To optimize the execution start time of task sequence k corresponding to target i, w i It is the time zero point of the task sequence corresponding to the optimization target.

[0080] In order to simplify the multi-objective optimization problem of formation and pointing control, a quantitative comprehensive evaluation function is needed to judge the pros and cons of candidate tracking schemes during task allocation. The evaluation function of weighted product or weighted sum can be used. Here, an evaluation function combining the two is proposed, and its formal description model is as follows:

[0081]

[0082] In the formula, E i Represents the total benefit of formation and pointing of satellite i. η i1 is the weight coefficient of the task priority. The higher the priority, the greater the value assigned. Represents the total time the thruster is turned on. Represents the total time the distance between stars is maintained; is the number of task switches; Represents control accuracy; represents the robustness index, i.e., the tolerance envelope of the system to uncertainty; is the corresponding weight coefficient, which takes values ​​between 0 and 1. The sum of the total weight coefficients is 1. The specific value of each weight is pre-configured according to the characteristics of the task.

[0083] Step 3: Dynamically optimize the attitude / orbit mission strategy generation and scheduling.

[0084] Based on the formation mission planning model, the optimal scheduling scheme is generated through hierarchical optimization. When the formation constraints (the phase difference between the own satellite and the target satellite and the satellite spacing meet the requirements) are met, the satellite is scheduled to the payload mission stage, and attitude control is implemented according to the inter-satellite pointing attitude control mode; when the formation constraints are not met, if the own satellite is the formation reference satellite, orbit control is not performed, and it is still scheduled to the payload mission stage, and attitude control is implemented according to the inter-satellite pointing attitude control mode. If the own satellite is not the formation reference satellite, it is scheduled to the orbit control mission stage. In this stage, the orbit control attitude maneuvering mode is first implemented to establish the orbit control attitude, and then the satellite spacing orbit control mode is autonomously switched to perform orbit control operations.

[0085] In order to realize on-orbit real-time and fast calculation, the present invention adopts a hierarchical optimization strategy according to specific optimization objectives (time optimization, fuel optimization, etc.) to divide the optimization process into rough evaluation optimization and fine evaluation optimization.

[0086] In the rough evaluation optimization, a rough evaluation model based on particle swarm genetic programming is established to quickly evaluate all parameter configuration schemes and optional branches of the sample space to obtain a high-quality solution set, such as the first-level determination of the formation benchmark satellite, sensor configuration, and thrust configuration. In the fine evaluation optimization, an iterative optimization method is used to obtain corrections to the current parameters through feedback information, and the accuracy of the rough evaluation model is gradually improved through continuous optimization, and the scale of the high-quality solution set is reduced. On this basis, the time series of the payload mission phase and the orbit control mission phase and the specific control configuration and control parameters are determined.

[0087] The present invention also provides a dual-satellite formation satellite lightweight autonomous task planning and scheduling system based on multi-objective optimization, which is characterized by comprising:

[0088] The input determination module uses the binary satellite orbit parameters provided by the ground or the binary satellite orbit information provided by inter-satellite communication to calculate the inter-satellite pointing target angle and the predicted satellite spacing, and uses the calculated values ​​as the input of the mission planning;

[0089] Model building module, which performs dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, and establishes a mission planning model;

[0090] The dynamic optimization module uses the calculation results of the input determination module as the input of task planning. According to the constructed task planning model, it uses dynamic optimization to generate attitude / orbit task strategies, that is, to obtain the optimal scheduling plan for the task sequence.

[0091] For the parts of the module's specific functions that are the same as those in the method, you can refer to the relevant introduction in the method.

[0092] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0093] The present invention will be further described below in conjunction with the embodiments.

[0094] Example:

[0095] Taking a certain satellite in orbit as an example, the autonomous planning method for the entire formation process was simulated and verified. In the simulation, the formation configuration of the two satellites must keep the satellite spacing stable at 170km-270km for as long as possible. During the non-orbital control period, the satellites operate in the dual-satellite pointing mode, and the pointing accuracy must meet ≤3mrad (3σ; pitch, yaw), ≤30mrad (3σ; roll). Taking the above indicators as optimization targets, autonomous on-board mission planning was carried out. The satellite autonomously carried out two dual-pulse orbit controls with an orbital control interval of 129 days. The satellite spacing control curve is shown as follows: Figure 2 , the double-satellite pointing control accuracy curve is as follows Figure 3 .

[0096] 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.

[0097] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A lightweight autonomous mission planning and scheduling method for dual-satellite formation based on multi-objective optimization, characterized in that include: Using the binary satellite orbit parameters provided by the ground or the binary satellite orbit information provided by inter-satellite communication, calculate the inter-satellite pointing target angle and predict the satellite distance, and use the calculated values ​​as the input of mission planning; Perform dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, and establish a mission planning model; Based on the task planning model, dynamic optimization is used to generate attitude / orbit task strategies, that is, to obtain the optimal scheduling plan for the task sequence.

