Plasma virtual control surface-oriented cooperative group optimization control distribution method for reentry flight of high-speed aircraft

Abstract

The invention provides a plasma virtual control surface-oriented high-speed aircraft reentry flight cooperative group optimization control distribution method. The method comprises the following steps of: firstly, determining that an aircraft enters a reentry flight stage; secondly, the needed torque is calculated according to an attitude controller; re-constructing optimization targets of a plasma active flow control system, a pneumatic control surface control mechanism and a reverse thrust control system; then, control distribution optimization is carried out based on a collaborative group optimization algorithm; and finally, control distribution results are respectively transmitted to execution mechanisms corresponding to a plasma active flow control system, a pneumatic control surface control mechanism and a reverse thrust control system to complete reentry flight. Aiming at a plasma active flow control system, a pneumatic control surface operation system and a backstepping control system, targeted optimization targets are respectively constructed, the optimization problem is modeled by applying a collaborative group optimization algorithm, the stability of the control system can be effectively improved, and the method has a relatively good application prospect.

Description

Technical Field

[0001] The present invention relates to the field of re - entry flight control allocation of high - speed aircraft, and particularly to a cooperative swarm optimization control allocation method for high - speed aircraft re - entry flight facing a plasma virtual rudder surface. Background Technique

[0002] In the complex and extremely challenging process of high - speed aircraft re - entry flight, plasma active flow control, aileron control mechanisms, and the reverse thrust control system work together. However, since these systems each have unique performance and working principles, and have different advantages and limitations at different flight stages and operating conditions. For example, plasma active flow control may be able to effectively adjust the aircraft attitude under certain specific airflow conditions, but it has its own characteristics in terms of energy consumption and response speed; while the control accuracy and stability of the aileron control mechanism have a certain range; the reverse thrust control system is crucial in the deceleration and landing stages, but has extremely high requirements for the precise control of thrust. Therefore, to achieve safe, stable, and efficient re - entry flight of high - speed aircraft, it is necessary to reasonably allocate control tasks to each system so that they work together and complement each other's advantages, which is directly related to the success or failure of the flight mission and the overall performance and reliability of the aircraft. To achieve effective control allocation, it is necessary to establish accurate aircraft dynamics models and mathematical models of each control method. However, the complex physical phenomena during the re - entry process of high - speed aircraft, such as the interaction between plasma and airflow, the influence of high temperature on material properties, etc., make it very difficult to establish accurate models. Uncertainty factors in the models may lead to deviations in control allocation strategies, thus affecting the effect of flight control. Summary of the Invention

[0003] Aiming at the problem of how to achieve effective control allocation of plasma active flow control, aerodynamic aileron control mechanisms, and the reverse thrust control system during the re - entry flight of high - speed aircraft, the present invention proposes a cooperative swarm optimization control allocation method for high - speed aircraft re - entry flight facing a plasma virtual rudder surface. For the plasma active flow control system, aerodynamic aileron operating system, and reverse thrust control system, targeted optimization objectives are first constructed respectively, and then the cooperative swarm optimization algorithm (SSOA) is used to model the optimization problem, thereby effectively improving the stability of the control system.

[0004] The technical solution of the present invention is as follows:

[0005] The cooperative swarm optimization control allocation method for high - speed aircraft re - entry flight facing a plasma virtual rudder surface includes the following steps:

[0006] Step 1: Determine the re - entry flight stage entered by the aircraft according to the flight state parameters; the re - entry flight stage is divided into three stages: the early stage of re - entry flight, the middle stage of re - entry flight, and the late stage of re - entry flight;

[0007] Step 2: Calculate the expected moment M based on the attitude angular velocity of the aircraft;

[0008] Step 3: Construct the optimization objectives of the plasma active flow control system, the aerodynamic control surface manipulation mechanism, and the RCS thrust control system, and determine the optimization variables:

[0009] Step 4: Based on the cooperative swarm optimization algorithm, optimize the control allocation result for the optimization objective in Step 3;

[0010] Step 5: According to the control allocation result obtained in Step 4, control the corresponding actuators of the plasma active flow control system, the aerodynamic control surface manipulation mechanism, and the RCS thrust control system to complete the corresponding reentry flight phase.

[0011] Furthermore, in the early stage of reentry flight, the flight control actuator is only composed of the RCS thrust system;

[0012] In the middle stage of reentry flight, the flight control actuator is composed of three heterogeneous hybrid structures: the aerodynamic control surface, the RCS thrust control system, and the plasma active flow control system;

[0013] In the late stage of reentry flight, the flight control actuator is composed of the aerodynamic control surface and the plasma active flow control system.

