Rocket sub-level recovery online trajectory planning method considering attack angle constraint

Abstract

The invention discloses a rocket sub-level recovery online trajectory planning method considering attack angle constraints, and solves the problems of trajectory safety boundary failure, pneumatic overload overrun and flight stability risk caused by neglecting the attack angle constraints in an existing convex optimization trajectory planning method. According to the method, an attack angle safety boundary constraint model is established, nonlinear attack angle constraint is converted into a second-order cone programming problem (SOCP), and a strict attack angle safety boundary of a planning track is ensured by combining sequence convex optimization with an adaptive trust region adjustment mechanism for solving. According to the method, the defect that the trajectory is not feasible due to the fact that the attack angle is not restrained in a traditional convex optimization method is effectively avoided, and the engineering adaptability of the trajectory to a guidance system and attitude control and the safety of a recovery task are remarkably improved while the sub-problem feasibility and the optimal solution interpretability are guaranteed.

Description

Technical Field

[0001] This invention relates to an online trajectory planning method for rocket stage recovery considering angle-of-attack constraints, belonging to the field of aircraft guidance and control technology. Background Technology

[0002] Rocket stage recovery technology is a crucial step in achieving rocket reusability and reducing launch costs. When recovering stages in an atmospheric environment, precise flight trajectory planning is essential to meet multiple constraints, including landing position, velocity, aerodynamic loads, and structural safety. Existing convex optimization trajectory planning methods are techniques for generating motion trajectories that satisfy initial and terminal conditions in real time, and are widely used in online trajectory planning scenarios. However, most existing convex optimization methods only consider constraints on state variables such as position, velocity, and thrust direction during the planning process, neglecting the angle of attack constraint. The angle of attack is a core parameter affecting rocket aerodynamic performance, structural loads, and flight stability. An excessively large angle of attack can lead to aerodynamic overload, structural damage, instability, or control failure, threatening recovery safety. Ignoring the angle of attack constraint results in an infeasible trajectory, causing recovery failure or damage. Existing convex optimization methods do not incorporate this constraint, making it impossible to avoid risks and causing the planning results to deviate from reality, failing to meet the requirements of high precision and high safety. Therefore, there is an urgent need for an online trajectory planning method for rocket stage recovery that considers angle-of-attack constraints in an atmospheric environment, so as to ensure that the trajectory planning meets the basic requirements such as position and velocity while strictly adhering to the angle-of-attack safety boundary, thereby significantly improving the reliability of the recovery mission. Summary of the Invention

[0003] The technical problem solved by this invention is to overcome the shortcomings of the prior art and provide an online trajectory planning method for rocket stage recovery that considers angle-of-attack constraints. This solves the problems of trajectory infeasibility, excessive aerodynamic overload, and flight stability risks caused by neglecting angle-of-attack constraints in existing convex optimization trajectory planning methods, thereby achieving safe and accurate rocket stage recovery.

[0004] The technical solution of this invention is: Firstly, a method for online trajectory planning of rocket stage recovery considering angle-of-attack constraints, comprising: Based on the target point, current flight status, and atmospheric environment parameters, a baseline trajectory planning model without angle-of-attack constraints is established; the baseline trajectory planning model is solved using a sequential convex optimization method, and the initial reference trajectory is output. Using the position, velocity, mass, and thrust profile of the initial reference trajectory, the initial parameters of the angle-of-attack constraint optimization model are generated by interpolation. Input speed variation trust region Preset trust region adjustment coefficient The initial parameters of the angle-of-attack constraint optimization model are obtained, and the angle-of-attack safety boundary is embedded into the optimization constraint to generate a trajectory optimization model with angle-of-attack constraint. The sequential convex optimization method is used to solve the trajectory optimization problem with angle of attack constraints. The optimized trajectory is dynamically solved, and the trust region range is adjusted during the iteration process until the convergence condition is met. Generate the final planned trajectory that satisfies the angle-of-attack safety boundary constraints.

