Small-thrust collision avoidance prediction guidance method with observation configuration constraints

Abstract

This invention discloses a low-thrust collision avoidance prediction and guidance method with observation configuration constraints, belonging to the field of satellite constellation technology. The implementation method is as follows: Considering a group of satellites in an orbital plane of a satellite network, the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage are calculated based on the minimum coverage elevation angle and orbital altitude. According to the constellation configuration maintenance requirements of the orbital plane, the maximum phase deviation of the satellites is given, and the boundary conditions, path constraints, and control constraints for prediction and guidance are determined. A dynamic model of the low-Earth orbit satellite is established. The continuous trajectory optimization problem is discretized into a nonlinear programming problem using the Gaussian pseudospectral method. A sequential quadratic optimization method is used to optimize the burnup-optimal low-thrust collision avoidance trajectory. The burnup-optimal trajectory is used as the waypoint sequence for subsequent prediction and guidance, and a real-time prediction correction algorithm is used to guide the waypoints, reducing the cumulative error of orbit prediction.

Description

Technical Field

[0001] This invention relates to a satellite low-thrust collision avoidance prediction and correction guidance method, and more particularly to a trajectory optimization and prediction guidance method constrained by the observation configuration of a low-Earth orbit satellite constellation, belonging to the field of satellite constellation technology. Background Technology

[0002] In recent years, large satellite constellations such as OneWeb and Starlink have been put into operation. With the increasing congestion of low Earth orbit, collisions with satellites, debris, and other space objects are one of the greatest threats to the safe operation of these constellations. Generally, for large satellite constellations, their remote sensing, communication, and other design functions must have global coverage capabilities, which is usually achieved through a specific observation configuration. However, this observation configuration is difficult to maintain during collision avoidance. Therefore, collision avoidance under observation configuration constraints will be beneficial to the safety and functional realization of large satellite constellations. Currently, most satellite collision avoidance maneuvers are based on pulse thrust, requiring calculation of the pulse timing, thrust magnitude, and thrust direction. Furthermore, the constraints of the constellation's observation configuration are usually not considered. However, as satellite constellations increasingly adopt electric propulsion technology, collision avoidance methods based on low thrust will be more effective in extending the constellation's lifespan. To improve satellite safety, it is necessary to study low-thrust collision avoidance guidance with configuration constraints. Summary of the Invention

[0003] To address the limitations of thrust amplitude and Earth observation configuration constraints during collision avoidance by satellite constellations, this invention aims to provide a low-thrust collision avoidance prediction and guidance method that considers observation configuration constraints. Under low thrust conditions, it achieves collision avoidance maneuvers with continuous Earth coverage. By optimizing the trajectory to the optimal fuel consumption level, waypoints that satisfy multiple complex constraints are obtained. Predictive correction guidance is then performed between waypoints, thereby realizing collision avoidance prediction and correction guidance under observation configuration constraints.

[0004] The objective of this invention is achieved through the following technical solution.

[0005] This invention discloses a low-thrust collision avoidance prediction guidance method with observation configuration constraints. Considering a group of satellites in an orbital plane of a satellite network, the maximum and minimum distances between adjacent satellites in the same orbital plane that allow for continuous Earth coverage are calculated based on the minimum coverage elevation angle and orbital altitude. According to the constellation configuration maintenance requirements of the orbital plane, the maximum phase deviation of the satellites is given, and the boundary conditions, path constraints, and control constraints for prediction guidance are determined. A dynamic model of the low-Earth orbit satellite is also established. To achieve a low-thrust collision avoidance guidance method with path constraints, the Gaussian pseudospectral method is used to discretize the continuous trajectory optimization problem into a nonlinear programming problem. A sequential quadratic optimization method is then used to optimize the fuel-efficient low-thrust collision avoidance trajectory, obtaining the fuel-efficient trajectory. This fuel-efficient trajectory is used as the waypoint sequence for subsequent prediction guidance, and a real-time prediction correction algorithm is employed to guide the waypoints, reducing the cumulative error of orbit prediction and achieving collision avoidance prediction correction guidance under observation configuration constraints.