[0014] Furthermore, in Step 3, the optimization objective of the plasma active flow control system is

[0015] J1 = ‖(B w w - M w )‖ + ε‖(w - w d )‖

[0016] where B w is the plasma control allocation matrix; M w is the expected moment M obtained according to Step 2 and allocated to the plasma active flow control system according to the design rules of heterogeneous mechanisms; w d is the expected plasma control power calculated according to the expected moment; ε is the weight value used to adjust the proportion between the minimum control quantity and the minimum moment error; w is the plasma control power to be optimized;

[0017] The optimization objective of the aerodynamic control surface manipulation mechanism is

[0018] J2 = ‖(B δ δ - M v )‖ + μ‖(δ - δ d )‖

[0019] where B δis the pneumatic control surface control distribution matrix; M v is the desired moment allocated to the pneumatic control surface actuator; δ d is the desired deflection angle of the pneumatic control surface under the equilibrium state; μ is the weight used to adjust the proportion between minimizing control and minimizing moment error; δ is the deflection angle of the pneumatic control surface to be optimized;

[0020] The optimization objective of the RCS reverse thrust control system is

[0021]

[0022] where, M c is the desired moment allocated to the reverse thrust control system according to the design rules of heterogeneous mechanisms, M RCS is the moment actually provided by the reverse thrust control system; λ ∈ [0, 1] is the weight used to adjust the proportion between minimizing moment deviation and minimizing control; M i is the moment actually provided by the i-th reverse thrust nozzle, p i is the output proportionality coefficient of the i-th reverse thrust nozzle to be optimized.

[0023] Furthermore, determining the optimization variables includes three parts: among them, the optimization variable parameters corresponding to the plasma active flow control system include the input power of the plasma actuators in each channel w = [wp1, … wp n T , where wp1 is the input power of the plasma actuator in the first channel of the plasma active flow control system; the optimization variable parameters corresponding to the pneumatic control surface actuator include the deflection angles of each pneumatic control surface δ = [δ1, …, δ m T , where δ1 is the deflection angle of the first pneumatic control surface in the pneumatic control surface actuator; the optimization variable parameters corresponding to the reverse thrust control system include the proportionality coefficients of each nozzle P = [p1, …, p l T , where p1 is the proportionality coefficient of the first nozzle in the reverse thrust control system; the finally determined optimization variables are U = [w; δ; P].

[0024] Furthermore, in step 4, when optimizing based on the cooperative swarm optimization algorithm, the total objective function is:

[0025] min J = w1 * J1 + w2 * J2 + w3 * J3

[0026] where, w1, w2 and w3 are the weight coefficients of J1, J2 and J3 respectively; the total objective function J is the weighted sum of the optimization objectives of the plasma active flow control system, the pneumatic control surface actuator and the RCS reverse thrust control system, and w1, w2 and w3 are determined by the reentry flight phase respectively, taking values of 0 or 1;

[0027] ​​​In the early stage of re - entry flight, the actuator consists only of the RCS reverse - thrust system. At this stage, M w and M v are 0, M c = M, w1 and w2 are 0, and w3 is 1

[0028] In the middle stage of re - entry flight, the actuator is composed of three heterogeneous hybrid structures: aerodynamic control surfaces, RCS reverse - thrust control systems, and plasma active flow control systems. M w + M v + M c = M, and w1, w2, and w3 are all 1.

[0029] In the late stage of re - entry flight, the actuator is composed of aerodynamic control surfaces and plasma active flow control systems. Then M w + M v = M, M c is 0, w1 and w2 are 1, and w3 is 0.

[0030] In addition, the present invention also provides an electronic device, including a processor and a memory for storing a program. The program includes instructions that, when executed by the processor, cause the processor to execute the above - mentioned method.

[0031] And a computer - readable storage medium for storing a program. The program includes instructions that, when executed by a processor of an electronic device, cause the electronic device to execute the above - mentioned method.

[0032] Advantageous Effects

[0033] A cooperative group optimization control allocation method for high - speed vehicle re - entry flight facing plasma virtual control surfaces proposed by the present invention constructs targeted optimization objectives for plasma active flow control, aerodynamic control surface actuators, and RCS reverse - thrust control systems respectively, and uses the cooperative swarm optimization algorithm (SSOA) to model the optimization problem, which can effectively improve the stability of the control system and has good application prospects.

