CN110955974A - Rocket recovery simulation platform and implementation method - Google Patents

Rocket recovery simulation platform and implementation method Download PDF

Info

Publication number
CN110955974A
CN110955974A CN201911201746.8A CN201911201746A CN110955974A CN 110955974 A CN110955974 A CN 110955974A CN 201911201746 A CN201911201746 A CN 201911201746A CN 110955974 A CN110955974 A CN 110955974A
Authority
CN
China
Prior art keywords
rocket
thrust
state quantity
guidance
information
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201911201746.8A
Other languages
Chinese (zh)
Other versions
CN110955974B (en
Inventor
龚胜平
宋雨
张伟
苗新元
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tsinghua University
Original Assignee
Tsinghua University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tsinghua University filed Critical Tsinghua University
Priority to CN201911201746.8A priority Critical patent/CN110955974B/en
Publication of CN110955974A publication Critical patent/CN110955974A/en
Application granted granted Critical
Publication of CN110955974B publication Critical patent/CN110955974B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Navigation (AREA)
  • Testing Of Engines (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A rocket recovery simulation platform and an implementation method are provided, wherein the rocket recovery simulation platform comprises: the system comprises a guidance module, a control module and a control module, wherein the guidance module is used for acquiring the actual state quantity of a rocket in each guidance period, taking the actual state quantity as an input parameter, and determining the optimal thrust information according to a dynamic model of a rocket landing section and an optimal control problem model of rocket recovery guidance; the control module is used for determining thrust execution information according to the optimal thrust information and the thrust constraint information and transmitting the thrust execution information to the rocket model module; the rocket model module is used for determining theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamic model; and the navigation module is used for determining the actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module and outputting the actual state quantity to the guidance module.

