Intelligent planning method for first-sublevel parachute control recovery of carrier rocket under highly redundant condition

Abstract

The invention relates to the technical field of flight control, in particular to an intelligent planning method for one-sublevel parachute control recovery of a carrier rocket under the condition of high redundancy. Establishing a mass point model of a sublevel system of the carrier rocket based on an inertial system in the homing process; determining kinetic parameters of the carrier rocket recovery system in combination with a wind field coordinate system and the mass point model; the homing process is divided into a high-altitude homing section, a circling height cutting section, a regression section, an energy management section and an upwind landing section according to flight characteristics of a sublevel system of the carrier rocket under the condition of height redundancy; obtaining the geometrical relationship between the position and the length of the track of the first sub-level system in each stage, and determining the initial homing track of the first sub-level system of the carrier rocket; and establishing a target function and updating the position of a sublevel system of the carrier rocket by adopting a heuristic particle swarm optimization algorithm. The five-segment homing algorithm provided by the invention can effectively reduce energy loss under the condition of high redundancy.

Description

Technical Field

[0001] The present invention relates to the field of flight control technology, and in particular to an intelligent planning method for parachute-controlled recovery of a launch vehicle sub-stage under highly redundant conditions. Background Art

[0002] In recent years, my country's aerospace industry has entered a stage of rapid development. However, with the gradual expansion of the scale of aerospace activities and the increasing frequency of launch missions, the uncontrolled fall of carrier rockets has become a safety issue that cannot be ignored. In order to prevent the first-stage debris of carrier rockets from falling into densely populated areas, my country invests a lot of money every year in safety analysis of landing areas and evacuation of the masses. However, as the scope of human activities gradually expands, the safety hazards of this method that relies entirely on manpower are becoming more obvious. Therefore, it is urgent to achieve the controlled recovery of the first-stage of carrier rockets.

[0003] The parachute-controlled recovery technology using a parafoil as an actuator is one of the most promising methods for the controllable recovery of a launch vehicle stage. It can not only meet the recovery requirements of both solid rockets and liquid rockets, but also has flexible flight and high safety. In fact, the primary method to improve landing area accuracy is to design a more reasonable return route. The planning method currently commonly used for the parachute-controlled recovery mission of the first stage of a rocket is mainly the classic three-stage segmented return method. This method simplifies the trajectory into a combination of straight lines and arcs, and at the same time ensures high landing accuracy and the requirements of parafoil landing against the wind, and has good engineering application prospects.

[0004] However, when the parachute opening point is too high, the trajectory planned by the three-stage segmentation algorithm will drive the system to be in a hovering stage for a long time in order to consume the redundant height, resulting in a large amount of control energy loss later; in addition, the first-stage parachute control system of the rocket is likely to be affected by the strong thermal air flow after entering the atmospheric boundary layer, causing flight problems such as wing breakage and collapse of the parachute canopy, posing a considerable safety hazard. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the present invention provides an intelligent planning method for parachute-controlled recovery of a sub-stage of a carrier rocket under highly redundant conditions, and the scheme includes: establishing a particle model of a sub-stage system of a carrier rocket based on an inertial system during the homing process; determining the dynamic parameters of the carrier rocket recovery system in combination with the wind field coordinate system and the particle model; dividing the homing process into five stages: high-altitude homing stage, circling height-cutting stage, return stage, energy management stage and headwind landing stage according to the flight characteristics of the sub-stage system of the carrier rocket under highly redundant conditions; obtaining the geometric relationship between the position and length of the track of the sub-stage system in each stage, and determining the initial homing trajectory of the sub-stage system of the carrier rocket; establishing an objective function and using a heuristic particle swarm optimization algorithm to update the position of the sub-stage system of the carrier rocket. The five-stage homing algorithm proposed by the present invention can effectively reduce the energy loss under highly redundant conditions.