[0034] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. Brief Description of the Drawings

[0035] The above - mentioned and / or additional aspects and advantages of the present invention will become apparent and be easily understood from the description of the embodiments in conjunction with the following drawings, where:

[0036] Figure 1 is the design flow chart of the present invention.

[0037] Figure 2 is the flow chart of the cooperative swarm optimization algorithm involved in the present invention

[0038] Figure 3 is the block diagram of the reentry flight phase control system of a high-speed aircraft based on the cooperative swarm optimization algorithm involved in the present invention Specific embodiments

[0039] The embodiments of the present invention will be described in detail below. The embodiments are exemplary and are intended to explain the present invention, but should not be construed as limiting the present invention.

[0040] This embodiment mainly addresses the control allocation problem of plasma active flow control, aerodynamic control surface manipulation mechanism, and RCS thrust control system during the reentry flight of a high-speed aircraft, and proposes a cooperative swarm optimization control allocation method for the reentry flight of a high-speed aircraft facing a plasma virtual control surface, as Figure 1 shown, and specifically includes the following steps:

[0041] Step 1: Determine the reentry flight phase entered by the aircraft according to the flight state parameters; the reentry flight phase is divided into three phases: the early reentry flight phase, the mid reentry flight phase, and the late reentry flight phase.

[0042] In the early reentry flight phase, the low air density results in a small dynamic pressure, and the control efficiency of the aerodynamic control surface manipulation mechanism is low. In order to stably control the initial stage of the reentry of an aerospace high-speed aircraft, the aerodynamic control surface is not used, and the flight control actuator consists only of the RCS thrust system.

[0043] In the mid reentry flight phase, the increasing dynamic pressure makes the control efficiency of the aerodynamic operation mechanism continuously increase. Therefore, in this stage, a hybrid heterogeneous actuator structure is used to perform composite control on the aircraft. At this time, the flight control actuator consists of three heterogeneous hybrid structures: an aerodynamic control surface, an RCS thrust control system, and a plasma active flow control system.

[0044] In the late reentry flight phase, the gradually increasing dynamic pressure enables the aerodynamic control surface operation mechanism to complete the control of the aerospace high-speed aircraft. Therefore, it is not necessary to perform control allocation on the RCS thrust control system. Therefore, the flight control actuator at this time consists of an aerodynamic control surface and a plasma active flow control system.

[0045] In this embodiment, taking the aircraft entering the mid reentry flight phase as an example, this stage uses an aerodynamic control surface (quantity: 8) + plasma active flow control (quantity: 2) + RCS (quantity: 8).

[0046] Step 2: Calculate the required torque according to the dynamic equation of the angular rate loop in the attitude controller of the aerospace high-speed aircraft.

[0047] Using the attitude angular velocity of the high-speed aircraft, according to the formula

[0048]

[0049] Solve for the required moment \(M\), i.e., the desired moment, where \(\omega = [p, q, r]\). T It is the representation form of the aircraft attitude angular rate vector, and \(I\) is the inertia tensor matrix.

[0050] Step 3: Construct the optimization objectives of the plasma active flow control system, the aerodynamic control surface operating mechanism, and the RCS thrust vector control system, and determine the optimization variables:

[0051] (1) Construct the optimization objective of the plasma active flow control system

[0052] \(J_1=\left\|\left(B w w - M w \right)\right\|+\varepsilon\left\|\left(w - w d \right)\right\|\)

[0053] where \(B w is the plasma control allocation matrix; \(M w is the required moment \(M\) obtained according to Step 2 and is the desired moment allocated to the plasma active flow control system according to the design rules of heterogeneous mechanisms; \(w d is the desired plasma control power calculated according to the desired moment; \(\varepsilon\) is the weight used to adjust the proportion between the minimum control quantity and the minimum moment error; \(w\) is the plasma control power to be optimized. The goal of the plasma active flow control allocation is to minimize the deviation between the allocated moment and the actually generated moment, and at the same time minimize the deviation between the plasma control power and the desired plasma control power.