Description

Rocket recovery simulation platform and implementation method
Technical Field
The present disclosure relates to the field of rocket recovery, and more particularly, to a rocket recovery simulation platform and an implementation method thereof.
Background
For the problem of vertical recovery guidance of the rocket, the traditional guidance method makes many simplifications and assumptions for obtaining a real-time guidance law, and usually the landing precision under a complex dynamic environment is difficult to ensure, but the requirement of the rocket landing process on the real-time performance of the guidance method cannot be met based on a numerical value and an approximate solution under a more accurate model.
Convex optimization is used for space mission guidance and trajectory optimization, and is firstly applied to solving the problem of powered accurate soft landing of a Mars probe. Acikmese et al propose to transform the trajectory optimization Problem of detector landing into a Second Order Cone Problem (SOCP) to solve, and further propose a technique of lossless saliency to solve the general trajectory optimization Problem. Compared with the traditional numerical iteration algorithm for solving the track optimization problem, the algorithm has the advantages that the problem can be solved in polynomial time on the premise of ensuring the equivalence of the problem solution and not losing the optimality of the original problem, and online solving and real-time guidance are possible. Aiming at the problem of rocket vertical recovery, domestic Liu, Zhang and the like respectively establish a model for solving two-dimensional and three-dimensional problems through convex optimization. The jet Laboratory (JPL) of NASA, the European Space Agency (ESA) and other organizations also develop the relevant research of convex optimization for the vertical take-off and landing rocket guidance algorithm, and further expand the problem to six degrees of freedom. However, the robustness of the guidance algorithm in practical application is not known.
Disclosure of Invention
The application provides a rocket recovery simulation platform and an implementation method thereof, which are used for testing the robustness of a guidance algorithm.
The embodiment of the application provides a rocket recovery simulation platform, which comprises: a guidance module, a control module, a rocket model module and a navigation module connected in a closed loop, wherein
The system comprises a guidance module, a dynamic model and an optimal control problem model, wherein the guidance module is used for acquiring the actual state quantity of a rocket in each guidance period, taking the actual state quantity as an input parameter, and determining optimal thrust information according to the dynamic model of a rocket landing section and the optimal control problem model of rocket recovery guidance;
the control module is used for determining thrust execution information according to the optimal thrust information and the thrust constraint information and transmitting the thrust execution information to the rocket model module;
the rocket model module is used for determining theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamic model;
and the navigation module is used for determining the actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module and outputting the actual state quantity to the guidance module.
In an embodiment, the guidance module is configured to perform a convex processing on the optimal control problem model according to the dynamic model, convert the optimal control problem model into a second-order cone optimization problem, and solve the second-order cone optimization problem based on an interior point method according to the actual state quantity to obtain the optimal thrust information.
In an embodiment, the guidance module is configured to perform discrete processing and linearization on a nonlinear dynamical equation constraint corresponding to the dynamical model, and perform relaxation processing on a non-convex thrust amplitude constraint through a relaxation variable, so that the optimal control problem model is subjected to lossless convex processing to be a second-order cone optimization problem.
In an embodiment, the optimal thrust information includes an optimal thrust curve, the guidance module is configured to convert the second-order cone optimization problem into a plurality of sub-problem instances, determine parameters of each sub-problem instance according to the actual state quantity, record non-zero element information by using a column compression algorithm for a coefficient matrix constrained by each sub-problem instance, and perform an interior point method iteration solution on each sub-problem instance to obtain the optimal thrust curve, where the non-zero element information is used as matrix description information during the interior point method iteration solution, and only the matrix description information is modified during each interior point method iteration;
wherein the non-zero element information includes: the non-zero element value, the non-zero element column index and the column first non-zero element array index.
In an embodiment, the optimal thrust information includes an optimal thrust curve, and the control module is configured to determine the thrust execution information in one of the following manners:
when the thrust constraint information is a constant thrust value in a control period, setting thrust execution information as an interpolation average of an optimal thrust curve in the current control period;
when the thrust constraint information is thrust grading, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as a thrust value corresponding to the thrust gear;
and when the thrust constraint information is thrust gear and amplitude deviation exists, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as the sum of a thrust value corresponding to the thrust gear and the amplitude deviation.
In an embodiment, the rocket model module is configured to determine a flight trajectory and a mass of the rocket according to the thrust execution information, the dynamics model, and the atmospheric disturbance information, and determine the theoretical state quantity according to the flight trajectory and the mass;
the flight track comprises a flight position and a flight speed, and the atmospheric disturbance information comprises atmospheric resistance information and/or lateral wind disturbance information.
In an embodiment, the navigation module is configured to determine the actual state quantity of the rocket by one of the following methods:
setting the actual state quantity of the rocket to be equal to the theoretical state quantity;
setting the actual state quantity of the rocket to be equal to the theoretical state quantity plus the measurement deviation;
estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket to obtain a first estimated state quantity, and setting the actual state quantity of the rocket to be equal to the first estimated state quantity;
estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket and the measurement deviation to obtain a second estimated state quantity, and setting the actual state quantity of the rocket to be equal to the second estimated state quantity.
In one embodiment, the navigation module is configured to:
when estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket, acquiring the first estimated state quantity in a mode of integrating one guidance period by a 10-step fourth-order Runge-Kutta fixed step length based on the theoretical state quantity of the rocket;
and when estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket and the measurement deviation, obtaining the second estimated state quantity according to a mode of integrating one guidance period by a fixed step length of 10 steps of fourth-order Runge-Kutta on the basis of the theoretical state quantity of the rocket and the measurement deviation.
The embodiment of the present application further provides a method for implementing rocket recovery simulation, including:
acquiring the actual state quantity of the rocket in each guidance period, taking the actual state quantity as an input parameter, and determining optimal thrust information according to a dynamic model of a rocket landing section and an optimal control problem model of rocket recovery guidance;
determining thrust execution information according to the optimal thrust information and the thrust constraint information;
determining theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamic model;
and determining the actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module.
The embodiment of the present application further provides a rocket recovery simulation platform, including: the rocket recycling simulation system comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the implementation method of the rocket recycling simulation when executing the program.
Compared with the related art, the method comprises the following steps: the system comprises a guidance module, a control module, a rocket model module and a navigation module, wherein the guidance module is used for acquiring the actual state quantity of the rocket in each guidance period, taking the actual state quantity as an input parameter, and determining the optimal thrust information according to a dynamic model of a rocket landing section and an optimal control problem model of rocket recovery guidance; the control module is used for determining thrust execution information according to the optimal thrust information and the thrust constraint information and transmitting the thrust execution information to the rocket model module; the rocket model module is used for determining theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamic model; and the navigation module is used for determining the actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module and outputting the actual state quantity to the guidance module. According to the method and the device, a guidance, navigation and control integrated closed-loop numerical simulation platform is set up, a rocket landing closed-loop simulation strategy is provided, and the robustness of a guidance algorithm in a rocket landing stage can be tested.
In an exemplary embodiment, the solving efficiency is improved by customizing the solver of the interior point method, and the method has the characteristics of embeddability and real-time online calculation.
In an exemplary embodiment, the robustness of the guidance algorithm under the action of non-ideal factors such as environmental atmospheric disturbance, thrust execution errors, navigation system errors and guidance process delay can be tested.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. Other advantages of the present application may be realized and attained by the instrumentalities and combinations particularly pointed out in the specification and the drawings.
Drawings
The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.
FIG. 1 is a schematic diagram of a rocket recovery simulation platform according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a general convex optimization solver for solving a general convex optimization problem;
FIG. 3 is a schematic diagram of a process for solving a specific convex optimization problem by a customized convex optimization solver according to an embodiment of the present application;
FIG. 4 is a schematic flow chart of an online guidance scheme based on a state estimator according to an embodiment of the present application;
FIG. 5 is a flowchart of a method for implementing rocket recovery simulation according to an embodiment of the present application;
FIG. 6 is a flowchart of step 301 of an embodiment of the present application;
fig. 7 is a comparison diagram of state quantity change conditions of solution results of the customized solver and the general solver according to the embodiment of the application, where x, y, and x respectively represent three position components of the rocket, and Vx, Vy, and Vz respectively represent three velocity components of the rocket;
FIG. 8 is a comparison graph of actual flight paths before and after adding ambient atmospheric disturbances in an embodiment of the present application;
fig. 9 is an execution curve of different thrust scenarios of the embodiment of the present application, where (a) is an originally planned thrust execution scenario of the embodiment of the present application, (b) is an execution scenario of the embodiment of the present application according to a constant thrust value in each control cycle, (c) is an execution scenario of the embodiment of the present application according to a thrust step in each control cycle, and (d) is an execution scenario diagram of the embodiment of the present application according to a thrust step in each control cycle and considering a ± 5% thrust amplitude error.
Fig. 10 is a comparison of flight paths and state quantities according to an embodiment of the present application, in which (a) is a schematic flight trajectory, and (b) is a schematic flight state change situation.
Detailed Description
The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
When the rocket landing problem is solved by utilizing convex optimization, the problem of the guidance algorithm is mainly solved, but a plurality of assumptions are usually made on the model, such as neglecting atmospheric resistance, constant gravity acceleration, continuously adjustable thrust and the like, so that the guidance algorithm can be quickly converged. However, the robustness and adaptation boundaries of the guidance algorithm are not known during actual flight.
The guidance algorithm of the embodiment of the application meets the requirement of real-time performance while ensuring the accuracy of the rocket landing model, and the robustness of the convex optimization guidance algorithm can be tested by building a closed-loop simulation platform.