[0006] The observation configuration-constrained small-thrust collision avoidance prediction guidance method disclosed in this invention includes the following steps:

[0007] Step 1: Consider a group of satellites in an orbital plane of the satellite network. Based on the minimum coverage elevation angle and orbital altitude of the satellites, calculate the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage. Based on the constellation configuration maintenance requirements of the orbital plane, give the maximum phase deviation of the satellites, determine the boundary conditions, path constraints, and control constraints of predictive guidance, and establish a dynamic model of low Earth orbit satellites.

[0008] Step 1.1: Consider a group of satellites in an orbital plane of the satellite network. Based on the minimum coverage elevation angle and orbital altitude of the satellites, calculate the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage. Based on the constellation configuration maintenance requirements of the orbital plane, give the maximum phase deviation of the satellites. Use the distance constraint and phase constraint as path constraints. Based on the collision avoidance mission, give the initial and final state conditions of predictive guidance, and use the thrust amplitude as the control constraint. That is, the boundary conditions, path constraints and control constraints are determined.

[0009] Boundary conditions and control constraints are shown in equations (1) and (2).

[0010]

[0011] T min ≤||u||≤T max (2)

[0012] Where r0, v0, and m0 are the initial position, initial velocity, and initial mass, respectively, and r f and vf These represent the end position and end velocity, respectively, t f It is the final moment, T min and T max These are the minimum and maximum values ​​of the thrust.

[0013] Based on the minimum coverage elevation angle of the satellite, the satellite's observation configuration constraints are transformed into distance and phase constraints between adjacent satellites. The maximum coverage length of a single satellite is shown in equation (3).

[0014] L TS =2θ×R e (3)

[0015] The minimum number of satellites required to achieve continuous coverage in an orbital plane is:

[0016]

[0017] Where θ is the coverage angle of a single satellite, and H and R e These are the orbital altitude and the Earth's radius, respectively. The relationship between θ and the coverage elevation angle σ is:

[0018]

[0019] The maximum distance is calculated using equation (6):

[0020]

[0021] The minimum distance is calculated using equation (7):

[0022]

[0023] Distance constraints and phase constraints are shown in equations (8) and (9).

[0024] s min ≤s≤s max (8)

[0025] p≤p max (9)

[0026] Among them, s min and s max These are minimum and maximum distance constraints, where s is the distance between adjacent satellites, and p... max It is the maximum phase deviation constraint, and p is the orbital phase deviation of the maneuvering satellite.

[0027] Step 1.2: When a low-Earth orbit satellite orbits the Earth, we mainly consider the two-body orbital dynamics, neglecting the effects of Earth's rotation and gravitational perturbations, and establish a two-body dynamics model for the low-Earth orbit satellite.

[0028]

[0029] Where r = [x, y, z] T It is the position vector from the satellite's center of mass to the Earth's center of mass, v = [v x ,v y ,v z ] T It is a velocity vector, g(r,t)=[g x ,g y ,g z ] T It is the acceleration due to gravity on Earth, u = [u x ,u y ,u z ] T It is the satellite thrust vector. It is the thrust amplitude, m is the satellite mass, and a is the thrust amplitude. c =u / m is the control acceleration, I sp It is the specific impulse of the thruster, g E It is the magnitude of gravitational acceleration at sea level on Earth, a d It is the acceleration due to disturbance.

[0030] Step 2: Using the low-Earth orbit satellite dynamics model established in Step 1, considering boundary conditions and multiple constraints, construct a low-thrust trajectory optimization problem. Use the Gaussian pseudospectral method to discretize the continuous trajectory optimization problem into a nonlinear programming problem, and use the sequential quadratic optimization method to optimize the low-thrust collision avoidance trajectory with optimal fuel consumption, obtain the optimal fuel consumption trajectory, and use the optimal fuel consumption trajectory as the waypoint sequence for subsequent prediction guidance.

[0031] The small thrust trajectory optimization problem is described as follows: in the dynamic system equation (10), find the optimal trajectory x(t) and the optimal control u(t) and satisfy the boundary conditions (1), control constraints (2), path constraints (8) and (9) and make the fuel consumption index take the minimum value.