[0005] Furthermore, initial parameters are obtained by interpolating the position, velocity, mass, and thrust profiles of the reference trajectory. These initial parameters include:

[0006]

[0007] in,

[0008] in, The total flight time represents the reference trajectory; A sequence of reference trajectory positions, velocities, and thrust-mass profiles; Represents the reference trajectory time series; The time interval between solving the baseline trajectory planning model problem without angle-of-attack constraints and the trajectory optimization model problem with angle-of-attack constraints; The time series of initial parameters for the angle-of-attack constraint optimization model; The initial parameters of the angle-of-attack constrained optimization model include the sequence of position, velocity, and thrust-mass profiles. The sequence number is the sub-model number of the sequential programming model; The index of the element in the reference trajectory profile sequence; The index of the element in the sequence of initial parameters in the angle-of-attack constraint optimization model; The number of elements in the reference trajectory profile sequence.

[0009] Furthermore, the angle-of-attack constraint optimization model is as follows: ;in, For thrust vector, It is a velocity vector. For angle of attack constraints, For time.

[0010] Furthermore, the linearized and discretized angle-of-attack constraint optimization model is as follows:

[0011] in, thrust vector The slack variables have a second-order cone. , For the angle of attack constraint relaxation variable, , For the velocity and thrust vector profile of the previous subproblem, This is the sequence number of the sub-model for sequential programming.

[0012] Furthermore, the trust region of the angle-of-attack constrained optimization model is ; For the velocity variation trust region, The velocity sequence of the previous subproblem.

[0013] Furthermore, the method of solving the trajectory optimization problem with angle-of-attack constraints using sequential convex optimization includes:

[0014]

[0015] in, Preset trust region adjustment coefficient, Lower bound of the trust region for velocity change Upper limit of the trust region for velocity change The previous subproblem was the velocity sequence. For velocity variation, the trust region is defined.

[0016] Secondly, a rocket stage recovery online trajectory planning system considering angle-of-attack constraints, characterized in that it includes: The first module establishes a baseline trajectory planning model without angle-of-attack constraints based on the target point, current flight status, and atmospheric environment parameters; it then solves the baseline trajectory planning model using a sequential convex optimization method to output the initial reference trajectory. The second module uses the position, velocity, mass, and thrust profile of the initial reference trajectory to generate the initial parameters of the angle-of-attack constraint optimization model through interpolation methods. The third module, the trust region for input speed variation. Preset trust region adjustment coefficient The initial parameters of the angle-of-attack constraint optimization model are obtained, and the angle-of-attack safety boundary is embedded into the optimization constraint to generate a trajectory optimization model with angle-of-attack constraint. The fourth module uses sequential convex optimization to solve the trajectory optimization problem with angle of attack constraints. It dynamically solves the optimized trajectory and adjusts the trust region range during the iteration process until the convergence condition is met. The fifth module generates the final planned trajectory that satisfies the angle-of-attack safety boundary constraints.

[0017] Furthermore, the initial parameters are obtained by interpolating the position, velocity, mass, and thrust profile of the reference trajectory. These initial parameters include:

[0018]

[0019] in,

[0020] in, The total flight time represents the reference trajectory; A sequence of reference trajectory positions, velocities, and thrust-mass profiles; Represents the reference trajectory time series; The time interval between solving the baseline trajectory planning model problem without angle-of-attack constraints and the trajectory optimization model problem with angle-of-attack constraints; The time series of initial parameters for the angle-of-attack constraint optimization model; The initial parameters of the angle-of-attack constrained optimization model include the sequence of position, velocity, and thrust-mass profiles. The sequence number is the sub-model number of the sequential programming model; The index of the element in the reference trajectory profile sequence; The index of the element in the sequence of initial parameters in the angle-of-attack constraint optimization model; The number of elements in the reference trajectory profile sequence.

[0021] Furthermore, the angle-of-attack constraint optimization model is as follows: ;in, For thrust vector, It is a velocity vector. For angle of attack constraints, For time.