[0054] (2) Construct the optimization objective of the aerodynamic control surface operating mechanism

[0055] \(J_2=\left\|\left(B δ \delta - M v \right)\right\|+\mu\left\|\left(\delta - \delta d \right)\right\|\)

[0056] where \(B δ is the aerodynamic control surface control allocation matrix; \(M v is the desired moment allocated to the aerodynamic control surface operating mechanism; \(\delta d is the desired deflection angle of the aerodynamic control surface in the equilibrium state; \(\mu\) is the weight used to adjust the proportion between the minimum control and the minimum moment error; \(\delta\) is the deflection angle of the aerodynamic control surface to be optimized. The goal of the aerodynamic control surface operating mechanism allocation is to minimize the deviation between the allocated moment and the actually generated moment, and at the same time minimize the deviation between the deflection angle of the aerodynamic control surface and the desired deflection angle of the aerodynamic control surface.

[0057] (3) Construct the optimization objective of the RCS thrust vector control system

[0058]

[0059] Among them, M c is the expected moment assigned to the reverse thrust control system according to the design rules of heterogeneous mechanisms, and M RCS is the moment actually provided by the reverse thrust control system. λ ∈ [0, 1] is the weight value used to adjust the proportion between the minimum moment deviation and the minimum control. The goal of nozzle allocation in the RCS reverse thrust control system is to minimize the deviation between the allocated moment and the actually generated moment, while minimizing the number of nozzles of the RCS reverse thrust control system used. M i is the moment actually provided by the i-th reverse thrust nozzle, and p i is the output proportionality coefficient of the i-th reverse thrust nozzle to be optimized.

[0060] (4) Determine the optimization variables

[0061] The optimization variables involved in the above three optimization problems mainly include three parts: among them, the parameters related to the plasma active flow control system include the input power w = [wp1,..., wp n T of the plasma actuators in each channel, the parameters related to the pneumatic rudder control mechanism include the deflection angles δ = [δ1,..., δ m T of each pneumatic rudder, and the parameters related to the reverse thrust control system include the proportionality coefficients P = [p1,..., p l T of each nozzle. Summarizing the above, the optimization variables are as follows:

[0062] U = [w; δ; P]

[0063] Step 4: Perform control allocation optimization based on the Synergistic Swarm Optimization Algorithm (SSOA), and the process is as follows:

[0064] (1) Set the total objective function

[0065] min J = w1 * J1 + w2 * J2 + w3 * J3

[0066] Among them, w1, w2, and w3 are the weight coefficients of J1, J2, and J3 respectively. The overall objective function J is the weighted sum of the three optimization objectives of plasma active flow control, pneumatic rudder control mechanism, and RCS reverse thrust control system, and w1, w2, and w3 are determined by the reentry flight phase and take values of 0 or 1.

[0067] In the early stage of reentry flight, since the actuator consists only of the RCS reverse thrust system, then at this stage M w and M v are 0, M c = M, w1 and w2 are 0, and w3 is 1

[0068] ​​​In the middle stage of reentry flight, the actuator consists of three heterogeneous hybrid structures: aerodynamic control surfaces, RCS thrust control system, and plasma active flow control system. Then M w +M v +M c = M, where w1, w2, and w3 are all 1.

[0069] In the later stage of reentry flight, the actuator consists of aerodynamic control surfaces and plasma active flow control system. Then M w +M v = M, M c is 0, w1 and w2 are 1, and w3 is 0.

[0070] (2) Population initialization

[0071] Taking the optimization variable U = [w; δ; P] as an individual, a certain number of individuals are randomly generated as the initial population of the algorithm.

[0072] The positions of the individuals can be randomly distributed in the solution space of the problem or initialized according to prior knowledge. Let the population size be N, the dimension of a single individual be D, and the range of each dimension of the individual be X(i,j) ∈ [X i_min , X imax , where i ∈ D and j ∈ N, and X(i,j) is the value of the i-th dimension of the j-th individual.

[0073] Then the initial population (i.e., an N*D matrix) can be expressed as follows:

[0074] X ini = rand(N,D)*(X imax - X i_min ) + X i_min

[0075] where X imax and X i_min are the maximum and minimum values of a single individual in the i-th dimension, respectively. In this embodiment, 8 aerodynamic control surfaces + 2 plasma active flow control modules + 8 RCS thrust nozzles are adopted, so the dimension of a single individual is 18.

[0076] (3) Calculation of individual fitness

[0077] In the algorithm, it is necessary to evaluate the fitness of individuals to determine the quality of individuals. Fitness evaluation is usually based on the objective function of the problem. The smaller the objective function value, the higher the fitness of the individual. Fitness evaluation is the basis for the algorithm to optimize. By continuously selecting individuals with high fitness and eliminating individuals with low fitness, the algorithm can gradually approach the optimal solution of the problem. The fitness value of the present invention is the opposite of the total objective function value.