[0032]

[0033]

[0034] The continuous trajectory optimization problem is constructed as shown in equations (11) to (12). The Gaussian pseudospectral method is used to discretize the continuous trajectory optimization problem into a nonlinear programming problem, and the sequential quadratic optimization method is used to optimize the fuel-efficient low-thrust collision avoidance trajectory to obtain the fuel-efficient trajectory. The fuel-efficient trajectory is used as the waypoint sequence for subsequent prediction guidance.

[0035] Step 3: Based on the fuel-optimal trajectory obtained in Step 2, design a multi-constraint, low-thrust collision avoidance prediction and guidance law. This enables collision avoidance maneuvers with continuous ground coverage. Through fuel-optimal trajectory optimization, waypoints satisfying multiple complex constraints are obtained. Predictive correction guidance is performed between waypoints to achieve collision avoidance prediction and correction guidance under observation configuration constraints.

[0036] Let the dynamic model equation (10) be denoted as

[0037]

[0038] At the initial state X(0) = X0, linearizing equation (13) yields:

[0039]

[0040] in Let △X = X - X0, The above equation is transformed into

[0041]

[0042] The solution to this linear system is:

[0043] △X f =Φ△X0 (16)

[0044] in Φ(0)=I 6×6 According to equation (16), we get

[0045]

[0046] The desired maneuver speed is

[0047] △v0=[Φ3Φ1 -1 Φ2-Φ4] -1 ×[Φ3Φ1 -1 △r f -△v f (18)

[0048] Where △r f and △v f To predict the position-velocity vector difference between the target point and the waypoint.

[0049] The low-thrust collision avoidance guidance problem constrained by observation configuration includes the dynamics determined by equation (10), the boundary conditions determined by equation (1), the control constraints determined by equation (2), the path constraints determined by equations (8) and (9), the optimal trajectory determined by equation (11), and the guidance law determined by equation (18). Based on the optimal burnup trajectory obtained in step 2, the waypoints that satisfy various constraints during the guidance process are obtained. A multi-constraint low-thrust collision avoidance prediction guidance law is designed so that the satellite can achieve collision avoidance maneuvers under low thrust and observation configuration constraints.

[0050] Beneficial effects:

[0051] 1. The low-thrust collision avoidance prediction guidance method disclosed in this invention addresses the limitation of low-Earth orbit Earth satellites' electric thrusters being unable to generate large-amplitude thrust pulses. It designs a low-thrust burnout-optimal trajectory and a prediction correction guidance law. The continuous trajectory optimization problem is discretized into a nonlinear programming problem using the Gaussian pseudospectral method, and a sequential quadratic optimization method is employed to optimize the burnout-optimal low-thrust collision avoidance trajectory, obtaining the burnout-optimal trajectory. This trajectory is then used as the waypoint sequence for subsequent prediction guidance. Predictive correction guidance between waypoints enables the satellite to perform avoidance maneuvers under low-thrust constraints.

[0052] 2. The collision avoidance prediction guidance method under observation configuration constraints disclosed in this invention considers a group of satellites in an orbital plane of a satellite network. Based on the minimum coverage elevation angle and orbital altitude of the satellites, the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage are calculated. Based on the constellation configuration maintenance requirements of the orbital plane, the maximum phase deviation of the satellites is given. The distance constraints and phase constraints are used as path constraints to realize the avoidance maneuver of low-Earth orbit satellites under Earth observation configuration constraints. Attached Figure Description

[0053] Figure 1 Flowchart of a prediction guidance method for low-thrust collision avoidance;

[0054] Figure 2 A schematic diagram illustrating the observation coverage capability of a single satellite;

[0055] Figure 3 Satellite three-axis position curves for low-thrust collision avoidance prediction guidance methods;

[0056] Figure 4 Local magnified curves of the satellite's three-axis position for low-thrust collision avoidance prediction guidance methods;

[0057] Figure 5 Fuel consumption curve for predictive guidance process to avoid low-thrust collisions;

[0058] Figure 6Distance constraints and distance variation curves during the predicted guidance process to avoid low-thrust collisions;

[0059] Figure 7 The phase deviation change curve during the prediction guidance process for low-thrust collision avoidance. Detailed Implementation

[0060] To better illustrate the purpose and advantages of the present invention, the invention will be further described below in conjunction with the embodiments and corresponding drawings.