[0022] Furthermore, the linearized and discretized angle-of-attack constraint optimization model is as follows:

[0023] in, thrust vector The slack variables have a second-order cone. , For the angle of attack constraint relaxation variable, , For the velocity and thrust vector profile of the previous subproblem, The sequence number is the sub-model number of the sequential programming model; The trust region of the angle-of-attack constraint optimization model is ; For the velocity variation trust region, The velocity sequence of the previous subproblem; The method of solving the trajectory optimization problem with angle-of-attack constraints using sequential convex optimization includes:

[0024]

[0025] in, Preset trust region adjustment coefficient, Lower bound of the trust region for velocity change Upper limit of the trust region for velocity change The previous subproblem was the velocity sequence. For velocity variation, the trust region is defined.

[0026] Thirdly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the online trajectory planning method for rocket stage recovery considering angle-of-attack constraints.

[0027] Fourthly, an online trajectory planning device for rocket stage recovery considering angle-of-attack constraints includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the online trajectory planning method for rocket stage recovery considering angle-of-attack constraints.

[0028] The advantages of this invention compared to the prior art are: (1) The present invention uses a model that ignores the angle of attack constraint to solve the initial reference trajectory in order to avoid the problem of no solution caused by excessive nonlinearity of the angle of attack.

[0029] (2) This invention incorporates angle of attack constraints into the SOCP optimization model to avoid risks of aerodynamic overload, structural damage and flight instability, and ensure that the planned trajectory meets the feasibility of atmospheric environmental engineering.

[0030] (3) The present invention dynamically ensures that the angle of attack constraint is strictly satisfied in the iterative solution by adaptively adjusting the radius of the trust region; the adaptive adjustment of the radius of the trust region ensures that the angle of attack constraint is strictly satisfied in the iteration. Attached Figure Description

[0031] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram illustrating an online trajectory planning method for rocket stage recovery that considers angle-of-attack constraints in an atmospheric environment. Detailed Implementation

[0032] To better understand the above technical solutions, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solutions of the present invention, rather than limitations on the technical solutions of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0033] The following description, in conjunction with the accompanying drawings, provides a more detailed explanation of an online trajectory planning method for rocket stage recovery considering angle-of-attack constraints, as provided in the embodiments of the present invention. Figure 1 Specific implementation methods may include: Based on the target point, current flight status, and atmospheric environment parameters, a baseline trajectory planning model without angle-of-attack constraints is established; the baseline trajectory planning model is solved using a sequential convex optimization method, and the initial reference trajectory is output. Using the position, velocity, mass, and thrust profile of the initial reference trajectory, the initial parameters of the angle-of-attack constraint optimization model are generated by interpolation. Input speed variation trust region Preset trust region adjustment coefficient The initial parameters of the angle-of-attack constraint optimization model are obtained, and the angle-of-attack safety boundary is embedded into the optimization constraint to generate a trajectory optimization model with angle-of-attack constraint. The sequential convex optimization method is used to solve the trajectory optimization problem with angle of attack constraints. The optimized trajectory is dynamically solved, and the trust region range is adjusted during the iteration process until the convergence condition is met. Generate the final planned trajectory that satisfies the angle-of-attack safety boundary constraints.

[0034] In the solution provided by the embodiments of the present invention, a method for online trajectory planning of rocket stage recovery considering angle-of-attack constraints in an atmospheric environment includes the following steps: 1) Based on the target point, current flight status, and atmospheric environment parameters, establish a baseline trajectory planning model without angle-of-attack constraints; solve the baseline trajectory planning model using a sequential convex optimization method and output the initial reference trajectory; 2) Using the position, velocity, mass, and thrust profile of the initial reference trajectory, the initial parameters of the angle-of-attack constraint optimization model are generated through interpolation. 3) Input speed variation trust region Preset trust region adjustment coefficient The initial parameters of the angle-of-attack constraint optimization model are obtained, and the angle-of-attack safety boundary is embedded into the optimization constraint to generate a trajectory optimization model with angle-of-attack constraint. 4) Solve the trajectory optimization problem with angle of attack constraints using the sequential convex optimization method. Dynamically solve the optimized trajectory and adjust the trust region range during the iteration process until the convergence condition is met. 5) Generate the final planned trajectory that satisfies the angle-of-attack safety boundary constraints.