As shown in fig. 1, a rocket recovery simulation platform according to an embodiment of the present application includes: a guidance module 11, a control module 12, a rocket model module 13 and a navigation module 14 connected in a closed loop, wherein
The guidance module 11 is configured to acquire an actual state quantity of the rocket in each guidance period, use the actual state quantity as an input parameter, and determine optimal thrust information according to a dynamic model of a rocket landing segment and an optimal control problem model of rocket recovery guidance;
the control module 12 is configured to determine thrust execution information according to the optimal thrust information and the thrust constraint information, and transmit the thrust execution information to the rocket model module;
the rocket model module 13 is configured to determine a theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamics model;
the navigation module 14 is configured to determine an actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module, and output the actual state quantity to the guidance module.
In the rocket recovery simulation platform, a rocket recovery guidance algorithm and a real-time solving method are packaged into a guidance module 11, the actual state quantity of the rocket is used as an input parameter, and the optimal thrust information (namely an optimal guidance scheme) corresponding to the actual state quantity is used as an output parameter. In a guidance period, based on the state of the current time, the guidance module 11 adopts a guidance algorithm to generate an optimal fuel control law, namely an optimal thrust value curve corresponding to the current time until the task is finished, and transmits the optimal thrust value curve to the control module 12; the control module 12 executes the thrust according to the optimal thrust value curve of the current period, and transmits a thrust execution scheme to the rocket model module 13; the rocket model module 13 performs numerical integration according to the control instruction output by the control module 12 to obtain the state (theoretical state quantity) of the rocket after a period, and transmits the state to the navigation module 14; the navigation module is responsible for collecting the rocket state (actual state quantity) of the current period and transmitting the rocket state to the guidance module, so that the next guidance period is started until the position speed of the rocket meets the requirement of accurate soft landing, namely the landing position error is less than 1m, the speed error is less than 1m/s, or the flying height of the rocket reaches 0.
The guidance, navigation and control integrated closed-loop numerical simulation platform set up by the embodiment of the application can test the robustness of a guidance algorithm in a rocket landing stage.
Each block will be described below.
Guidance module 11
In the embodiment of the application, the guidance algorithm is tested by building a rocket recovery simulation platform, so that the guidance module 11 can adopt various guidance algorithms.
The embodiment of the application adopts a convex optimization solving algorithm, and establishes a customized convex optimization solver by using an interior point method, so that the guidance algorithm has real-time performance.
The guidance module 11 performs a convex processing on the optimal control problem model according to the dynamic model, converts the optimal control problem model into a second-order cone optimization problem, and solves the second-order cone optimization problem based on an interior point method according to the actual state quantity to obtain the optimal thrust information.
The state quantities include the position, velocity and mass of the rocket. The optimal thrust information includes an optimal thrust curve.
The following explains the guidance algorithm employed in the embodiments of the present application.
In the embodiment of the present application, the implementation of the guidance algorithm may include the following steps:
the method comprises the following steps: and establishing a dynamic model of the rocket landing section and an optimal control problem model of the rocket recovery guidance problem.
Wherein, the dynamic model of the rocket landing segment is as follows:
Figure BDA0002296051970000081
wherein r and v represent the position and velocity vector of rocket motion, g represents the gravity acceleration vector, T is the thrust vector of rocket engine, m represents the rocket mass, aDIs an aerodynamic resistance vector, IspDenotes rocket motor specific impulse, g0Representing the earth sea level gravitational acceleration constant.
Aiming at the rocket dynamics model of the formula (1), the optimal control problem model of the rocket recovery guidance problem with the optimal fuel is established as follows:
an objective function:
min J＝-m(tf) (2)
and (3) state constraint:
Figure BDA0002296051970000091
Figure BDA0002296051970000092
Figure BDA0002296051970000093
and (3) controlling quantity constraint:
Tmin≤||T(t)||≤Tmax(6)
Figure BDA0002296051970000094
wherein, t0To the landing start time, tfIs the total time of flight of the rocket, i.e. the landing time, r0、v0、m0Respectively representing a landing starting position, a velocity vector and a rocket starting mass, mdryExpressing the dry weight of the rocket, β expressing the obstacle avoidance constraint angle of the flight path of the rocket, taking the vertical ground direction as the coordinate y axis, the x axis points to the north direction,z axis and x and y axes form a right-hand coordinate system, rx、ryAnd rzThe sub-table represents three components of the position vector at any time t; t isminAnd TmaxRespectively representing the upper and lower limit constraints of the thrust amplitude, gamma representing the maximum swing angle of the thrust direction and the vertical direction, Tx、TyAnd TzThe partial table represents three components of the thrust vector.
Step two: and (5) carrying out convex processing on the optimal control problem model.
The optimal control problem model of the rocket recovery guidance problem established in the step one has nonlinear kinetic equation constraints shown in a formula (1) and non-convex constraints of thrust amplitude shown in a formula (6). Therefore, the step two carries out convex processing on the original problem on the basis of the non-convex problem established in the step one.
For the nonlinear dynamical equation constraint of the formula (1), a successive convex method is mainly adopted. Firstly, a kinetic equation is discretized, so that a continuous time problem is converted into a discrete problem, and an originally implicit time free variable is obviously contained through a discrete difference equation. Taking N discrete points in the time domain, the time interval between each discrete point is expressed as:
Figure BDA0002296051970000101
writing equation (1) as an expanded form of Δ t, and taking a first order term as an approximation, can be expressed as:
Figure BDA0002296051970000102
wherein x is used to represent the state quantity in the formula (1), and u is used to represent the control quantity in the formula (1), i.e.
Figure BDA0002296051970000103
Figure BDA0002296051970000104
The right-end term of the equation in the formula (1) is expressed by f, k represents the k-th discrete point, and the range of k is 1-N-1.
In equation (9), the second term on the right end of the equation is still a non-linear constraint, so that the following expression can be obtained by taking a taylor expansion first-order approximation of the state quantity x, the controlled quantity u and the time term Δ t through linearization:
Akx[k]+Ak+1x[k+1]+Bku[k]+Bk+1u[k+1]+C·Δt+D＝0 (10)
wherein:
Figure BDA0002296051970000105
Figure BDA0002296051970000106
Figure BDA0002296051970000107
Figure BDA0002296051970000111
Figure BDA0002296051970000112
D＝x'[k]-x'[k+1]-Akx'[k]-Ak+1x'[k+1]-Bku'[k]-Bk+1u'[k+1]-CΔt' (16)
wherein, the prime notation is used to represent the known quantity obtained by the previous iteration solution.
As can be seen from expressions (11) to (16), the coefficients before all the unknown variables in expression (10) are constant matrices. The equation is an equality linear constraint and meets the equality constraint requirement of the convex optimization problem. Because a great deal of approximation processing is carried out on the problem in the successive approximation process, and the state quantity and the control quantity are strictly restricted, the problem that the previous iterations have no feasible solution is easily caused in the successive approximation iterative solution process. In order to avoid such problems, a relaxation technology can be adopted, namely, a virtual control force is added in addition to the control force, and a larger penalty term coefficient is applied to the term in the objective function, so that the problem of convergence is well solved.
For the non-convex thrust magnitude constraint of the form of equation (6), a lossless convex method is mainly adopted, a relaxation variable Г is introduced, and the constraint is relaxed as shown in equation (17):
||T(t)||≤Γ
Tmin≤Γ≤Tmax(17)
at the same time, the thrust term in the rate of change of mass in equation (1) is also replaced by a new relaxation variable:
Figure BDA0002296051970000113
the extreme maximum principle of Pontryagin proves that the final convergence solution before and after the relaxation transformation has equivalence to the original problem, namely the problem after the relaxation transformation is definitely converged to the optimal solution which enables the inequality (17) to be active, so that the problem is called lossless relaxation. The attestation process is not described in detail herein.
Through the convex processing in the Second step, the original continuous time optimal control Problem is converted into a Second Order Cone optimization Problem (SOCP) at a series of discrete points, and the form is as follows:
Figure BDA0002296051970000121
subject to：
Figure BDA0002296051970000122
η thereinuAnd ηΔtTrust domains of control quantity and time, respectively, avAs virtual acceleration, ωu、ωΔtAnd ωavIs a penalty term coefficient. For the SOCP problem, the SOCP problem can be solved through an interior point method, so that an optimal control quantity curve u from a given initial state to a landing target is obtained*。
Step three: and (5) customizing and solving the convex optimization problem.
And step three, aiming at the SOCP problem obtained in the step two, customizing and solving the problem based on an interior point method, and establishing an embedded calculation customization-oriented solver to enable the algorithm to have real-time performance and online calculability.
In general, the general process of solving the SOCP problem using a general convex optimization solver is shown in FIG. 2. The universal convex optimization solver generally has a relatively friendly interpretative language interface, a user only needs to describe the convex optimization problem through the interpretative language, and the solver converts the problem into a series of sub-problem examples through pre-processing such as interpretative language translation, discrete assignment and the like, and solves the problem through an interior point method. However, the solution of the original problem is completed by solving the SOCP problem, and usually several iterations are required, and the result of the last solution is required to be the latest initial value, and the above process is repeated until the convergence condition of the optimization problem is satisfied. It can be seen that the method has a large number of repeated calculations, so that the calculation efficiency is low, the algorithm real-time solving performance is not provided, and the problem solving usually depends on a specific general solver and is not provided with online calculability.
The process of solving a specific SOCP by the customized convex optimization solver provided by the embodiment of the application is shown in FIG. 3. Different from the general convex optimization solver, the customized solver directly describes the problem as a series of sub-problem examples after the discretization and convex processing of the step two is performed for a specific SOCP problem. For the sub-problem example on each discrete point, such as the coefficient matrix appearing in formula (10), a column compression algorithm is adopted, three arrays which respectively record a non-zero element value, a non-zero element column index and a column first non-zero element array index can be obtained, all information of the sparse matrix can be reflected by using the three arrays, and the storage space is greatly saved. The array information can be directly used as matrix description information when the interior point method is used for solving. In addition, for a specific SOCP problem, only the value of the recorded non-zero element value changes along with iteration, and the rest is a fixed constant array. Therefore, in the repeated iteration process, only the information of the array needs to be modified, and the efficiency of iterative computation is greatly improved. Compared with the situation that the general solver repeats the interpreted language translation, the pre-processing of the solution, the extra library function support and the like once in each solution, the customized solution only needs to update some elements in the fixed array once, does not depend on extra library functions, and has the characteristics of high efficiency, rapidness, light weight, embeddable calculation and the like.