[0078] (4) Individual update

[0079] During the iterative process, the population is gradually updated. In essence, it is the update of individuals in the population, and its update is mainly through the velocity v update which is carried out in the following calculation method:

[0080] X new (i,j) = X(i,j) + v update (i,j)

[0081] In the SSOA algorithm, the update velocity is designed, and its calculation method is as follows:

[0082] v update (i,j) = IWV + PBC + GBC + DAC + ANIC + MDC

[0083] where IWV is the component in the direction of the current inertial velocity, PBC is the individual historical best component, GBC is the current global best component, DAC is the dynamic attraction component, ANIC is the adaptive neighborhood interaction component, MDC is the diversity maintenance component, and v update (i,j) is the sum of these components. The calculation of each component is as follows:

[0084] IWV = w(t)*(1 - exp(-k*t))*v(i,j)

[0085] PBC = r1*(eps*rand(pbest) - X i )

[0086] GBC = r2*gbest t -X i

[0087]

[0088] ANIC = r4*rand(bestf) - best i

[0089]

[0090] where w(t)*(1 - exp(-k*t)) represents the calculation method of the adaptive weight, t represents the t-th iteration, k is a constant, pbest is the historical state of the individual, gbest is the global optimal state of all individuals, attract i is the optimal state within a certain neighborhood range around the individual X i bestf is the historical global optimal state of all individuals, diversity i is the individual X iThe state with the maximum fitness within a certain neighboring range. SSOA searches for the optimal solution by repeatedly executing the individual update mechanism.

[0091] (5) Termination state setting

[0092] The termination state is used to control the number of iterations of the algorithm. It can be determined by monitoring that the results of each iteration are maintained within a very small range Δ, and if this state persists for C rounds, it indicates the end of the solution.

[0093] Refer to Figure 2 And step 4, when performing control allocation based on the cooperative swarm optimization algorithm, first, the initial parameter setting work needs to be carried out. This process includes determining the population size, the number of iterations, and relevant constants in the algorithm. Subsequently, the population initialization operation is completed according to the value range of the particles in each dimension. Then, the population is updated using the velocity update rule of the SSOA algorithm. During each update process of the particles, the fitness is calculated, which means that the fitness value exists throughout the entire life cycle of the particles during the entire iteration process. Finally, by detecting whether the difference between the global optimal fitness of each iteration and the previous iteration is stable within a certain very small range, and this state persists for 100 rounds of iteration, to determine whether the algorithm terminates.

[0094] Step 5: Transmit the optimized control allocation results to the corresponding actuators of the constructed plasma active flow control system, the aerodynamic rudder surface control mechanism, and the RCS reverse thrust control system respectively to complete the reentry flight. Refer to Figure 3 First, the attitude controller calculates the required torque, and then through SSOA optimization, it outputs the rudder deflection angle of the aerodynamic rudder surface control mechanism, the power of the plasma jet exciter, and the nozzle switch state of the RCS reverse thrust control system, so as to control the attitude stability of the high-speed aircraft and achieve the full-process control in the early, middle, and late stages of the reentry flight.

[0095] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention without departing from the principles and purposes of the present invention.

Claims

1. A method for optimizing control allocation of high-speed aircraft reentry flight coordinated group for plasma virtual control surfaces, characterized in that: The following steps are involved: Step 1: Determine the reentry flight phase that the aircraft enters according to the flight state parameters; the reentry flight phase is divided into three phases: early reentry flight phase, mid reentry flight phase and late reentry flight phase; Step 2: Calculate the expected torque M according to the aircraft attitude angular velocity; Step 3: Construct the optimization objectives of the plasma active flow control system, the pneumatic control surface control mechanism, and the RCS reverse thrust control system, and determine the optimization variables: Step 4: Based on the collaborative group optimization algorithm, the optimization objective of step 3 is optimized to obtain the control allocation result; Step 5: According to the control allocation result obtained in step 4, the corresponding actuators of the plasma active flow control system, the pneumatic control surface control mechanism and the RCS reverse thrust control system are controlled to complete the corresponding reentry flight phase.