[0061] Example: Multi-constraint collision avoidance guidance problem for low-thrust low-Earth orbit satellites.

[0062] The flowchart of the observation configuration-constrained low-thrust collision avoidance prediction guidance method disclosed in this embodiment is attached. Figure 1 As shown, the specific implementation steps are as follows:

[0063] Step 1: Consider a group of satellites in an orbital plane of the satellite network. Based on the minimum coverage elevation angle and orbital altitude of the satellites, calculate the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage. Based on the constellation configuration maintenance requirements of the orbital plane, give the maximum phase deviation of the satellites, determine the boundary conditions, path constraints, and control constraints of predictive guidance, and establish a dynamic model of low Earth orbit satellites.

[0064] Step 1.1: Consider a group of satellites in an orbital plane of the satellite network. Based on the minimum coverage elevation angle and orbital altitude of the satellites, calculate the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage. Based on the constellation configuration maintenance requirements of the orbital plane, give the maximum phase deviation of the satellites. Use the distance constraint and phase constraint as path constraints. Based on the collision avoidance mission, give the initial and final state conditions of predictive guidance, and use the thrust amplitude as the control constraint. That is, the boundary conditions, path constraints and control constraints are determined.

[0065] Boundary conditions and control constraints are shown in equations (19) and (20).

[0066]

[0067] T min ≤||u||≤T max (20)

[0068] Where r0 = [7378145,0,0]m, v0 = [0,7350,0] and m0 = 500kg are the initial position, initial velocity and initial mass, respectively, and r f = [0, 7381145, 0]m and v f = [-7350,0,0] m / s represents the terminal position and terminal velocity, respectively, tf It is the final moment, T min =0N and T max =0.3N represents the minimum and maximum thrust values.

[0069] Based on the satellite's minimum coverage elevation angle, the satellite's observation configuration constraints are transformed into distance and phase constraints between adjacent satellites. The maximum coverage length of a single satellite is given by equation (21).

[0070] L TS =2θ×R e (twenty one)

[0071] The minimum number of satellites required to achieve continuous coverage in an orbital plane is:

[0072]

[0073] Where θ is the coverage angle of a single satellite, and H and R e These are the orbital altitude and the Earth's radius, respectively. The relationship between θ and the coverage elevation angle σ is:

[0074]

[0075] The specific coverage capabilities of a single satellite are shown in the attached figure. Figure 2 As shown.

[0076] The maximum distance is calculated using equation (24):

[0077]

[0078] The minimum distance is calculated using equation (25):

[0079]

[0080] Taking an orbital plane with an altitude H = 1000km and 12 satellites as the implementation case, when the minimum coverage elevation angle σ min When the angle is 20°, the minimum distance constraint s can be calculated. min =3689187m, maximum distance constraint s max =4037273m, maximum phase deviation p max =0.1°. Therefore, there is a path constraint:

[0081] s min ≤s≤s max (26)

[0082] p≤p max (27)

[0083] Step 1.2: When a low-Earth orbit satellite orbits the Earth, we mainly consider the two-body orbital dynamics, neglecting the effects of Earth's rotation and gravitational perturbations, and establish a two-body dynamics model for the low-Earth orbit satellite.

[0084]

[0085] Where r = [x, y, z] T It is the position vector from the satellite's center of mass to the Earth's center of mass, v = [v x ,v y ,v z ] T It is a velocity vector, g(r,t)=[g x ,g y ,g z ] T It is the acceleration due to gravity on Earth, u = [u x ,u y ,u z ] T It is the satellite thrust vector. It is the thrust amplitude, m is the satellite mass, and a is the thrust amplitude. c =u / m is the control acceleration, I sp =3800s is the thruster specific impulse, g E = 9.80665m / s 2 It is the magnitude of gravitational acceleration at sea level on Earth, a d It is the acceleration due to disturbance.