In the above steps, modeling of the rocket recovery guidance problem is completed through the first step; on the basis, the optimal control problem of the rocket recovery guidance problem is converted into the SOCP problem in the second step; and step three, establishing a customized solver based on an interior point method, thereby realizing the real-time online solution of the SOCP problem obtained in the step two.
As can be seen from the above description, for the first step and the second step, the guidance module 11 performs discrete processing and linearization processing on the nonlinear dynamical equation constraint corresponding to the dynamical model, and performs relaxation processing on the non-convex thrust amplitude constraint through a relaxation variable, so as to perform lossless convex processing on the optimal control problem model into a second-order cone optimization problem.
Aiming at the third step, the guidance module 11 converts the second-order cone optimization problem into a plurality of sub-problem instances, determines parameters of each sub-problem instance according to the actual state quantity, adopts a column compression algorithm for a coefficient matrix constrained by each sub-problem instance, records non-zero element information, and performs inner point method iteration solution on each sub-problem instance to obtain the optimal thrust curve, wherein the non-zero element information is used as matrix description information during inner point method iteration solution, and only the matrix description information is modified during each inner point method iteration; wherein the non-zero element information includes: the non-zero element value, the non-zero element column index and the column first non-zero element array index.
Second, control module 12
In the above steps one and two, it is assumed that the thrust is continuously adjustable within its amplitude range. In practice, however, the thrust force may have the following constraints:
1. thrust constant value in the control period:
and when the thrust constraint information is a constant thrust value in one control period, setting the thrust execution information as an interpolation average of the optimal thrust curve in the current control period.
In a closed loop control period, the thrust is considered to be not continuously variable and is fixed to a constant value. An interpolated average of the thrust curves in the optimal guidance schedule generated during the control period may be taken.
2. Thrust grading:
and when the thrust constraint information is thrust grading, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as a thrust value corresponding to the thrust gear.
For example, considering that the engine thrust cannot be continuously adjusted, it can only be performed in several fixed gears, 0.2, 0.4, 0.6, 0.8, 1.0 times the maximum amplitude, etc. When the method is executed, the method can be executed according to the interpolation average of the thrust curve in the control period, and the thrust gear which is relatively close to the control period is correspondingly taken.
3. The thrust is graded and has amplitude deviation:
and when the thrust constraint information is thrust gear and amplitude deviation exists, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as the sum of a thrust value corresponding to the thrust gear and the amplitude deviation.
The thrust of the engine cannot be continuously adjusted and can only be executed according to a fixed gear, but the random amplitude deviation is considered on the basis of thrust stepping to realize the execution effect.
Third, rocket model module 13
The rocket model module 13 determines the flight trajectory and the mass of the rocket according to the thrust execution information, the dynamic model and the atmospheric disturbance information, and determines the theoretical state quantity according to the flight trajectory and the mass.
The flight path comprises a flight position and a flight speed, and the atmospheric disturbance information comprises atmospheric resistance information and/or lateral wind disturbance information.
In the first step and the second step, in order to consider convergence and calculation efficiency of the guidance algorithm, the online guidance algorithm does not consider atmospheric disturbance factors, and the factors are considered in a dynamic model of the rocket, so that the actual flight state is different from the path planned by the guidance algorithm, and therefore the guidance module 11 is required to update the guidance law in real time according to the current state, so as to ensure that the rocket can update the guidance algorithm according to the current actual state in real time to ensure that the rocket finally reaches the preset landing target.
Fourthly, the navigation module 14
In the ideal case, the actual state quantity of the rocket is equal to the theoretical state quantity. However, during the rocket flight, the navigation system may have a certain measurement deviation. To simulate a real environment, the navigation module 14 may set the actual state quantity of the rocket equal to the theoretical state quantity plus a measured deviation.
The absolute error and the relative error are comprehensively considered, and the following error form can be adopted before the navigation module 14 acquires the theoretical state quantity of the rocket and transmits the theoretical state quantity to the guidance module:
Figure BDA0002296051970000151
where ε represents the maximum range of relative error, rtest、vtestAnd rtrue、vtrueRespectively, representing the position velocity vectors output and input by the navigation module 14. Different values can be respectively taken in the simulation to test the adaptive boundary of the algorithm to the navigation system error.
In addition, a certain time is needed for the guidance algorithm 11 to calculate and update the guidance scheme each time, in the closed-loop simulation, in order to improve the calculation efficiency of the guidance scheme, the guidance scheme generated last time is used as an iteration initial value for calculating the guidance scheme next time through time interpolation each time, the iteration times can be reduced to 1 time to 2 times, and the calculation time is about 20-30 ms. However, considering that the rocket-borne computer has limited computing power and the possibility that the generation of the guidance scheme cannot be completed once in a control period (e.g. 200ms), the present embodiment shows a guidance scheme based on the state estimator processing system delay, and the flow chart of the scheme is shown in fig. 4.
Step 201, generating an optimal thrust curve through a convex optimization solver, and storing the optimal thrust curve in a local guidance scheme;
step 202, the control module executes the local guidance scheme and outputs thrust execution information;
step 203, outputting theoretical state quantity of the rocket according to the thrust execution information according to the rocket model;
step 204, adding a measurement error to the theoretical state quantity (the step can be omitted);
step 205, determining whether the local guidance plan needs to be updated, if yes, executing step 206, if no, executing step 202,
wherein, according to a preset guidance period TGAnd judging, if the guidance period is reached, updating the local guidance scheme, otherwise, updating is not needed.
Step 206, estimating the rocket in a guidance period T through the state estimatorGThe latter state quantity returns to step 201.
In the embodiment, the rocket is estimated in a guidance period T according to the theoretical state quantity of the rocketGObtaining a first estimated state quantity by the later state quantity, and setting the actual state quantity of the rocket to be equal to the first estimated state quantity; or estimating the rocket at T according to the theoretical state quantity of the rocket and the measurement deviationGAnd obtaining a second estimated state quantity by the later state quantity, and setting the actual state quantity of the rocket to be equal to the second estimated state quantity.
Wherein, the rocket is estimated in a guidance period T according to the theoretical state quantity of the rocketGAnd in the later state quantity, based on the theoretical state quantity of the rocket, integrating T by a fixed step length of 10 steps of fourth-order Runge-KuttaGThe first estimated state quantity is obtained.
Estimating the rocket in a guidance period T according to the theoretical state quantity of the rocket and the measurement deviationGThe state quantity of the rocket is added with measurement based on the theoretical state quantity of the rocketDeviation, according to a 10-step fourth-order Runge-Kutta fixed step length integral TGThe second estimated state quantity is obtained.
The state estimation method adopted in this embodiment is to integrate one guidance period T with a fixed step length of 10 steps of fourth-order longge-kuta (RK4) according to the theoretical state quantity (or the theoretical state quantity plus the measurement deviation) obtained by the navigation module 14 (i.e. the current state quantity measured by the navigation system in practical application)GTo thereby estimate TGThe latter state is transmitted to the guidance module 11 as a state input at the time of guidance scheme update. Because the RK4 fixed-step integration only needs to perform a few algebraic operations, the method can avoid efficiency loss caused by a large amount of complex calculation and has higher precision when the system delay is shorter.
As shown in fig. 4, this scheme adds an outer loop based on a state estimator. Different closed-loop guidance periods T can be set by considering different degrees of time delay of the systemG. In a guidance period TGIn the method, an online guidance algorithm generates and updates a guidance scheme by taking the state estimation generated by the state estimator as an input quantity, and thereafter, a closed loop simulation system continuously executes the guidance scheme until the next guidance period.
Aiming at the rocket vertical recovery on-line guidance problem, the embodiment of the invention starts from a convex optimization solving algorithm, solves the rocket recovery landing guidance problem through convex optimization, and establishes a customized convex optimization solver by utilizing an interior point method, so that the guidance algorithm has real-time property. On the basis, the Guidance algorithm is packaged into a Guidance module, a closed-loop numerical simulation platform (namely a rocket recovery simulation platform) integrating Guidance, Navigation and Control (Guidance, and Control, GNC) is built, a thrust execution strategy under the conditions of environmental disturbance, engine thrust deviation, limited throttling performance and the like is provided, and a state estimation Guidance strategy for processing the time delay of a Guidance system is provided.
In the closed-loop numerical simulation platform, non-ideal factors such as environmental atmospheric disturbance, thrust execution error, navigation system error, guidance process delay and the like in the actual flight process are considered, so that the optimal flight state of the guidance module is designed under the ideal condition, and deviation occurs under the condition that many non-ideal factors are considered. Therefore, the guidance module can continuously and repeatedly modify the guidance scheme in real time according to the actual flight state input in each guidance period so as to ensure the realization of the task target, and further provides great test for the robustness of the algorithm. The following test calculation example, that is, the test, still has strong adaptability to the guidance algorithm adopted in the embodiment of the present application under the interference of these many unknown and non-ideal factors.
Correspondingly, as shown in fig. 5, an embodiment of the present application further provides a method for implementing rocket recovery simulation, including:
step 301, acquiring the actual state quantity of the rocket in each guidance period, taking the actual state quantity as an input parameter, and determining the optimal thrust information according to a dynamic model of the rocket landing section and an optimal control problem model of rocket recovery guidance.
As shown in fig. 6, in an embodiment, determining optimal thrust information according to a dynamic model of a rocket landing segment and an optimal control problem model of rocket recovery guidance by using the actual state quantity as an input parameter includes:
step 401, according to the dynamic model, performing convex processing on the optimal control problem model, and converting the optimal control problem model into a second-order cone optimization problem.
Wherein step 401 may include:
1. carrying out discrete processing and linearization processing on nonlinear kinetic equation constraints corresponding to the kinetic model;
2. the non-convex thrust amplitude is constrained to be subjected to relaxation treatment through a relaxation variable;
3. and the processing completes the lossless convex processing of the optimal control problem model into a second-order cone optimization problem.
And step 402, solving the second-order cone optimization problem based on an interior point method according to the actual state quantity to obtain the optimal thrust information.
In an embodiment, the optimal thrust information includes an optimal thrust curve, and step 402 may include:
converting the second-order cone optimization problem into a plurality of sub-problem instances, determining parameters of each sub-problem instance according to the actual state quantity, recording non-zero element information by adopting a column compression algorithm for a coefficient matrix constrained by each sub-problem instance, and performing inner point method iteration solution on each sub-problem instance to obtain the optimal thrust curve, wherein the non-zero element information is used as matrix description information during inner point method iteration solution, and only the matrix description information is modified during each inner point method iteration; wherein the non-zero element information includes: the non-zero element value, the non-zero element column index and the column first non-zero element array index.