2. According to claim 1, a high-speed aircraft reentry flight collaborative group optimization control allocation method for plasma virtual control surfaces is characterized by: In the early stage of the reentry flight, the flight control actuator is composed only of the RCS reverse thrust system; In the mid-stage of the reentry flight, the flight control actuator is composed of three heterogeneous hybrid structures: aerodynamic control surfaces, RCS reverse thrust control system, and plasma active flow control system; In the later stage of the reentry flight, the flight control actuator is composed of aerodynamic control surfaces and a plasma active flow control system.

3. According to claim 2, a high-speed aircraft reentry flight collaborative group optimization control allocation method facing a plasma virtual control surface is characterized in that: In step 3, the optimization objective of the plasma active flow control system is J1=||(B w w-M w )||+ε||(w-w d )|| Among them, B w Assignment matrix for plasma control; M w is the expected torque M obtained in step 2 and allocated to the plasma active flow control system according to the design rules of heterogeneous mechanisms; w d is the expected plasma control power calculated according to the expected torque; ε is the weight used to adjust the ratio between the minimum control amount and the minimum torque error; w is the plasma control power to be optimized; The optimization goal of the pneumatic control surface mechanism is J2=||(B δ d-M v )||+μ||(d-d d )|| Among them, B δ Assignment matrix for aerodynamic control surfaces; M v is the desired torque assigned to the pneumatic control surface control mechanism; δ d is the desired deflection angle of the aerodynamic control surface in the equilibrium state; μ is the weight used to adjust the proportion between the minimum control and the minimum torque error; δ is the deflection angle of the aerodynamic control surface to be optimized; The optimization objective of the RCS thrust reverse control system is Among them, M c is the expected torque assigned to the thrust reverse control system according to the design rules of the heterogeneous mechanism, M RCS is the torque actually provided by the back thrust control system; λ∈[0,1] is the weight used to adjust the ratio between the minimum torque deviation and the minimum control; M i is the torque actually provided by the i-th thrust reverser nozzle, p i is the output proportional coefficient of the i-th reverse thrust nozzle to be optimized.

4. According to claim 3, a high-speed aircraft reentry flight collaborative group optimization control allocation method for plasma virtual control surfaces is characterized by: Determining the optimization variables includes three parts: the optimization variable parameters corresponding to the plasma active flow control system include the input power w of each channel plasma actuator = [wp1,…wp n ] T , wp1 is the input power of the first channel plasma actuator in the plasma active flow control system; the optimization variable parameters corresponding to the pneumatic control surface control mechanism include the deflection angles of each pneumatic control surface δ=[δ1,…,δ m ] T , δ1 is the deflection angle of the first pneumatic control surface in the pneumatic control surface control mechanism; the optimization variable parameters corresponding to the back-thrust control system include the proportional coefficients of each nozzle P = [p1,…,p l ] T , p1 is the proportional coefficient of the first nozzle in the back-thrust control system; the final optimization variable is U = [w; δ; P].

5. According to claim 4, a high-speed aircraft reentry flight coordinated group optimization control allocation method for plasma virtual control surfaces is characterized by: In step 4, when optimizing based on the collaborative group optimization algorithm, the overall objective function is: min J=w1*J1+w2*J2+w3*J3 Among them, w1, w2 and w3 are the weight coefficients of J1, J2 and J3 respectively; the overall objective function J is the weighted sum of the three optimization objectives of the plasma active flow control system, the aerodynamic control surface control mechanism and the RCS reverse thrust control system, and w1, w2 and w3 are determined by the reentry flight phase respectively, and the values ​​are 0 or 1; In the early stage of reentry flight, the actuator is composed only of the RCS reverse thrust system. w and M v is 0, M c =M, w1 and w2 are 0, w3 is 1 In the middle of the reentry flight, the actuator is composed of three heterogeneous hybrid structures: aerodynamic control surfaces, RCS reverse thrust control system, and plasma active flow control system. w +M v +M c =M, w1, w2 and w3 are all 1. In the later stage of reentry flight, the actuator is composed of aerodynamic control surfaces and plasma active flow control system, then M w +M v =M,M c is 0, w1 and w2 are 1, and w3 is 0.

6. An electronic device comprising a processor and a memory storing a program, wherein the program comprises instructions, characterized in that: When the instructions are executed by the processor, the processor executes the method according to any one of claims 1 to 5.

7. A computer-readable storage medium storing a program, the program comprising instructions, characterized in that: When the instructions are executed by a processor of an electronic device, the electronic device is caused to execute the method according to any one of claims 1 to 5.

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