[0006] The present invention adopts the following technical scheme: an intelligent planning method for parachute-controlled recovery of a launch vehicle sub-stage under highly redundant conditions, comprising: establishing a particle model of a launch vehicle sub-stage system based on an inertial system during the homing process; Determining the dynamic parameters of the launch vehicle recovery system by combining the wind field coordinate system and the mass point model; According to the flight characteristics of the first-stage system of the launch vehicle under highly redundant conditions, the return process is divided into five stages: high-altitude return stage, circling and height reduction stage, return stage, energy management stage and headwind landing stage. Based on the dynamic parameters of the launch vehicle recovery system, the geometric relationship between the position and length of the track of each stage of the sub-stage system is obtained, and the initial return trajectory of the launch vehicle sub-stage system is determined; The objective function is established according to the geometric relationship between the position and length of the track of the first-stage system in each stage, and the heuristic particle swarm optimization algorithm is used to update the position of the first-stage system of the launch vehicle.

[0007] Furthermore, the method further comprises: When the position of the first sub-stage system of the carrier rocket reaches the headwind landing stage, marking the current position as the starting point; Starting from the starting point, the initial homing trajectory is corrected using a random search tree algorithm to obtain a final homing path.

[0008] Furthermore, the particle model is specifically: ; in, Indicates airspeed, Indicates wind speed Axis and The weight on the axis, and They represent the velocity components of the system based on the inertial system, and the subscripts represents the inertial system, Indicates the altitude, is the falling speed, represents the derivative symbol, represents the yaw angle, Represents the yaw rate.

[0009] Furthermore, the dynamic parameters of the launch vehicle recovery system are determined by combining the wind field coordinate system and the mass point model, specifically: The wind field coordinate system is expressed as: ; in, and Respectively, in height The horizontal and vertical wind speed components are: Indicates the altitude, and They represent the velocity components of the system based on the inertial system respectively; The dynamic parameters of the launch vehicle recovery system are: ; in, and Indicates the position of the system based on the wind field coordinate system, Indicates the altitude, is the descent speed, is the ground speed, represents the yaw angle, Indicates the yaw rate.

[0010] Furthermore, the geometric relationship between the position and length of the track of each stage-by-stage system is obtained, including: Set the parachute opening point coordinates for , the initial azimuth is , the center angle and coordinate information of the first transition stage are: ; ; in, , Indicates the circling direction, 1 means counterclockwise, -1 means clockwise, is the circling radius in the transition phase; The initial point coordinates of the high altitude return segment BC are: ; in, ; The length and azimuth of the high altitude return segment are: ; ; in, Represents the radius of the spiral cut section; The center of the second transition segment The angle between the wind coordinate system x-axis and the central angle are: ; ; The position coordinates of the end point of the spiral cutting section are: ; in, , Indicates the center angle of the spiral cutting arc; The central angle of the third transition segment is: ; in, ; The vector representation of the regression stage is: ; in, , Indicates the azimuth angle of entering the energy management section; The angle between the vector of the regression phase and the positive direction of the x-axis is: ; The central angle of the fourth transition segment is: ; The center angles of the energy management section and the fifth transition section are: ; ; The length of the upwind landing segment is: ; in, is the radius of the energy management segment.

[0011] Furthermore, the objective function is established according to the geometric relationship between the position and length of the track of each sub-level system, which is expressed as: ; in, represents the weight coefficient, and They represent the target point accuracy function and the energy optimal function respectively, specifically: ; ; in, is the circling radius in the transition phase, Represents the radius of the spiral cut section, is the radius of the energy management segment, Respectively represent the center angle of the arc at each stage, is the descent speed, is the ground speed, Indicates the altitude of the parachute opening point. Indicates the return time.

[0012] Furthermore, the heuristic particle swarm optimization algorithm is used to update the position of the launch vehicle's first-stage system, specifically: Initialize to a set of A random particle swarm, each particle With speed and location , in each iteration the particle tracks two extreme values and Update, expressed as: ; in, , is a random number between 0 and 1, and represents the learning factor, is the best position that a particle itself experiences, is the optimal position experienced by the particle swarm.