[0035] This invention effectively avoids aerodynamic overload risks by embedding angle-of-attack constraints and a dynamic adjustment mechanism for the trust region, significantly improving the trajectory feasibility and safety of rocket stage recovery in atmospheric environments.

[0036] 1. The method for online trajectory planning of rocket stage recovery under atmospheric conditions considering angle-of-attack constraints, as described in claim 1, is characterized in that, in step 2), the model is initialized based on a reference trajectory. Initial parameters are obtained by interpolating the position, velocity, mass, and thrust profile of the reference trajectory.

[0037]

[0038]

[0039] in,

[0040] in, The total flight time represents the reference trajectory; A sequence of reference trajectory positions, velocities, and thrust-mass profiles; Represents the reference trajectory time series; The time interval between solving the baseline trajectory planning model problem without angle-of-attack constraints and the trajectory optimization model problem with angle-of-attack constraints; The time series of initial parameters for the angle-of-attack constraint optimization model; The initial parameters of the angle-of-attack constrained optimization model include the sequence of position, velocity, and thrust-mass profiles. The sequence number is the sub-model number of the sequential programming model; The index of the element in the sequence of initial parameters in the angle-of-attack constraint optimization model; The index of the element in the reference trajectory profile sequence; The number of elements in the reference trajectory profile sequence.

[0041] 2. The method for online trajectory planning of rocket stage recovery under atmospheric conditions considering angle-of-attack constraints as described in claim 1, characterized in that, in step 3), the angle-of-attack constraint model and the trust region model are used.

[0042] (1) Angle of attack constraint is expressed as the vector angle between the velocity and thrust directions:

[0043] in: It is the thrust vector; It is a velocity vector; For angle of attack constraints.

[0044] (2) Linearize and discretize the model

[0045] in: thrust vector The slack variables have a second-order cone. ; For the angle of attack constraint relaxation variable; , This is the velocity and thrust vector profile of the previous subproblem; This is the sequence number of the sub-model for sequential programming.

[0046] (2) Trust Domain Considering the strong nonlinearity caused by velocity variations between subproblems, a trust region constraint is added to limit the range of velocity variations:

[0047] in: For the velocity variation trust region; The velocity sequence of the previous subproblem; This is the sequence number of the sub-model for sequential programming.

[0048] 3. The method for online trajectory planning of rocket stage recovery considering angle-of-attack constraints under atmospheric conditions according to claim 1, characterized in that, in step 4), the solution results of the sequential convex neutron problem are updated. .

[0049]

[0050]

[0051] in: Preset trust region adjustment coefficient; Lower bound of the trust region for velocity variation; Upper limit of the reliability region for velocity variation; The velocity sequence of the previous subproblem; This is the sequence number of the sub-model for sequential programming.

[0052] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0053] The original problem of rocket-powered landing and descent considering nonlinear aerodynamics can be described as follows:

[0054] in: For quality; End time; Position vector, containing three directions: x, y, and z. , , For the elements therein; Velocity vector; : Gravitational acceleration vector; : Inference vector; Flight time; Aerodynamic drag coefficient; : Aircraft reference area; Atmospheric density; Standard atmospheric density; Atmospheric density calculation coefficient; : Fuel consumption calculation parameters; Initial position vector; Initial velocity vector; Initial mass; Terminal thrust magnitude; Dry weight; : Lower limit of thrust constraint; Thrust constraint upper limit; := Thrust unit vector; Thrust direction constraint; Trajectory constraints; Speed ​​magnitude constraint.

[0055] If angle of attack is considered

[0056] The discretized solution problem, specifically the locally linear approximation subproblems, can be described in the standard form of the SOCP problem:

[0057] Among them, the discretization result of the angle of attack constraint is

[0058]

[0059] In this embodiment, the above problem can be solved by transforming sequential convexity into a series of locally linear approximate subproblems. The method used in this invention can be divided into the following five steps: 1) Based on the target point, current flight status, and atmospheric environment parameters, establish a baseline trajectory planning model without angle-of-attack constraints; solve the baseline trajectory planning model using a sequential convex optimization method and output the initial reference trajectory; 2) Using the position, velocity, mass, and thrust profile of the initial reference trajectory, the initial parameters of the angle-of-attack constraint optimization model are generated through interpolation.