And step 302, determining thrust execution information according to the optimal thrust information and the thrust constraint information.
In an embodiment, the optimal thrust information includes an optimal thrust curve, and the determining thrust execution information according to the optimal thrust information and thrust constraint information includes at least one of:
when the thrust constraint information is a constant thrust value in a control period, setting thrust execution information as an interpolation average of an optimal thrust curve in the current control period;
when the thrust constraint information is thrust grading, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as a thrust value corresponding to the thrust gear;
and when the thrust constraint information is thrust gear and amplitude deviation exists, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as the sum of a thrust value corresponding to the thrust gear and the amplitude deviation.
Step 303, determining a theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamic model.
In one embodiment, the flight path and the mass of the rocket are determined according to the thrust execution information, the dynamic model and the atmospheric disturbance information, and the theoretical state quantity is determined according to the flight path and the mass; the flight path comprises a flight position and a flight speed, and the atmospheric disturbance information comprises atmospheric resistance information and/or lateral wind disturbance information.
And 304, determining the actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module.
In one embodiment, step 304 includes:
setting the actual state quantity of the rocket to be equal to the theoretical state quantity; or
Setting the actual state quantity of the rocket to be equal to the theoretical state quantity plus the measurement deviation; or
Estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket to obtain a first estimated state quantity, and setting the actual state quantity of the rocket to be equal to the first estimated state quantity; or
Estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket and the measurement deviation to obtain a second estimated state quantity, and setting the actual state quantity of the rocket to be equal to the second estimated state quantity.
The estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket to obtain the first estimated state quantity may be based on the theoretical state quantity of the rocket and obtain the first estimated state quantity in a manner of integrating one guidance period by a 10-step fourth-order Runge-Kutta fixed step length.
The estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket plus the measurement deviation, and the obtaining of the second estimated state quantity may be based on the theoretical state quantity of the rocket plus the measurement deviation and obtained in a manner of integrating a guidance period by a 10-step fourth-order Runge-Kutta fixed step length.
The guidance algorithm is tested by using the rocket recovery simulation platform provided by the embodiment of the application through a plurality of test examples.
Test example 1:
the solving efficiency of the customized convex optimization solver and the general convex optimization solver is mainly tested.
Considering the rocket in a starting position r0＝[500m,1600m,0m]TInitial velocity v0＝[-5m/s,26.7m/s,0m/s]TAs the landing leg start state. Some simulation parameters are shown in table 1. Assuming that the thrust of the rocket engine has the depth adjustability, the upper limit is 235kN, the lower limit is 0kN, the specific impulse is 218s, the initial mass of the rocket is 24t, and the dry weight is 10 t.
TABLE 1 exemplary simulation parameters
Figure BDA0002296051970000201
In the simulation calculation example, the precision of flight time convergence is set to be 1E-6, the iteration precision of the controlled variable is 1E-6, and the number of discrete points is 30. The general convex optimization solver for solving the efficiency comparison adopts a CVX general solver based on an MATLAB interface, and the simulation examples run on a PC with a 3.4GHz i 74770 processor and an 8G memory (if no special description exists, the test examples in the following text are executed under the condition). The simulation results and the calculation efficiency ratio are shown in table 2 and fig. 7.
TABLE 2 general solver to custom solver efficiency comparison
Figure BDA0002296051970000202
Figure BDA0002296051970000211
From the test results, the customized convex optimization solver can reduce the solving time by two orders of magnitude on the premise of ensuring the convergence solution to be consistent with the general convex optimization solver, and the solving speed reaches millisecond order.
The rocket recovery online guidance algorithm provided by the embodiment of the application is combined with the customized solver, has higher solving efficiency than a general solver, and solves the problem that the existing guidance algorithm does not have real-time online guidance conditions.
Test example 2:
the method mainly tests the adaptability of the guidance algorithm to environmental disturbance.
The test results are shown in fig. 8, considering the atmospheric resistance during the flight of the rocket, and the lateral shear wind disturbance of 10m/s (wind power about 6 levels). Due to the influence of aerodynamic drag and lateral wind, if the rocket is executed according to a primarily planned flight guidance scheme, the actual flight trajectory of the rocket has larger lateral deviation, and the speed in the vertical direction is decelerated to 0 in advance (for example, the actual flight trajectory of atmospheric disturbance which is not corrected is considered in fig. 8); through real-time online guidance of a 200ms guidance period and a control period, a guidance scheme is continuously corrected and updated, and the final landing errors of the rocket are 3.252E-2m and 7.941E-2m/s, so that the requirement of accurate soft landing (such as the flight path after real-time online guidance correction in the figure 8) is met. Therefore, the online guidance algorithm has the capability of correcting the environmental atmospheric disturbance in real time.
Test example 3:
the method mainly tests the error adaptability of the guidance algorithm to the control system.
In the present embodiment, the error of the thrust force execution system is considered to test the influence of the execution scheme and the execution error of the thrust force of the control system on the simulation result in the actual closed-loop control framework. And still taking 200ms as a guidance period and a control period, and testing and comparing three thrust execution schemes and corresponding execution effects provided by the embodiment of the application.
1. Thrust constant value in the control period: in a closed loop control period, the thrust is considered to be not continuously variable and is fixed to a constant value. The method adopted by the embodiment is that the interpolation average of the thrust curve in the optimal guidance scheme generated in the control period is taken;
2. thrust grading: considering that the thrust of the engine can not be continuously adjusted, the thrust can only be executed according to a plurality of fixed gears of 0.2, 0.4, 0.6, 0.8, 1.0 times of maximum amplitude and the like. During execution, the method adopted by the embodiment is executed by correspondingly taking a closer thrust gear according to the interpolation average of the thrust curve in the control period;
3. the thrust is stepped and has maximum +/-5% amplitude deviation: the thrust of the engine can not be continuously adjusted and can only be executed according to a fixed gear, but the execution effect is that the amplitude deviation within +/-5% of random is considered on the basis of thrust stepping.
The thrust force execution curves and closed loop simulation results for the three cases are shown in table 3 and fig. 9.
TABLE 3 closed-loop simulation results for different thrust execution scenarios
Figure BDA0002296051970000221
From the results, the rocket can realize accurate soft landing for the three thrust execution schemes, the landing error is 1E-2 magnitude (m or m/s), and the algorithm has good adaptability to a thrust system.
Test example 4:
the method mainly tests the adaptability of the guidance algorithm to the error of the navigation system.
Suppose that a navigation system has certain measurement deviation in the rocket flight process. The absolute error and the relative error are comprehensively considered, and the following error form is adopted before the navigation module acquires the real-time state of the rocket and transmits the real-time state to the guidance module:
Figure BDA0002296051970000222
where ε represents the maximum range of relative error, rtest、vtestAnd rtrue、vtrueRespectively representing the position velocity vectors output and input by the navigation module. Different values are respectively taken in the simulation to test the adaptive boundary of the algorithm to the navigation system error, and the test result is shown in table 4.
TABLE 4 closed-loop simulation results under different navigation system errors
Figure BDA0002296051970000231
As can be seen from the results in Table 4, when the random error is less than 5%, the error of rocket landing can reach 1E-2, and when the error is increased to 10% and 15%, the landing position error and speed error are increased significantly, reaching 3.573E-1m and 4.018E-1m/s landing speed. When the error of the navigation system is large, the rocket cannot accurately judge the current position and speed, so that accurate soft landing is difficult to realize.
Test example 5:
the method mainly tests the adaptability of the guidance algorithm to the guidance module and the whole simulation platform.
As shown in fig. 4, this scheme adds an outer loop based on a state estimator. Different closed-loop guidance periods T can be set by considering different degrees of time delay of the systemG. In a guidance period TGIn the method, an online guidance algorithm generates and updates a guidance scheme by taking the state estimation generated by the state estimator as an input quantity, and thereafter, a closed loop simulation system continuously executes the guidance scheme until the next guidance period. In order to test the feasibility and the accuracy of the method, three different guidance periods are respectively set for simulation, and the simulation results are shown in table 5.
TABLE 5 comparison of closed-loop simulation results for different guidance module delays
Figure BDA0002296051970000232
It can be seen that in the case of short system delay, for example, setting the guidance period to 500ms (guidance scheme update frequency to 2Hz), the accuracy of the final landing of the rocket can be ensured, and when the guidance period is increased to 2s (guidance scheme update frequency to 0.5Hz), the landing speed error is increased accordingly, reaching 7.239E-1 m/s.
Test example 6:
the method mainly tests the effect of the guidance algorithm in consideration of the whole rocket recovery simulation platform.
The simulation settings of each module are as follows:
a guidance module: considering system delay, updating the guidance scheme according to a guidance period of 1s (namely the updating frequency is 1 Hz);
a control module: considering the execution of thrust stepping, and the amplitude deviation within +/-3 percent exists;
a rocket model module: considering the atmospheric resistance and the lateral shear wind of 5 m/s;
a navigation module: the measurement error of + -3 m (or + -3%) is considered.
Setting rocket initial state as r0＝[0m,1760m,0m]T，v0＝[0m/s,-29.3m/s,0m/s]TThe rest rocket parameters and the parameters of the guidance algorithm are set in the same table 1, and the control period is set to be 200 ms. The simulation results are shown in table 6 and fig. 10.
TABLE 6 GNC Integrated closed-loop simulation result comparison
Figure BDA0002296051970000241
The GNC integrated closed-loop numerical simulation platform constructed in the embodiment of the application comprehensively tests the robustness of the rocket recovery online guidance algorithm under various complex disturbance conditions.
According to the simulation result, the online guidance algorithm and the GNC integrated closed-loop numerical simulation platform provided by the embodiment of the application can complete accurate soft landing of a rocket landing segment by updating and correcting the guidance scheme in real time on line under the comprehensive influence of various complex disturbances such as environmental atmospheric disturbance, different control system execution schemes, navigation system deviation and system delay, and the like, and has higher algorithm robustness and accuracy.
The embodiment of the present application further provides a rocket recovery simulation platform, including: the rocket recycling simulation system comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the implementation method of the rocket recycling simulation when executing the program.
The embodiment of the application also provides a computer-readable storage medium, which stores computer-executable instructions, wherein the computer-executable instructions are used for executing the implementation method of rocket recovery simulation.
In this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