[0013] The beneficial effects of the present invention are as follows: the five-stage homing algorithm proposed in the present invention can effectively solve the problem that the three-stage algorithm causes the system to be in a hovering state for a long time, thereby generating a large amount of control energy loss. Compared with the three-stage algorithm, it can effectively reduce the energy loss under highly redundant conditions. At the same time, based on the problem that the first-stage parachute control system of the rocket may be affected by the vigorous thermal airflow after entering the atmospheric boundary layer, the present invention proposes to use a random forest tree algorithm to correct the homing path of the system in the last stage, thereby improving the safety of the system's homing. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 It is a schematic flow chart of a method for intelligent planning of parachute-controlled recovery of a launch vehicle sub-stage under highly redundant conditions according to an embodiment of the present invention; Figure 2 A schematic diagram of a five-stage homing phase overall planning of an embodiment of the present invention; Figure 3 It is a schematic diagram of a three-dimensional trajectory of a three-stage planning method in an inertial system; Figure 4 A schematic diagram of a three-dimensional trajectory of a five-stage planning method in an inertial system according to an embodiment of the present invention Figure 5 A schematic diagram of energy loss comparison according to an embodiment of the present invention; Figure 6 A schematic diagram of a simulated trajectory of a homing path correction according to an embodiment of the present invention; Figure 7 A top view of a homing path simulation trajectory according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] The flowchart of the intelligent planning method for parachute-controlled recovery of a launch vehicle sub-stage under highly redundant conditions in an embodiment of the present invention is as follows: Figure 1 As shown: Step 1: Establish a particle model of the first stage of the launch vehicle based on the inertial system during the homing process: ; in, Indicates airspeed, Indicates wind speed Axis and The weight on the axis, and They represent the velocity components of the system based on the inertial system, and the subscripts represents the inertial system, Indicates the altitude, is the falling speed, represents the derivative symbol, represents the yaw angle, Represents the yaw rate.

[0018] Considering the difficulty of directly attempting to perform trajectory planning in an inertial system disturbed by the wind field, the present invention introduces a virtual wind coordinate system to further simplify the problem. The wind field coordinate system can be expressed as: ; in, and Respectively, in height The horizontal and vertical wind speed components are: and Indicates the position of the system based on the wind field coordinate system.

[0019] When the wind profile estimation is accurate enough, the dynamic equation of the rocket parachute recovery system based on the wind field coordinate system can be obtained as follows: ; in, and Indicates the position of the system based on the wind field coordinate system, Indicates the altitude, is the descent speed, is the ground speed, represents the yaw angle, Represents the yaw rate.

[0020] Step 2: Taking into account the flight characteristics of the first stage system of the launch vehicle under high redundancy, the present invention divides the flight track into a high-altitude return segment BC and a radius The spiral cutting height section DE, the return stage FG, and the radius are The energy management stage HI and the upwind landing stage JK are five stages, and the circling radius is The transition segments are connected, and the centers of the transition segments are defined as and The overall planning diagram is as follows: Figure 2 As shown in the figure represents the center angle of each arc segment, Satisfy constraints , ,and .

[0021] Step 3: Establish the geometric relationship between the position and length of each track segment during the first-stage recovery process: The present invention sets the coordinates of the parachute opening point for , the initial azimuth is , then the center angle and coordinate information of the first transition segment are: ; ; in, , S represents the circling direction, 1 represents counterclockwise, and -1 represents clockwise.

[0022] The coordinates of the initial point of the high altitude return segment are: ; in, ; The length and azimuth of the high altitude return segment are: ; ; in, Represents the radius of the spiral cut section; The center of the second transition segment The angle between the wind coordinate system x-axis and the central angle are: ; ; End point of spiral cutting The location coordinates are: ; in, , Indicates the center angle of the spiral cutting arc; The central angle of the third transition segment is: ; in, ; The vector expression of the regression stage is: ; in, , Indicates the azimuth of entering the energy management segment.

[0023] The angle between the vector of the regression phase and the positive direction of the x-axis is: ; The central angle of the fourth transition segment is: ; The central angles of the energy management section and the fifth transition section are: ; ; The length of the upwind landing segment is: ; Step 4: Determine the four key parameters of the homing trajectory A unique homing trajectory can be obtained, and the optimization objective function established based on this is: ; in, represents the weight coefficient, and They represent the target point accuracy function and the energy optimal function respectively, namely: ; ; in, is the circling radius in the transition phase, Represents the radius of the spiral cut section, is the radius of the energy management segment, Respectively represent the center angle of the arc at each stage, is the descent speed, is the ground speed, Indicates the altitude of the parachute opening point. Indicates the return time.

[0024] Step 5: The present invention introduces a heuristic particle swarm optimization algorithm, by initializing a group of A random particle swarm, each particle Each has its own speed and location , in each iteration, the particle tracks two extreme values and To be continuously updated, among which, is the best position that a particle itself experiences, is the optimal position experienced by the particle swarm. After finding these optimal values, the particles update their speed and position in the following way: ; in, ,also, is a random number between 0 and 1, and Represents the learning factor.

[0025] Step 6. The present invention uses the homing trajectory obtained in steps 1 to 5 as the initial target trajectory of the rocket sub-stage system until the system's altitude drops to 800 meters or enters the headwind landing stage. The terminal trajectory online correction algorithm is given through steps 7 to 11. At this time, the system's position is set to .

[0026] Step 7: Considering that cumulus clouds and thermal turbulence obstacles may threaten the system's homing process, the present invention corrects the terminal track based on the random search tree algorithm, specifically: Initialize the random search structure tree , assign the starting point of the random search structure tree to , and then generate a random sampling point in the specified planning space ,calculate With random trees The distance between each node in , then calculate The final point of the return path Distance , calculate each and of , expressed as: , where the distance , , thus The node corresponding to the minimum value is used as the node to be expanded .

[0027] Step 8: The node to be expanded Expand towards the final point of the homing path and determine Is there a record of failure to expand in the target direction? If so, go to the next step. Otherwise, continue along the and direction, with step length Expand to get new nodes ;like and If the expansion test ball at any point does not hit an obstacle, it means that the expansion is successful. Add random trees and go to step 11; otherwise, continue to the next step and mark the failed point as extending toward the final point of the homing path; ; Step 9: Follow and direction, with step length Expand to get new nodes ;like and If the expansion test ball at any point does not hit an obstacle, it means that the expansion is successful. Add random trees Otherwise, go to step eight; Step 10: Judgment and Whether the Euclidean distance between them reaches the final point of the return path, if so, proceed to the next step; otherwise, go to step eight; Step 11: When the final point of the homing path is reached, the loop ends and the Back to The corrected path can be obtained.

[0028] The embodiment of the present invention further provides the difference between the five-stage return path planning method and the three-stage return path planning method through simulation. Figure 3As shown in FIG. 1 , when the parachute opening point is too high, the trajectory planned by the three-stage segmentation algorithm will drive the system to hover for a long time in order to consume the redundant height, resulting in a large amount of control energy loss in the future. The five-stage planning method of the present invention is as follows: Figure 4 As shown, the time that the system is in a hovering state can be effectively reduced, thereby reducing the energy loss of the system in a highly redundant situation. A simulation result of the present invention is shown in Figure 5 As shown, it can be seen that compared with the three-stage planning algorithm, the five-stage planning algorithm designed in the present invention has lower energy loss.

[0029] The embodiment of the present invention further provides schematic diagrams of simulation results of path correction at an altitude below 800 meters. Figure 6 and Figure 7 As shown, Figure 6 is a three-dimensional trajectory, Figure 7 This is a trajectory diagram from a bird's-eye view. It can be seen that the homing path correction algorithm proposed in the present invention improves the safety of the system homing.

[0030] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent planning method for parachute-controlled recovery of a launch vehicle stage under highly redundant conditions, characterized in that: include: Establish a particle model of the first stage system of the launch vehicle based on the inertial system during the homing process; Determining the dynamic parameters of the launch vehicle recovery system by combining the wind field coordinate system and the mass point model; According to the flight characteristics of the first-stage system of the launch vehicle under highly redundant conditions, the return process is divided into five stages: high-altitude return stage, circling and height reduction stage, return stage, energy management stage and headwind landing stage. Based on the dynamic parameters of the launch vehicle recovery system, the geometric relationship between the position and length of the track of each stage of the sub-stage system is obtained, and the initial return trajectory of the launch vehicle sub-stage system is determined; The objective function is established according to the geometric relationship between the position and length of the track of the first-stage system in each stage, and the heuristic particle swarm optimization algorithm is used to update the position of the first-stage system of the launch vehicle.

2. The intelligent planning method for parachute-controlled recovery of a launch vehicle substage under highly redundant conditions according to claim 1 is characterized in that: The method further comprises: When the position of the first sub-stage system of the carrier rocket reaches the headwind landing stage, marking the current position as the starting point; Starting from the starting point, the initial homing trajectory is corrected using a random search tree algorithm to obtain a final homing path.

3. The intelligent planning method for parachute-controlled recovery of a launch vehicle substage under highly redundant conditions according to claim 1 is characterized in that: The particle model is specifically: ； in, Indicates airspeed, Indicates wind speed Axis and The weight on the axis, and They represent the velocity components of the system based on the inertial system, and the subscripts represents the inertial system, Indicates the altitude, is the falling speed, represents the derivative symbol, represents the yaw angle, Represents the yaw rate.

4. The intelligent planning method for parachute-controlled recovery of a launch vehicle substage under highly redundant conditions according to claim 1 is characterized in that: The dynamic parameters of the launch vehicle recovery system are determined by combining the wind field coordinate system and the mass point model, which are specifically: The wind field coordinate system is expressed as: ； in, and Respectively, in height The horizontal and vertical wind speed components are: Indicates the altitude, and They represent the velocity components of the system based on the inertial system respectively; The dynamic parameters of the launch vehicle recovery system are: ； in, and Indicates the position of the system based on the wind field coordinate system, Indicates the altitude, is the descent speed, is the ground speed, represents the yaw angle, Represents the yaw rate.

5. The intelligent planning method for parachute-controlled recovery of a launch vehicle substage under highly redundant conditions according to claim 1 is characterized in that: Get the geometric relationship between the position and length of each stage-by-stage system track, including: Set the parachute opening point coordinates for , the initial azimuth is , the center angle and coordinate information of the first transition stage are: ； ； in, , Indicates the circling direction, 1 means counterclockwise, -1 means clockwise, is the circling radius in the transition phase; The initial point coordinates of the high altitude return segment BC are: ; in, ; The length and azimuth of the high altitude return segment are: ； ； in, Represents the radius of the spiral cut section; The center of the second transition segment The angle between the wind coordinate system x-axis and the central angle are: ； ； The position coordinates of the end point of the spiral cutting section are: ； in, , Indicates the center angle of the spiral cutting arc; The central angle of the third transition segment is: ; in, ; The vector representation of the regression stage is: ； in, , Indicates the azimuth angle of entering the energy management section; The angle between the vector of the regression phase and the positive direction of the x-axis is: ； The central angle of the fourth transition segment is: ; The center angles of the energy management section and the fifth transition section are: ； ； The length of the upwind landing segment is: ； in, is the radius of the energy management segment.

6. The intelligent planning method for parachute-controlled recovery of a launch vehicle substage under highly redundant conditions according to claim 1 is characterized in that: The objective function is established according to the geometric relationship between the position and length of the track of each sub-level system, which is expressed as: ； in, represents the weight coefficient, and They represent the target point accuracy function and the energy optimal function respectively, specifically: ； ； in, is the circling radius in the transition phase, Represents the radius of the spiral cut section, is the radius of the energy management segment, Respectively represent the center angle of the arc at each stage, is the descent speed, is the ground speed, Indicates the altitude of the parachute opening point. Indicates the return time.

7. The intelligent planning method for parachute-controlled recovery of a launch vehicle substage under highly redundant conditions according to claim 1 is characterized in that: The heuristic particle swarm optimization algorithm is used to update the position of the launch vehicle's first-stage system, specifically: Initialize to a set of A random particle swarm, each particle With speed and location , in each iteration the particle tracks two extreme values and Update, expressed as: ； in, , is a random number between 0 and 1, and represents the learning factor, is the best position that a particle itself experiences, is the optimal position experienced by the particle swarm.

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