[0060]

[0061] in,

[0062] in, The total flight time represents the reference trajectory; A sequence of reference trajectory positions, velocities, and thrust-mass profiles; Represents the reference trajectory time series; The time interval between solving the baseline trajectory planning model problem without angle-of-attack constraints and the trajectory optimization model problem with angle-of-attack constraints; The time series of initial parameters for the angle-of-attack constraint optimization model; The initial parameters of the angle-of-attack constrained optimization model include the sequence of position, velocity, and thrust-mass profiles. The sequence number is the sub-model number of the sequential programming model; The index of the element in the sequence of initial parameters in the angle-of-attack constraint optimization model; The index of the element in the reference trajectory profile sequence; The number of elements in the reference trajectory profile sequence.

[0063] 3) Input speed variation trust region Preset trust region adjustment coefficient The model is initialized with angle of attack constraints, and the angle of attack safety boundary is embedded into the optimization constraints to generate a trajectory optimization model with angle of attack constraints.

[0064]

[0065] in: thrust vector The slack variables have a second-order cone. ; For the angle of attack constraint relaxation variable; , This is the velocity and thrust vector profile of the previous subproblem; For the velocity variation trust region; 4) Solve the trajectory optimization problem with angle of attack constraints using the sequential convex optimization method. Dynamically solve the optimized trajectory and adjust the trust region range during the iteration process until the convergence condition is met.

[0066]

[0067] in: Preset trust region adjustment coefficient; Lower bound of the trust region for velocity variation; Upper limit of the reliability region for velocity variation; 5) Generate the final planned trajectory that satisfies the angle-of-attack safety boundary constraints.

[0068] This invention provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform... Figure 1 The method described.

[0069] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0070] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0074] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A method for online trajectory planning of a rocket substage recovery considering angle of attack constraints, characterized in that, include: Based on the target point, current flight status, and atmospheric environment parameters, a baseline trajectory planning model without angle-of-attack constraints is established; the baseline trajectory planning model is solved using a sequential convex optimization method, and the initial reference trajectory is output. Using the position, velocity, mass, and thrust profile of the initial reference trajectory, the initial parameters of the angle-of-attack constraint optimization model are generated by interpolation. Input speed change reliability domain , preset reliability domain adjustment coefficient and the initial parameters of the angle of attack constraint optimization model, the angle of attack safety boundary is embedded in the optimization constraint, and the trajectory optimization model with the angle of attack constraint is generated. The sequential convex optimization method is used to solve the trajectory optimization problem with angle of attack constraints. The optimized trajectory is dynamically solved, and the trust region range is adjusted during the iteration process until the convergence condition is met. Generate the final planned trajectory that satisfies the angle-of-attack safety boundary constraints.

2. The method for online trajectory planning of rocket stage recovery considering angle-of-attack constraints according to claim 1, characterized in that, The initial parameters are obtained by interpolating the position, velocity, mass, and thrust profiles of the reference trajectory. These initial parameters include: in, in, The total flight time represents the reference trajectory; A sequence of reference trajectory positions, velocities, and thrust-mass profiles; Represents the reference trajectory time series; The time interval between solving the baseline trajectory planning model problem without angle-of-attack constraints and the trajectory optimization model problem with angle-of-attack constraints; The time series of initial parameters for the angle-of-attack constraint optimization model; The initial parameters of the angle-of-attack constrained optimization model include the sequence of position, velocity, and thrust-mass profiles. The sequence number is the sub-model number of the sequential programming model; The index of the element in the reference trajectory profile sequence; The index of the element in the sequence of initial parameters in the angle-of-attack constraint optimization model; The number of elements in the reference trajectory profile sequence.