2. According to the method of claim 1, a dual-satellite formation satellite lightweight autonomous task planning and scheduling method based on multi-objective optimization is characterized in that: Intersatellite pointing target angle θ r , r The calculation formula is: r pvo ＝A OI r pv Normalization pvo =r pvo / ||r pvo || ψ r =arctan2(r pvo [1],r pvo [0]) Among them, x, y, and z are the three-dimensional positions of the star in the inertial system. T ,y T 、z T are the three-dimensional positions of the target star in the inertial system, A OI is the direction cosine matrix of the local orbital system relative to the inertial system; || || is the norm of the matrix.

3. The method for lightweight autonomous mission planning and scheduling of dual-satellite formation satellites based on multi-objective optimization according to claim 2 is characterized in that: The calculation formula of the star spacing is D r ＝||r pv ||。 4. The method for lightweight autonomous mission planning and scheduling of dual-satellite formation satellites based on multi-objective optimization according to claim 1 is characterized in that: Perform dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, analyze and optimize target characteristics, and establish a mission planning model: where f=g(E1,E2,…,E n ) (1) Formula (1): is the planning optimization objective function, E i represents the total benefit of formation and pointing of satellite i, i∈{1,...,n}, n is the total number of satellites in the formation, and the multiplication weighted method is used to integrate the optimization objectives; Formula (2): Decision variables represents the decision variable of task sequence j corresponding to optimization objective i. If the task is executed, then is 1, otherwise it is 0; Formula (3): Decision variables The corresponding constraint set C includes thruster thrust constraints and satellite attitude constraints; Formula (4): To optimize the execution start time of task sequence k corresponding to target i, w i It is the time zero point of the task sequence corresponding to the optimization target.

5. The method for lightweight autonomous mission planning and scheduling of dual-satellite formation satellites based on multi-objective optimization according to claim 4 is characterized in that: The solution of the task planning model is simplified by using an evaluation function of weighted product or weighted sum or a combination of the two.

6. The method for lightweight autonomous mission planning and scheduling of dual-satellite formation satellites based on multi-objective optimization according to claim 5 is characterized in that: The evaluation function combining weighted product and weighted sum is formally described as follows: Where η i1 is the weight coefficient of the task priority. The higher the priority, the greater the value assigned. Represents the total time the thruster is turned on. Represents the total time the distance between stars is maintained; is the number of task switching; Represents control accuracy; represents the robustness index, i.e., the tolerance envelope of the system to uncertainty; is the corresponding weight coefficient, which takes a value between 0 and 1, and the sum of the total weight coefficients is 1.

7. A lightweight autonomous mission planning and scheduling system for dual-satellite formation based on multi-objective optimization, characterized in that include: The input determination module uses the binary satellite orbit parameters provided by the ground or the binary satellite orbit information provided by inter-satellite communication to calculate the inter-satellite pointing target angle and the predicted satellite spacing, and uses the calculated values ​​as the input of the mission planning; Model building module, which performs dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, and establishes a mission planning model; The dynamic optimization module uses the calculation results of the input determination module as the input of task planning. According to the constructed task planning model, it uses dynamic optimization to generate attitude / orbit task strategies, that is, to obtain the optimal scheduling plan for the task sequence.

8. The dual-satellite formation satellite lightweight autonomous mission planning and scheduling system based on multi-objective optimization according to claim 7 is characterized in that: Intersatellite pointing target angle θ r , r The calculation formula is: r pvo ＝A OI r pv Normalization pvo =r pvo / ||r pvo || ψ r =arctan2(r pvo [1],r pvo [0]) Among them, x, y, and z are the three-dimensional positions of the star in the inertial system. T ,y T 、z T are the three-dimensional positions of the target star in the inertial system, A OI is the direction cosine matrix of the local orbital system relative to the inertial system; || || is the norm of the matrix; The calculation formula of the star spacing is D r ＝||r pv ||。 9. The dual-satellite formation satellite lightweight autonomous mission planning and scheduling system based on multi-objective optimization according to claim 7 is characterized in that: Perform dual-satellite pointing control in the non-orbit control phase or position control in the orbit control phase, analyze and optimize target characteristics, and establish a mission planning model: where f=g(E1,E2,…,E n ) (1) Formula (1): is the planning optimization objective function, E i represents the total benefit of formation and pointing of satellite i, i∈{1,...,n}, n is the total number of satellites in the formation, and the multiplication weighted method is used to integrate the optimization objectives; Formula (2): Decision variables represents the decision variable of task sequence j corresponding to optimization objective i. If the task is executed, then is 1, otherwise it is 0; Formula (3): Decision variables The corresponding constraint set C includes thruster thrust constraints and satellite attitude constraints; Formula (4): To optimize the execution start time of task sequence k corresponding to target i, w i It is the time zero point of the task sequence corresponding to the optimization target.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images