[0086] Step 2: Using the low-Earth orbit satellite dynamics model established in Step 1, considering boundary conditions and multiple constraints, construct a low-thrust trajectory optimization problem. Use the Gaussian pseudospectral method to discretize the continuous trajectory optimization problem into a nonlinear programming problem, and use the sequential quadratic optimization method to optimize the low-thrust collision avoidance trajectory with optimal fuel consumption, obtain the optimal fuel consumption trajectory, and use the optimal fuel consumption trajectory as the waypoint sequence for subsequent prediction guidance.

[0087] The small thrust trajectory optimization problem is described as follows: in the dynamic system equation (28), find the optimal trajectory x(t) and the optimal control u(t) that satisfy the boundary conditions (19), control constraints (20), path constraints (26) and (27), and minimize the fuel consumption index.

[0088]

[0089]

[0090] The continuous trajectory optimization problem is constructed as shown in equations (29) to (30). The Gaussian pseudospectral method is used to discretize the continuous trajectory optimization problem into a nonlinear programming problem, and the sequential quadratic optimization method is used to optimize the fuel-efficient low-thrust collision avoidance trajectory to obtain the fuel-efficient trajectory. The fuel-efficient trajectory is used as the waypoint sequence for subsequent prediction guidance.

[0091] Step 3: Based on the fuel-optimal trajectory obtained in Step 2, design a multi-constraint, low-thrust collision avoidance prediction and guidance law. This enables collision avoidance maneuvers with continuous ground coverage. Through fuel-optimal trajectory optimization, waypoints satisfying multiple complex constraints are obtained. Predictive correction guidance is performed between waypoints to achieve collision avoidance prediction and correction guidance under observation configuration constraints.

[0092] Let the dynamic model equation (28) be denoted as

[0093]

[0094] At the initial state X(0) = X0, linearizing equation (31) yields:

[0095]

[0096] in Let △X = X - X0, The above equation is transformed into

[0097]

[0098] The solution to this system is

[0099] △X f =Φ△X0 (34)

[0100] in Φ(0)=I 6×6 According to equation (34), we get

[0101]

[0102] The desired maneuver speed is

[0103] △v0=[Φ3Φ1 -1 Φ2-Φ4] -1 ×[Φ3Φ1 -1 △r f -△v f (36)

[0104] Where △r f and △v f To predict the position-velocity vector difference between the target point and the waypoint.

[0105] In this embodiment, the main parameters are shown in Table 1.

[0106] Table 1 Main parameters of the implementation case

[0107]

[0108] Figure 3 Three-dimensional trajectory diagram for predictive guidance to avoid small-thrust collisions. Figure 4 This is a magnified view of a portion of the 3D trajectory. Figure 5 This is a fuel consumption curve. Figure 6 This is a graph showing the relationship between distance variation and distance constraints. Figure 7 This is a graph showing the phase deviation change.

[0109] The low-thrust collision avoidance guidance problem constrained by observation configuration includes the dynamics determined by equation (28), the optimal trajectory determined by equation (29), and the guidance law determined by equation (36). By optimizing the optimal trajectory with the lowest burnup, the waypoints that satisfy all constraints during the guidance process are obtained. Based on this, a predictive guidance algorithm is designed so that the satellite can achieve collision avoidance maneuvers under low thrust and observation configuration constraints.