Claims (10)

1. A rocket recovery simulation platform, comprising: a guidance module, a control module, a rocket model module and a navigation module connected in a closed loop, wherein
The system comprises a guidance module, a dynamic model and an optimal control problem model, wherein the guidance module is used for acquiring the actual state quantity of a rocket in each guidance period, taking the actual state quantity as an input parameter, and determining optimal thrust information according to the dynamic model of a rocket landing section and the optimal control problem model of rocket recovery guidance;
the control module is used for determining thrust execution information according to the optimal thrust information and the thrust constraint information and transmitting the thrust execution information to the rocket model module;
the rocket model module is used for determining theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamic model;
and the navigation module is used for determining the actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module and outputting the actual state quantity to the guidance module.
2. A rocket recovery simulation platform according to claim 1,
and the guidance module is used for carrying out convex processing on the optimal control problem model according to the dynamic model, converting the optimal control problem model into a second-order cone optimization problem, and solving the second-order cone optimization problem based on an interior point method according to the actual state quantity to obtain the optimal thrust information.
3. A rocket recovery simulation platform according to claim 2,
the guidance module is used for carrying out discrete processing and linearization processing on the nonlinear dynamical equation constraint corresponding to the dynamical model, and carrying out relaxation processing on the non-convex thrust amplitude constraint through a relaxation variable, so that the optimal control problem model is subjected to lossless convex processing into a second-order cone optimization problem.
4. A rocket recovery simulation platform according to claim 2 wherein the optimal thrust information comprises an optimal thrust curve,
the guidance module is used for converting the second-order cone optimization problem into a plurality of sub-problem instances, determining parameters of each sub-problem instance according to the actual state quantity, recording non-zero element information by adopting a column compression algorithm for a coefficient matrix constrained by each sub-problem instance, and performing inner point method iteration solution on each sub-problem instance to obtain the optimal thrust curve, wherein the non-zero element information is used as matrix description information during inner point method iteration solution, and only the matrix description information is modified during each inner point method iteration;
wherein the non-zero element information includes: the non-zero element value, the non-zero element column index and the column first non-zero element array index.
5. A rocket recovery simulation platform according to claim 1 wherein said optimal thrust information comprises an optimal thrust curve, said control module for determining thrust execution information in one of the following ways:
when the thrust constraint information is a constant thrust value in a control period, setting thrust execution information as an interpolation average of an optimal thrust curve in the current control period;
when the thrust constraint information is thrust grading, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as a thrust value corresponding to the thrust gear;
and when the thrust constraint information is thrust gear and amplitude deviation exists, calculating an interpolation average of an optimal thrust curve in the current control period, determining a thrust gear closest to the interpolation average, and setting thrust execution information as the sum of a thrust value corresponding to the thrust gear and the amplitude deviation.
6. A rocket recovery simulation platform according to claim 1,
the rocket model module is used for determining the flight track and the mass of the rocket according to the thrust execution information, the dynamic model and the atmospheric disturbance information, and determining the theoretical state quantity according to the flight track and the mass;
the flight track comprises a flight position and a flight speed, and the atmospheric disturbance information comprises atmospheric resistance information and/or lateral wind disturbance information.
7. A rocket recovery simulation platform according to claim 1 wherein said navigation module is adapted to determine the actual state quantity of said rocket by one of:
setting the actual state quantity of the rocket to be equal to the theoretical state quantity;
setting the actual state quantity of the rocket to be equal to the theoretical state quantity plus the measurement deviation;
estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket to obtain a first estimated state quantity, and setting the actual state quantity of the rocket to be equal to the first estimated state quantity;
estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket and the measurement deviation to obtain a second estimated state quantity, and setting the actual state quantity of the rocket to be equal to the second estimated state quantity.
8. A rocket recovery simulation platform according to claim 7, wherein said navigation module is configured to:
when estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket, acquiring the first estimated state quantity in a mode of integrating one guidance period by a 10-step fourth-order Runge-Kutta fixed step length based on the theoretical state quantity of the rocket;
and when estimating the state quantity of the rocket after a guidance period according to the theoretical state quantity of the rocket and the measurement deviation, obtaining the second estimated state quantity according to a mode of integrating one guidance period by a fixed step length of 10 steps of fourth-order Runge-Kutta on the basis of the theoretical state quantity of the rocket and the measurement deviation.
9. A method for realizing rocket recovery simulation is characterized by comprising the following steps:
acquiring the actual state quantity of the rocket in each guidance period, taking the actual state quantity as an input parameter, and determining optimal thrust information according to a dynamic model of a rocket landing section and an optimal control problem model of rocket recovery guidance;
determining thrust execution information according to the optimal thrust information and the thrust constraint information;
determining theoretical state quantity of the rocket after a control period according to the thrust execution information and the dynamic model;
and determining the actual state quantity of the rocket according to the theoretical state quantity output by the rocket model module.
10. A rocket recovery simulation platform comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to claim 9 when executing the program.
CN201911201746.8A 2019-11-29 2019-11-29 Rocket recovery simulation platform and implementation method Active CN110955974B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911201746.8A CN110955974B (en) 2019-11-29 2019-11-29 Rocket recovery simulation platform and implementation method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911201746.8A CN110955974B (en) 2019-11-29 2019-11-29 Rocket recovery simulation platform and implementation method