3. The method for online trajectory planning of rocket stage recovery considering angle-of-attack constraints according to claim 1, characterized in that, The angle-of-attack constraint optimization model is: ;in, For thrust vector, It is a velocity vector. For angle of attack constraints, For time; The linearized and discretized angle-of-attack constraint optimization model is as follows: in, thrust vector The slack variables have a second-order cone. , For the angle of attack constraint relaxation variable, , For the velocity and thrust vector profile of the previous subproblem, This is the sequence number of the sub-model for sequential programming.

4. The method for online trajectory planning of rocket stage recovery considering angle-of-attack constraints according to claim 1, characterized in that, The trust region of the angle-of-attack constraint optimization model is ; For the velocity change trust region, The velocity sequence of the previous subproblem.

5. The method for online trajectory planning of rocket stage recovery considering angle-of-attack constraints according to claim 1, characterized in that, The method of solving the trajectory optimization problem with angle-of-attack constraints using sequential convex optimization includes: in, Preset trust region adjustment coefficient, Lower bound of the confidence region for velocity change Upper limit of the trust region for velocity change The previous subproblem was the velocity sequence. For velocity variation, the trust region is defined.

6. An online trajectory planning system for rocket stage recovery considering angle-of-attack constraints, characterized in that, include: The first module establishes a baseline trajectory planning model without angle-of-attack constraints based on the target point, current flight status, and atmospheric environment parameters; it then solves the baseline trajectory planning model using a sequential convex optimization method to output the initial reference trajectory. The second module uses the position, velocity, mass, and thrust profile of the initial reference trajectory to generate the initial parameters of the angle-of-attack constraint optimization model through interpolation methods. The third module, the trust region for input speed variation. Preset trust region adjustment coefficient The initial parameters of the angle-of-attack constraint optimization model are obtained, and the angle-of-attack safety boundary is embedded into the optimization constraint to generate a trajectory optimization model with angle-of-attack constraint. The fourth module uses sequential convex optimization to solve the trajectory optimization problem with angle of attack constraints. It dynamically solves the optimized trajectory and adjusts the trust region range during the iteration process until the convergence condition is met. The fifth module generates the final planned trajectory that satisfies the angle-of-attack safety boundary constraints.

7. The online trajectory planning system for rocket stage recovery considering angle-of-attack constraints according to claim 5, characterized in that, The initial parameters are obtained by interpolating the position, velocity, mass, and thrust profiles of the reference trajectory. These initial parameters include: in, in, The total flight time represents the reference trajectory; A sequence of reference trajectory positions, velocities, and thrust-mass profiles; Represents the reference trajectory time series; The time interval between solving the baseline trajectory planning model problem without angle-of-attack constraints and the trajectory optimization model problem with angle-of-attack constraints; The time series of initial parameters for the angle-of-attack constraint optimization model; The initial parameters of the angle-of-attack constrained optimization model include the sequence of position, velocity, and thrust-mass profiles. The sequence number is the sub-model number of the sequential programming model; The index of the element in the reference trajectory profile sequence; The index of the element in the sequence of initial parameters in the angle-of-attack constraint optimization model; The number of elements in the reference trajectory profile sequence.

8. The online trajectory planning system for rocket stage recovery considering angle-of-attack constraints according to claim 5, characterized in that, The angle-of-attack constraint optimization model is: ;in, For thrust vector, It is a velocity vector. For angle of attack constraints, For time; The linearized and discretized angle-of-attack constraint optimization model is as follows: in, thrust vector The slack variables have a second-order cone. , For the angle of attack constraint relaxation variable, , For the velocity and thrust vector profile of the previous subproblem, The sequence number is the sub-model number of the sequential programming model; The trust region of the angle-of-attack constraint optimization model is ; For the velocity change trust region, The velocity sequence of the previous subproblem; The method of solving the trajectory optimization problem with angle-of-attack constraints using sequential convex optimization includes: in, Preset trust region adjustment coefficient, Lower bound of the confidence region for velocity change Upper limit of the trust region for velocity change The previous subproblem was the velocity sequence. For velocity variation, the trust region is defined.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

10. A rocket stage recovery online trajectory planning device considering angle-of-attack constraints, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

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