[0110] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A small-thrust collision avoidance prediction guidance method constrained by observation configuration, characterized in that: Includes the following steps, Step 1: Consider a group of satellites in an orbital plane of the satellite network. Based on the minimum coverage elevation angle and orbital altitude of the satellites, calculate the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage. Based on the constellation configuration maintenance requirements of the orbital plane, give the maximum phase deviation of the satellites, determine the boundary conditions, path constraints, and control constraints of predictive guidance, and establish a dynamic model of low Earth orbit satellites. Step 1 is implemented as follows: Step 1.1: Consider a group of satellites in an orbital plane of the satellite network. Based on the minimum coverage elevation angle and orbital altitude of the satellites, calculate the maximum and minimum distances between adjacent satellites in the same orbital plane that can satisfy continuous Earth coverage. Based on the constellation configuration maintenance requirements of the orbital plane, give the maximum phase deviation of the satellites. Use the range constraint and phase constraint as path constraints. Based on the collision avoidance mission, give the initial and final state conditions of predictive guidance, and use the thrust amplitude as the control constraint. That is, the boundary conditions, path constraints and control constraints are determined. Boundary conditions and control constraints are shown in equations (1) and (2). in, , and These represent the initial position, initial velocity, and initial mass, respectively. and These are the end position and end velocity, respectively. It is the final moment. and These are the minimum and maximum values ​​of the thrust; Based on the minimum coverage elevation angle of the satellite, the satellite's observation configuration constraints are transformed into distance and phase constraints between adjacent satellites; the maximum coverage length of a single satellite is shown in equation (3). The minimum number of satellites required to achieve continuous coverage in an orbital plane is: in, It is the coverage angle of a single satellite. and These are orbital altitude and Earth radius, respectively. With coverage elevation angle The relationship between them is: The maximum distance is calculated using equation (6): The minimum distance is calculated using equation (7): Distance constraints and phase constraints are shown in equations (8) and (9). in, and These are minimum and maximum distance constraints. It is the distance between adjacent satellites. It is the maximum phase deviation constraint. It is the orbital phase deviation of the maneuvering satellite; Step 1.2: When a low-Earth orbit satellite orbits the Earth, we mainly consider the two-body orbital dynamics, neglecting the effects of Earth's rotation and gravitational perturbations, and establish a two-body dynamics model for the low-Earth orbit satellite. in, It is the position vector from the satellite's center of mass to the Earth's center of mass. It is a velocity vector. It is the acceleration due to Earth's gravity. It is the satellite thrust vector. It is the thrust amplitude. It's about satellite quality. It controls acceleration. It is the specific impulse of the thruster. It is the magnitude of gravitational acceleration at sea level on Earth. It is the acceleration due to disturbance; Step 2: Using the low-Earth orbit satellite dynamics model established in Step 1, considering boundary conditions and multiple constraints, construct a low-thrust trajectory optimization problem. Use the Gaussian pseudospectral method to discretize the continuous trajectory optimization problem into a nonlinear programming problem, and use the sequential quadratic optimization method to optimize the low-thrust collision avoidance trajectory with optimal fuel consumption, obtain the optimal fuel consumption trajectory, and use the optimal fuel consumption trajectory as the waypoint sequence for subsequent prediction guidance. Step 2 is implemented as follows: The small thrust trajectory optimization problem is described as follows: in the dynamic system equation (10), find the optimal trajectory. With optimal control And satisfy the boundary conditions (1), control constraints (2), path constraints (8) and (9), and make the fuel consumption index take the minimum value; The continuous trajectory optimization problem is constructed as shown in equations (11) to (12); the Gaussian pseudospectral method is used to discretize the continuous trajectory optimization problem into a nonlinear programming problem, and the sequential quadratic optimization method is used to optimize the fuel-efficient low-thrust collision avoidance trajectory to obtain the fuel-efficient trajectory; the fuel-efficient trajectory is used as the waypoint sequence for subsequent prediction guidance; Step 3: Based on the optimal fuel consumption trajectory obtained in Step 2, design a multi-constraint low-thrust collision avoidance prediction guidance law; realize collision avoidance maneuvers with continuous ground coverage; through optimization of the optimal fuel consumption trajectory, obtain waypoints that satisfy multiple complex constraints; perform prediction correction guidance between waypoints to realize collision avoidance prediction correction guidance under observation configuration constraints. Step 3 is implemented as follows: Let the dynamic model equation (10) be denoted as In the initial state Linearizing equation (13) at that point, we have: in ,make , The above equation is transformed into The solution to this linear system is: in , According to equation (16), we get The desired maneuver speed is in and To predict the position-velocity vector difference between the target point and the waypoint; The problem of low-thrust collision avoidance guidance with observation configuration constraints includes the following formulas: (10) The dynamics determined by equation (1), the boundary conditions determined by equation (2), the control constraints determined by equation (8) and (9), the optimal trajectory determined by equation (11) and the guidance law determined by equation (18) are used to obtain the waypoints that satisfy various constraints during the guidance process based on the optimal burnup trajectory planned in step 2. The multi-constraint low-thrust collision avoidance prediction guidance law is designed so that the satellite can achieve collision avoidance maneuvers under the constraints of low thrust and observation configuration.

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