Publications (2)

Publication Number Publication Date
CN110955974A true CN110955974A (en) 2020-04-03
CN110955974B CN110955974B (en) 2022-11-11

Family

ID=69978970

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911201746.8A Active CN110955974B (en) 2019-11-29 2019-11-29 Rocket recovery simulation platform and implementation method

Country Status (1)

Country Link
CN (1) CN110955974B (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112256045A (en) * 2020-05-11 2021-01-22 北京航天自动控制研究所 Rocket landing trajectory planning method and device
CN112329131A (en) * 2020-10-10 2021-02-05 中国运载火箭技术研究院 Standard test model generation method and generation device and storage medium
CN112623279A (en) * 2021-01-04 2021-04-09 清华大学 Rocket recovery method and device in transverse fixed-component control mode
CN112800546A (en) * 2021-01-29 2021-05-14 清华大学 Method and device for analyzing controllability of rocket vertical recovery state
CN113739648A (en) * 2021-08-17 2021-12-03 航天科工火箭技术有限公司 Reusable technology demonstration verification rocket landing detection method, system and equipment
CN114655474A (en) * 2022-02-15 2022-06-24 北京理工大学 Rocket cable recovery system
CN114802829A (en) * 2022-02-15 2022-07-29 北京理工大学 Accurate control rocket recovery cable system based on unscented Kalman filter
CN117234696A (en) * 2023-11-13 2023-12-15 北京控制工程研究所 Determination method and device for multitasking execution strategy of high-frequency GNC system

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012106515A (en) * 2010-11-15 2012-06-07 Mitsubishi Space Software Kk Rocket guidance computing device, rocket guidance system, rocket, rocket guidance computing program, and rocket guidance computing method of rocket guiding computing device
CN103303495A (en) * 2013-04-11 2013-09-18 北京控制工程研究所 Method for estimating disturbance moment in power decreasing process
US20150161077A1 (en) * 2013-12-05 2015-06-11 Canon Kabushiki Kaisha Solution method and solution apparatus for underdetermined system of linear equations
US20170162067A1 (en) * 2015-12-04 2017-06-08 Airbus Operations (S.A.S.) System for assisting in managing the flight of an aircraft, in particular of a transport airplane, in a landing phase on a runway
EP3073289B1 (en) * 2015-03-24 2017-10-25 Honeywell International Inc. Systems and method for ais transponder integration with ils/vor receivers
CN107748808A (en) * 2017-09-14 2018-03-02 中国运载火箭技术研究院 Reliability index distribution optimization method, system and medium based on Operations of Interva Constraint
CN108438254A (en) * 2018-04-18 2018-08-24 北京空间技术研制试验中心 Spacecraft system and dispositions method
CN109612333A (en) * 2018-11-08 2019-04-12 北京航天自动控制研究所 A kind of vision auxiliary guide system vertically recycled towards reusable rocket
CN110399652A (en) * 2019-07-05 2019-11-01 中国人民解放军国防科技大学 Spacecraft hidden maneuvering trajectory design method based on motion camouflage
CN110466804A (en) * 2019-08-30 2019-11-19 北京理工大学 The quick track optimizing method of rocket-powered decline landing mission

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012106515A (en) * 2010-11-15 2012-06-07 Mitsubishi Space Software Kk Rocket guidance computing device, rocket guidance system, rocket, rocket guidance computing program, and rocket guidance computing method of rocket guiding computing device
CN103303495A (en) * 2013-04-11 2013-09-18 北京控制工程研究所 Method for estimating disturbance moment in power decreasing process
US20150161077A1 (en) * 2013-12-05 2015-06-11 Canon Kabushiki Kaisha Solution method and solution apparatus for underdetermined system of linear equations
EP3073289B1 (en) * 2015-03-24 2017-10-25 Honeywell International Inc. Systems and method for ais transponder integration with ils/vor receivers
US20170162067A1 (en) * 2015-12-04 2017-06-08 Airbus Operations (S.A.S.) System for assisting in managing the flight of an aircraft, in particular of a transport airplane, in a landing phase on a runway
CN107748808A (en) * 2017-09-14 2018-03-02 中国运载火箭技术研究院 Reliability index distribution optimization method, system and medium based on Operations of Interva Constraint
CN108438254A (en) * 2018-04-18 2018-08-24 北京空间技术研制试验中心 Spacecraft system and dispositions method
CN109612333A (en) * 2018-11-08 2019-04-12 北京航天自动控制研究所 A kind of vision auxiliary guide system vertically recycled towards reusable rocket
CN110399652A (en) * 2019-07-05 2019-11-01 中国人民解放军国防科技大学 Spacecraft hidden maneuvering trajectory design method based on motion camouflage
CN110466804A (en) * 2019-08-30 2019-11-19 北京理工大学 The quick track optimizing method of rocket-powered decline landing mission

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
YU SONG: "Solar-sail deep space trajectory optimization using successive convex programming", 《ASTROPHYSICS AND SPACE SCIENCE 》 *
康建斌: "火箭子级垂直返回海上平台制导、导航和控制技术研究", 《导弹与航天运载技术》 *
张志国: "火箭垂直回收着陆段在线制导凸优化方法", 《弹道学报》 *

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112256045A (en) * 2020-05-11 2021-01-22 北京航天自动控制研究所 Rocket landing trajectory planning method and device
CN112329131A (en) * 2020-10-10 2021-02-05 中国运载火箭技术研究院 Standard test model generation method and generation device and storage medium
CN112329131B (en) * 2020-10-10 2024-04-05 中国运载火箭技术研究院 Standard test model generation method, generation device and storage medium
CN112623279A (en) * 2021-01-04 2021-04-09 清华大学 Rocket recovery method and device in transverse fixed-component control mode
CN112623279B (en) * 2021-01-04 2022-09-23 清华大学 Rocket recovery method and device in transverse fixed-component control mode
CN112800546B (en) * 2021-01-29 2022-07-01 清华大学 Method and device for analyzing controllability of rocket vertical recovery state
CN112800546A (en) * 2021-01-29 2021-05-14 清华大学 Method and device for analyzing controllability of rocket vertical recovery state
CN113739648A (en) * 2021-08-17 2021-12-03 航天科工火箭技术有限公司 Reusable technology demonstration verification rocket landing detection method, system and equipment
CN114655474A (en) * 2022-02-15 2022-06-24 北京理工大学 Rocket cable recovery system
CN114802829A (en) * 2022-02-15 2022-07-29 北京理工大学 Accurate control rocket recovery cable system based on unscented Kalman filter
CN114655474B (en) * 2022-02-15 2023-10-20 北京理工大学 Rocket recovery rope system
CN114802829B (en) * 2022-02-15 2023-10-20 北京理工大学 Accurate control rocket recovery cable system based on unscented Kalman filter
CN117234696A (en) * 2023-11-13 2023-12-15 北京控制工程研究所 Determination method and device for multitasking execution strategy of high-frequency GNC system
CN117234696B (en) * 2023-11-13 2024-01-19 北京控制工程研究所 Determination method and device for multitasking execution strategy of high-frequency GNC system

Also Published As

Publication number Publication date
CN110955974B (en) 2022-11-11

Similar Documents

Publication Publication Date Title
CN110955974B (en) Rocket recovery simulation platform and implementation method
CN112247992B (en) Robot feedforward torque compensation method
CN111618864A (en) Robot model prediction control method based on adaptive neural network
CN106873611A (en) A kind of method for designing of multichannel linear active disturbance rejection controller
CN103312256B (en) Networked servomotor control method based on disturbance observer
Grande et al. Experimental validation of Bayesian nonparametric adaptive control using Gaussian processes
JP2015000703A (en) Orbit control method for spacecraft and device for the same
CN115437406A (en) Aircraft reentry tracking guidance method based on reinforcement learning algorithm
JP6457927B2 (en) Meteorological data assimilation method, weather prediction method, and weather prediction system
Verhaegen et al. Aeroservoelastic modelling and control of a slender anti-air missile for active damping of longitudinal bending vibrations
Zanon et al. Direct optimal control and model predictive control
Zhu Nonlinear adaptive neural network control for a model-scaled unmanned helicopter
Lew et al. Chance-constrained optimal altitude control of a rocket
Kokunko et al. Cascade synthesis of differentiators with piecewise linear correction signals
KR102484249B1 (en) Method and apparatus for improving the accuracy of 6-dof flight simulation using flight test data
Ahmed et al. Estimator design for a subsonic rocket car (soft landing) based on state-dependent delay measurement
JP2023517142A (en) Apparatus and method for control using data-driven model adaptation
Roux et al. Scheduling schemes and control law robustness in atmospheric flight of VEGA launcher
JP2019168776A (en) Parameter identification device and method and program thereof
CN101943889A (en) The method and the device that make the electric drive system regulator start and/or move automatically
Franko et al. Mpc and lqr type controller design and comparison for an unmanned helicopter
Butt Observer based dynamic surface control of a hypersonic flight vehicle
Aditya et al. Direct adaptive stability & command augmentation of an air-breathing hypersonic vehicle
CN108241380B (en) Control method and device of high-speed unmanned aerial vehicle and high-speed unmanned aerial vehicle
Sushnigdha et al. Reentry trajectory design with pigeon inspired optimization using derived angle of attack profile

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant