A robust predictive control method for a variant aircraft and related equipment

Abstract

The present application relates to the technical field of variant aircraft control, and provides a robust predictive control method for variant aircraft and related equipment. The method includes: constructing the motion equation of the target variant aircraft around the center of mass, and obtaining the system attitude angle equation of the target variant aircraft based on the motion equation around the center of mass; obtaining the state variables and control variables of the target variant aircraft at multiple historical moments, and constructing a Koopman nominal model based on the Koopman theory using all state variables, all control variables, and the system attitude angle equation; constructing a linear polyhedral system of the target variant aircraft based on the Koopman nominal model; constructing a robust predictive controller of the target variant aircraft based on the linear polyhedral system; solving the robust predictive controller to obtain the optimal control quantity of the target variant aircraft, and controlling the target variant aircraft according to the optimal control quantity. The method of the present application can improve the accuracy of controlling the variant aircraft.

Description

Technical Field

[0001] The present application relates to the technical field of variant aircraft control, and in particular to a robust predictive control method for a variant aircraft and related equipment. Background Art

[0002] High-speed morphing vehicles, as a hybrid product of advanced aviation and aerospace technologies, boast a large operational airspace, rapid response, excellent maneuverability, strong penetration capability, high accuracy, and superior lethality. Furthermore, morphing vehicles can achieve a more optimal aerodynamic shape by changing their aerodynamic configuration, thereby improving their aerodynamic characteristics and achieving greater environmental adaptability and flight capabilities. However, these changes in aerodynamic characteristics pose new challenges to the design of attitude control systems. First, the deformation process is difficult to model, making it difficult to accurately obtain a fully accurate system model of the vehicle. Second, the vehicle system exhibits strong nonlinear and tightly coupled characteristics during deformation, making it difficult to achieve precise control using traditional control methods. Third, the deformation process also presents internal system disturbances, making control more challenging in the presence of system model parameter deviations. Therefore, designing a robust controller that ensures high attitude tracking control stability, robustness, and control accuracy is crucial, ensuring that the vehicle accurately and rapidly tracks given attitude control commands to achieve long-duration, long-distance, and highly maneuverable high-speed gliding flight.

[0003] Existing control methods for high-speed morphing aircraft rely on the mechanism model of the controlled object. For example, a high-speed morphing aircraft anti-interference control method based on fixed-time convergence requires the design of its controller to construct an attitude motion and aerodynamic model based on the geometric model of the high-speed morphing aircraft, and then convert it into a control-oriented attitude control model. A method for predictive time control of a morphing aircraft with limited state error is similar to the above method. Its controller design still needs to rely on the mechanism model of the controlled object, that is, to construct an attitude motion and aerodynamic control model based on the geometric model of the high-speed morphing aircraft, and then convert it into a control-oriented control model.

[0004] The drawback of existing technologies is that traditional control methods can design stable, high-precision attitude controllers for high-speed morphing aircraft, but they rely on precise dynamic and kinematic models of the system. However, the dynamic models of high-speed morphing aircraft often exhibit parameter deviations, such as aerodynamic parameters. These parameter deviations pose new challenges to the performance of controllers designed using traditional control methods, resulting in low control accuracy for morphing aircraft. Summary of the Invention

[0005] The present application provides a robust predictive control method for a variant aircraft and related equipment, which can solve the problem of low accuracy in controlling a variant aircraft.

[0006] In a first aspect, an embodiment of the present application provides a robust predictive control method for a morphing aircraft, the robust predictive control method for a morphing aircraft comprising:

[0007] Constructing a motion equation of the target variant aircraft around the center of mass, and obtaining a system attitude angle equation of the target variant aircraft based on the motion equation around the center of mass;

[0008] The state variables and control variables of the target variant aircraft at multiple historical moments are obtained. Based on the Koopman theory, a Koopman nominal model is constructed using all state variables, all control variables, and the system attitude angle equations. The Koopman nominal model is used to describe the relationship between the state variables and control variables of the target variant aircraft.

[0009] A linear polyhedral system of a target variant aircraft is constructed based on the Koopman nominal model; the linear polyhedral system includes the Koopman nominal model and the constraints of the Koopman nominal model;

[0010] A robust predictive controller for a target variant aircraft is constructed based on a linear polyhedral system. The robust predictive controller is used to describe the relationship between the reference state variables, state variables, and control variables of the target variant aircraft.

[0011] The robust predictive controller is solved to obtain the optimal control quantity of the target variant aircraft, and the target variant aircraft is controlled according to the optimal control quantity.

[0012] Optionally, the equation of motion about the center of mass is:

[0013]

[0014] Among them, α represents the attitude angle vector of the first body axis, represents the derivative of the attitude angle vector of the first body axis, β represents the attitude angle vector of the second body axis, represents the derivative of the attitude angle vector of the second body axis, σ represents the attitude angle vector of the third body axis, Derivative of the attitude angle vector about the third body axis, ω x Represents the attitude angular velocity vector of the first body axis, ω y Represents the attitude angular velocity vector of the second body axis, ω z represents the attitude angular velocity vector of the third body axis, represents the derivative of the attitude angular velocity vector about the first body axis, represents the derivative of the attitude angular velocity vector about the second body axis, represents the derivative of the attitude angular velocity vector of the third body axis, m represents the mass of the target variant aircraft, V represents the speed of the target variant aircraft, θ represents the track inclination angle of the target variant aircraft, Y tZ is the component of the sum of the nominal aerodynamic force and the additional force in the geocentric coordinate system perpendicular to the plane formed by the prime meridian and the axis pointing to the North Pole. t It represents the component of the sum of the nominal aerodynamic force and the additional force pointing to the North Pole in the geocentric coordinate system under no deformation, g represents the acceleration due to gravity, and I xx The rotational inertia of the target variant aircraft around the first body axis, I yy I represents the rotational inertia of the target variant aircraft around the second body axis, zz I represents the rotational inertia of the target variant aircraft around the third body axis, xy represents the product of inertia, M tx represents the resultant moment about the first body axis, M ty represents the resultant moment about the second body axis, M tz represents the resultant moment about the third body axis.

[0015] Optionally, the system attitude angle equation is:

[0016]

[0017] Among them, x1 represents the attitude angle state variable, Represents the derivative of the attitude angle state variable, x1=[ɑ,β,σ] T , x2 represents the attitude angular velocity state variable, Represents the derivative of the attitude angular velocity state variable, x2=[ω x ,ω y ,ω z ] T , M t (u) represents the net external torque, d represents the external disturbance, R represents the matrix of the attitude angle vector, I represents the matrix of the rotational inertia, ω × A matrix representing the attitude angular velocity vector.

[0018] Optionally, a Koopman nominal model is constructed using all state variables, all control variables, and the system attitude angle equations, including:

[0019] Based on the Koopman theory, the system optimization problem is defined using all state variables and all control variables;

[0020] Solve the system optimization problem and obtain the solution;

[0021] The Koopman nominal model is constructed based on the solution results and the system attitude angle equation.

[0022] Optionally, the system optimization problem is:

[0023]

[0024] Where K = [A, B], K represents the approximate Koopman operator, A represents the approximate linear system matrix, B represents the approximate linear control matrix, J(D) represents the objective function for minimizing the truncation error of the approximate Koopman operator, C represents the inverse projection matrix of the approximate Koopman operator, Θ f represents the backward data set that satisfies the state equation under the Koopman dimensionality-increasing mapping, Θ p represents the forward data set that satisfies the state equation under the Koopman dimensionality-increasing mapping, Represents the data set obtained by splicing and collecting state variables and control variables under the dimensionality-increasing mapping:

[0025]

[0026] Where X represents all state variables, x(1) represents the state variable at the first historical moment, x(2) represents the state variable at the second historical moment, x(N) represents the state variable at the Nth historical moment, Ψ(x) represents the observed value of the state variable x under the Koopman dimensionality-increasing mapping, x∈{x(1),...,x(N)}, ψ1(x) represents the first element in the observed value, and ψ2(x) represents the second element in the observed value. Indicates the Nth observation k elements, ψ(x(1)) represents the observed value of the state variable x(1) under the Koopman dimensionality increase mapping, Ψ(x(2)) represents the observed value of the state variable x(2) under the Koopman dimensionality increase mapping, Ψ(x(B)) represents the observed value of the state variable x(N) under the Koopman dimensionality increase mapping, Ψ(x(N+1)) represents the observed value of the state variable x(N+1) under the Koopman dimensionality increase mapping, a (x,u)=[Ψ(x) T u T ] T ,Ψ a (x(1),u(1)) represents the concatenation of the observed value of the state variable x(1) under the Koopman dimensionality-increasing mapping and the observed value of the control variable at the first historical moment, u∈{u(1),...,u(N)}, u(1) represents the control variable at the first historical moment, u(N) represents the control variable at the Nth historical moment, n represents the original state dimension, m represents the control input dimension, N K Indicates the dimension of the upgraded state.

[0027] Optionally, the solution is:

[0028]

[0029] The Koopman nominal model is:

[0030] Ψ(x(k+1))=AΨ(x(k))+Bu(k)

[0031] x k =CΨ(x k )

[0032] Among them, Ψ(x(k+1)) represents the observed value of the state variable x(k+1) under the Koopman dimensionality-increasing mapping, Ψ(x(k)) represents the observed value of the state variable x(k) under the Koopman dimensionality-increasing mapping, x(k) represents the state variable at the current moment, x(k+1) represents the state variable at the next moment of the current moment, and u(k) represents the control variable at the current moment.

[0033] Alternatively, the linear polytopic system is:

[0034] Ψ(x(k+1))=A k Ψ(x(k))+B k u k ,

[0035] x(k)=CΨ(x(k)),

[0036]

[0037] Among them, A k Represents the approximate linear system matrix at the current moment, B k Represents the approximate linear control matrix at the current moment, A i Represents the system matrix of the ith vertex of the polytopic set, B i represents the control matrix of the ith vertex of the polytopic set, λ i represents the scheduling value of the ith vertex of the polytopic set, Represents the total number of vertices of the polytopic set.

[0038] Optionally, the robust predictive controller is:

[0039]

[0040] Among them, γ, Y, Q0 are all optimization variables, A j The system matrix of the j-th vertex of the polytopic set, B j represents the control matrix of the j-th vertex of the polytopic set, represents the state weight matrix, represents the control weight matrix, I n represents the unit matrix of dimension n, represents the intermediate matrix variables that satisfy the control constraints, express Matrix diagonal elements, u j,max represents the constraint limit value of the jth control variable, m represents the control variable dimension, x max represents the state constraint limit value, Ψ(xr ) represents the state variable x r The observed value under the Koopman dimensionality increase mapping, x r represents the reference state variable, and Ψ(x(k)) represents the observed value of the state variable x(k) under the Koopman dimensionality-increasing mapping.

[0041] Optionally, the optimal control quantity is:

[0042]

[0043] in, Indicates the optimal rudder angle control amount.

[0044] In a second aspect, an embodiment of the present application provides a robust predictive control device for a variant aircraft, comprising:

[0045] The first construction module is used to construct a motion equation of the target variant aircraft around the center of mass, and obtain a system attitude angle equation of the target variant aircraft based on the motion equation around the center of mass;

[0046] An acquisition module is used to obtain the state variables and control variables of the target variant aircraft at multiple historical moments, and based on the Koopman theory, uses all state variables, all control variables, and the system attitude angle equation to construct a Koopman nominal model; the Koopman nominal model is used to describe the relationship between the state variables and control variables of the target variant aircraft;

[0047] A second building module is used to build a linear polyhedral system of the target variant aircraft based on the Koopman nominal model; the linear polyhedral system includes the Koopman nominal model and the constraints of the Koopman nominal model;

[0048] The third building block is used to construct a robust predictive controller for the target variant aircraft based on the linear polyhedral system; the robust predictive controller is used to describe the relationship between the reference state variable, state variable and control variable of the target variant aircraft;

[0049] The solution module is used to solve the robust predictive controller to obtain the optimal control quantity of the target variant aircraft and control the target variant aircraft according to the optimal control quantity.

[0050] In a third aspect, an embodiment of the present application provides a terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned robust predictive control method for variant aircraft when executing the above-mentioned computer program.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned robust predictive control method for variant aircraft.

[0052] The above solution of the present application has the following beneficial effects:

[0053] In an embodiment of the present application, the motion equation of the target variant aircraft around the center of mass is constructed, and the system attitude angle equation of the target variant aircraft is obtained based on the motion equation around the center of mass, and then the state variables and control variables of the target variant aircraft at multiple historical moments are obtained, and based on the Koopman theory, a Koopman nominal model is constructed using all state variables, all control variables, and the system attitude angle equation, and then a linear polyhedral system of the target variant aircraft is constructed based on the Koopman nominal model, and then a robust predictive controller of the target variant aircraft is constructed based on the linear polyhedral system, and finally the robust predictive controller is solved to obtain the optimal control quantity of the target variant aircraft, and the target variant aircraft is controlled according to the optimal control quantity. Among them, the state variables, control variables and system attitude angle equations of the target variant aircraft are used to construct a Koopman nominal model, and a linear polyhedral system is constructed based on the Koopman nominal model, so that the strongly coupled and strongly nonlinear control system is converted into a linear control system, which improves the solvability and robustness of predictive control of the target variant aircraft. Even if there are deviations in the identification of relevant parameters, the optimal control quantity can still be provided for the target variant aircraft, thereby improving the accuracy, stability and robustness of the control of the variant aircraft, ensuring that the variant aircraft accurately and quickly tracks the given reference attitude to achieve long-term, long-distance, large-maneuver high-speed gliding flight.

[0054] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] Figure 1 A flowchart of a robust predictive control method for a variant aircraft provided in one embodiment of the present application;

[0057] Figure 2 A schematic diagram of a first attitude angle curve provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of a first attitude angle error curve provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of a first rudder deflection angle curve provided in one embodiment of the present application;

[0060] Figure 5 A schematic diagram of a second attitude angle curve provided in an embodiment of the present application;

[0061] Figure 6 A schematic diagram of a second attitude angle error curve provided in an embodiment of the present application;

[0062] Figure 7 A schematic diagram of a second rudder deflection angle curve provided in one embodiment of the present application;

[0063] Figure 8 A schematic diagram of the structure of a robust predictive control device for a variant aircraft provided in one embodiment of the present application;

[0064] Figure 9 A schematic diagram of the structure of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0065] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0066] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0067] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0068] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0069] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0070] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0071] In response to the problem of low accuracy in existing control of variant aircraft, an embodiment of the present application provides a robust predictive control method for variant aircraft. The robust predictive control method for variant aircraft constructs the motion equation of the target variant aircraft around the center of mass, and obtains the system attitude angle equation of the target variant aircraft based on the motion equation around the center of mass, then obtains the state variables and control variables of the target variant aircraft at multiple historical moments, and based on the Koopman theory, constructs a Koopman nominal model using all state variables, all control variables, and the system attitude angle equation, and then constructs a linear polyhedral system of the target variant aircraft based on the Koopman nominal model, and then constructs a robust predictive controller of the target variant aircraft based on the linear polyhedral system, and finally solves the robust predictive controller to obtain the optimal control quantity of the target variant aircraft, and controls the target variant aircraft according to the optimal control quantity. Among them, the state variables, control variables and system attitude angle equations of the target variant aircraft are used to construct a Koopman nominal model, and a linear polyhedral system is constructed based on the Koopman nominal model, so that the strongly coupled and strongly nonlinear control system is converted into a linear control system, which improves the solvability and robustness of predictive control of the target variant aircraft. Even if there are deviations in the identification of relevant parameters, the optimal control quantity can still be provided for the target variant aircraft, thereby improving the accuracy, stability and robustness of the control of the variant aircraft, ensuring that the variant aircraft accurately and quickly tracks the given reference attitude to achieve long-term, long-distance, large-maneuver high-speed gliding flight.

[0072] Next, the robust predictive control method for variant aircraft provided in this application is exemplified.

[0073] like Figure 1 As shown, the robust predictive control method for a variant aircraft provided in this application includes the following steps:

[0074] Step 11: construct the motion equation of the target variant aircraft around the center of mass, and obtain the system attitude angle equation of the target variant aircraft based on the motion equation around the center of mass.

[0075] The target variant aircraft is the one that needs to be controlled. The equation of motion about the center of mass is used to describe the state of the target variant aircraft during flight, and the system attitude angle equation is used to describe the attitude angle state and attitude angular velocity of the target variant aircraft during flight.

[0076] The above equation of motion around the center of mass is:

[0077]

[0078] Among them, α represents the attitude angle vector of the first body axis, represents the derivative of the attitude angle vector of the first body axis, β represents the attitude angle vector of the second body axis, represents the derivative of the attitude angle vector of the second body axis, σ represents the attitude angle vector of the third body axis, Derivative of the attitude angle vector about the third body axis, ω x Represents the attitude angular velocity vector of the first body axis, ω y Represents the attitude angular velocity vector of the second body axis, ω z represents the attitude angular velocity vector of the third body axis, represents the derivative of the attitude angular velocity vector about the first body axis, represents the derivative of the attitude angular velocity vector about the second body axis, represents the derivative of the attitude angular velocity vector of the third body axis, m represents the mass of the target variant aircraft, V represents the speed of the target variant aircraft, θ represents the track inclination angle of the target variant aircraft, Y t Z is the component of the sum of the nominal aerodynamic force and the additional force in the geocentric coordinate system perpendicular to the plane formed by the prime meridian and the axis pointing to the North Pole. t It represents the component of the sum of the nominal aerodynamic force and the additional force pointing to the North Pole in the geocentric coordinate system under no deformation, g represents the acceleration due to gravity, and I xx The rotational inertia of the target variant aircraft around the first body axis, I yy I represents the rotational inertia of the target variant aircraft around the second body axis, zz I represents the rotational inertia of the target variant aircraft around the third body axis, xy represents the product of inertia, M tx represents the resultant moment about the first body axis, M ty represents the resultant moment about the second body axis, M tz represents the resultant moment about the third body axis.

[0079] The attitude angle equation of the above system is:

[0080]

[0081] Among them, x1 represents the attitude angle state variable, Represents the derivative of the attitude angle state variable, x1=[α,β,σ] T , x2 represents the attitude angular velocity state variable, Represents the derivative of the attitude angular velocity state variable, x2=[ω x ,ω y ,ω z ] T , M t (u) represents the net external torque, d represents the external disturbance, R represents the matrix of the attitude angle vector, I represents the matrix of the rotational inertia, ω × The matrix representing the attitude angular velocity vector:

[0082]

[0083] It should be noted that the total external torque can be expressed as:

[0084]

[0085] Among them, Q A Indicates dynamic pressure, S r Indicates the reference area, L r represents the reference aerodynamic span, Ma=[1,Ma] T , δ f =[1,δ f ] T , α=[1,α] T , Ma represents the Mach number, δ f represents the folding angle, represents aerodynamic parameters, Both represent the aerodynamic vector coefficient matrix.

[0086] It is worth mentioning that by constructing the motion equation around the center of mass and the system attitude angle equation, the condition of the target variant aircraft during flight can be described, reflecting the relationship between the various parameters of the target variant aircraft.

[0087] Step 12: Obtain the state variables and control variables of the target variant aircraft at multiple historical moments, and construct a Koopman nominal model based on the Koopman theory using all state variables, all control variables, and system attitude angle equations.

[0088] The Koopman nominal model is used to describe the relationship between the state variables and control variables of the target variant aircraft. The state variables include attitude angular velocity state variables and track inclination, and the control variables include rudder angle control variables.

[0089] In some embodiments of the present application, the state variables and control variables of the target variant aircraft at multiple historical moments can be obtained by accessing the control system of the target variant aircraft. The above steps of constructing the Koopman nominal model based on the Koopman theory using all state variables, all control variables, and the system attitude angle equation are as follows:

[0090] In the first step, based on the Koopman theory, the system optimization problem is defined using all state variables and all control variables.

[0091] Specifically, the system optimization problem is:

[0092]

[0093] Where K = [A, B], K represents the approximate Koopman operator, A represents the approximate linear system matrix, B represents the approximate linear control matrix, J(D) represents the objective function for minimizing the truncation error of the approximate Koopman operator, C represents the inverse projection matrix of the approximate Koopman operator, Θ f represents the backward data set that satisfies the state equation under the Koopman dimensionality-increasing mapping, Θ p represents the forward data set that satisfies the state equation under the Koopman dimensionality-increasing mapping, Represents the data set obtained by splicing and collecting state variables and control variables under the dimensionality-increasing mapping:

[0094]

[0095] Where X represents all state variables, x(1) represents the state variable at the first historical moment, x(2) represents the state variable at the second historical moment, x(N) represents the state variable at the Nth historical moment, Ψ(x) represents the observed value of the state variable x under the Koopman dimensionality-increasing mapping, x∈{x(1),...,x(N)}, Ψ1(x) represents the first element in the observed value, and Ψ2(x) represents the second element in the observed value. Indicates the Nth observation k elements, Ψ(x(1)) represents the observed value of the state variable x(1) under the Koopman dimensionality increase mapping, Ψ(x(2)) represents the observed value of the state variable x(2) under the Koopman dimensionality increase mapping, Ψ(x(N)) represents the observed value of the state variable x(N) under the Koopman dimensionality increase mapping, Ψ(x(N+1)) represents the observed value of the state variable x(N+1) under the Koopman dimensionality increase mapping, a (x,u)=[Ψ(x) T u T ] T ,Ψ a(x(1),u(1)) represents the concatenation of the observed value of the state variable x(1) under the Koopman dimensionality-increasing mapping and the observed value of the control variable at the first historical moment, u∈{u(1),...,u(N)}, u1 represents the control variable at the first historical moment, u N represents the control variable at the Nth historical moment, n represents the original state dimension, m represents the control input dimension, N K Indicates the dimension of the upgraded state.

[0096] The above x=[θ,ω] T , θ represents the track inclination angle of the target variant aircraft, ω represents the attitude angular velocity state variable, and u is the control variable, that is, the rudder angle control variable.

[0097] The second step is to solve the system optimization problem and obtain the solution.

[0098] For example, a nonlinear programming algorithm, a simulated annealing algorithm, etc. may be used to solve the system optimization problem and obtain a solution.

[0099] Specifically, the solution is:

[0100]

[0101] The third step is to construct the Koopman nominal model based on the solution results and the system attitude angle equation.

[0102] Specifically, the Koopman nominal model is:

[0103] Ψ(x(k+1))=AΨ(x(k))+Bu k

[0104] x(k)=CΨ(x(k))

[0105] Among them, Ψ(x(k+1)) represents the observed value of the state variable x(k+1) under the Koopman dimensionality-increasing mapping, Ψ(x(k)) represents the observed value of the state variable x(k) under the Koopman dimensionality-increasing mapping, x(k) represents the state variable at the current moment, x(k+1) represents the state variable at the next moment of the current moment, and u(k) represents the control variable at the current moment.

[0106] It should be noted that by substituting the solution results into the system attitude angle equation, the dynamics of the system attitude angle equation in the increased dimensional space is written out, and the Koopman nominal model is obtained.

[0107] Step 13: Construct a linear polyhedral system of the target variant aircraft based on the Koopman nominal model.

[0108] The above linear polytopic system includes a Koopman nominal model and constraints of the Koopman nominal model.

[0109] In some embodiments of the present application, in order to solve the linear polyhedral system of the target variant aircraft, it is necessary to first solve different Koopman models. These different Koopman models are regarded as the various undetermined vertices of the polyhedral linear system of the high-speed variant aircraft. Considering the collection of different data sets, the initial values ​​of the attitude angle and attitude angular velocity, as well as the aircraft rudder deflection angle excitation sequence, are reselected. For convenience, different data sets are defined as Among them, D1 represents the data set (state variables and control variables) of the first forward open-loop experiment, and D2 represents the data set of the second forward open-loop experiment. Indicates the Nth d The dataset of the forward open-loop experiment is used to solve the minimization optimization problem J(D1), J(D2), ..., J(D Nd ), J(D1) represents the optimization problem of the first data set (state variables and control variables), and J(D2) represents the optimization problem of the second data set. Indicates the Nth d The optimization problem of a data set is obtained by [A1, B1] represents the pending vertex corresponding to the first historical moment, and [A2, B2] represents the pending vertex corresponding to the first historical moment. represents the pending vertex corresponding to the first historical moment. Next, we need to introduce the following steps to transform the set of pending vertices from linear combination to cone combination, from cone combination to convex combination, and add zero matrix vertex operations to increase the uncertainty that the convex hull Ω(Vert) can represent:

[0110] (1) Linear-cone combination transformation:

[0111] Create a new vertex set The current pending vertex set and the new vertex set are taken as the union, and the vertex set after the union is taken as the current vertex set, which is expressed as The above operations can be regarded as the operation of converting the original vertex linear combination into the current vertex cone combination.

[0112] (2) Cone-convex combination conversion:

[0113] Take the given coefficient η=[η1,η2,...,η 2Nd ],η i ≥0,i=1,..,2N d , perform the following operations on the pending vertex set:

[0114]

[0115] When η > 0, the set of pending vertices is expanded, resulting in a larger number of matrices that can be represented by the convex combination of the pending vertices, and a higher degree of uncertainty. When η = 0, no operation is performed on the set of pending vertices. The above formula can be simply replaced by Vert←(1+η)⊙Vert; when η → ∞, the above operation can be viewed as converting the vertex cone combination after the linear combination-cone combination conversion to the current vertex convex combination.

[0116] (3) Add zero matrix vertex:

[0117] Define the matrix And add it to the set of pending vertices to get the set: Let the first vertices, and the final set of pending vertices is obtained. Based on the final set of pending vertices, the linear polytopic system of the aircraft can be established. Specifically, the linear polytopic system is:

[0118] Ψ(x(k+1))=A k Ψ(x(k))+B k u k ,

[0119] x(k)=CΨ(x(k)),

[0120]

[0121] Among them, A k Represents the approximate linear system matrix at the current moment, B k Represents the approximate linear control matrix at the current moment, A i Represents the system matrix of the ith vertex of the polytopic set, B i represents the control matrix of the ith vertex of the polytopic set, λ i represents the scheduling value of the ith vertex of the polytopic set, Indicates the total number of vertices in the polytopic set. st indicates the constraint.

[0122] Step 14: construct a robust predictive controller for the target variant aircraft based on the linear polyhedral system.

[0123] The above robust predictive controller is used to describe the relationship between the reference state variables, state variables and control variables of the target variant aircraft.

[0124] The above-mentioned reference state variables include reference angular velocity state variables and reference track inclination angles, the state variables include attitude angular velocity state variables and track inclination angles, and the control variables include rudder angle control variables.

[0125] Specifically, based on the linear polyhedral system established by the final set of pending vertices, a predictive control method for a variant aircraft is designed under the condition of maximum deviation, which can be described as the following quadratic programming form:

[0126]

[0127] Set the control time domain and prediction time domain to be infinite, that is, N p =N c =∞, then the above equation can be written as a semi-positive definite convex optimization problem with linear matrix inequality constraints, namely, a robust predictive controller:

[0128]

[0129] Among them, γ, Y, Q0 are all optimization variables, A j The system matrix of the j-th vertex of the polytopic set, B j represents the control matrix of the j-th vertex of the polytopic set, represents the state weight matrix, represents the control weight matrix, I n represents the unit matrix of dimension n, represents the intermediate matrix variables that satisfy the control constraints, express Matrix diagonal elements, u j,max represents the constraint limit value of the jth control variable, m represents the control variable dimension, x max represents the state constraint limit value, Ψ(x r ) represents the state variable x r The observed value under the Koopman dimensionality increase mapping, x r represents the reference state variable, and Ψ(x(k)) represents the observed value of the state variable x(k) under the Koopman dimensionality-increasing mapping.

[0130] It is worth mentioning that a linear polyhedral system is constructed based on the Koopman nominal model, and then a robust predictive controller is constructed from the linear polyhedral system, which converts the strongly coupled and strongly nonlinear control system into a linear control system, thereby improving the ease of solution and robustness of predictive control of the target variant aircraft.

[0131] Step 15: Solve the robust predictive controller to obtain the optimal control quantity of the target variant aircraft, and control the target variant aircraft according to the optimal control quantity.

[0132] The above optimal control quantity is the optimal value of the control variable (rudder angle control quantity) obtained by solving the robust predictive controller.

[0133] Specifically, the optimal control quantity is:

[0134]

[0135] in, Represents the optimal rudder angle control quantity, Y and Q0 are the variable parameters of the robust predictive controller optimization problem, by defining the parameters to be solved Y, Q0, γ, the intermediate matrix variable Input Ψ(x(k)), Ψ(x r ), x(k), and the state weight matrix Control weight matrix The robust predictive controller is solved by convex optimization method and other algorithms to obtain the values ​​of the above variable parameters, and then the optimal control quantity is calculated by the above expression.

[0136] Then, the optimal rudder deflection angle control value is input into the control system of the target variant aircraft, and the target variant aircraft is controlled so that the target variant aircraft reaches the reference state variable at the next moment.

[0137] For example, the current moment may be 6 o'clock, and the historical moments may be 5 o'clock, 5:30, etc. The next moment of the current moment is 6:30. In order to make the target variant aircraft reach the reference state variable at 6:30, the optimal control quantity at the current moment is calculated through the above steps, and the target variant aircraft at the current moment is controlled based on the optimal control quantity.

[0138] It should be noted that in actual applications, in order to ensure the continuous control of the variant aircraft, after solving the optimal control quantity at the current moment, the next moment after the current moment is taken as the current moment, and the expression of the above optimal control quantity is used to continue to obtain the optimal control quantity at the next moment.

[0139] It is worth mentioning that since the trajectory tracking predictive control problem of a strongly nonlinear control system is usually a non-convex optimization problem, the trajectory tracking predictive control problem of a polyhedral linear model is a strictly convex optimization problem, and convex optimization problems are easier to solve than non-convex optimization problems. The state variables, control variables and system attitude angle equations of the target variant aircraft are used to construct a Koopman nominal model, and a linear polyhedral system is constructed based on the Koopman nominal model, so that the strongly coupled and strongly nonlinear control system is converted into a linear control system, thereby improving the solvability and robustness of the predictive control of the target variant aircraft. Even if there is a deviation in the identification of relevant parameters, the optimal control quantity can still be provided for the target variant aircraft, thereby improving the accuracy, stability and robustness of the control of the variant aircraft, and ensuring that the variant aircraft accurately and quickly tracks the given reference attitude to achieve long-term, long-distance, large-maneuver high-speed gliding flight.

[0140] In addition, this application constructs an approximate Koopman operator linear model (i.e., Koopman nominal model) by collecting information on the aircraft's three-axis attitude angle, angular velocity, and rudder deflection angle. Based on the Koopman operator linear model, a linear polyhedral system of the variant aircraft is constructed to represent the pending vertices. The pending vertices are transformed from linear combination to cone combination, from cone combination to convex combination, and zero matrix vertex operations are added to obtain a new vertex set to construct a robust predictive controller and solve the optimal control quantity that meets the constraints.

[0141] This application adopts a data-driven modeling method based on the Koopman operator to model high-speed variant aircraft, without the need to obtain the specific dynamic form of the variant aircraft a priori; in addition, since this application uses the form of a linear polyhedral system for modeling, the uncertainty of the model is concentrated in the convex combination of the linear polyhedral system, which can be handled in a targeted manner by designing a robust controller.

[0142] This application designs a data-driven robust predictive controller to solve the optimal control quantity under the condition of considering constraints, realize high-precision tracking control of the attitude angle curve, and improve the robustness of the control system.

[0143] The method of the present application is illustrated below with reference to a specific example.

[0144] The initial working condition settings for the forward simulation of the variant aircraft when collecting data sets are shown in Table 1:

[0145] Initial state Data Collection Forward Simulation Height (m) 60000 Longitude (°) 0 Latitude (°) 0 Speed ​​(m / s) 6488 Track angle (°) 0 Heading angle (°) 0

[0146] Table 1

[0147] The external disturbance torque is defined as:

[0148]

[0149] Wherein, Δd1 represents the external disturbance moment of roll, Δd2 represents the external disturbance moment of yaw, and Δd3 represents the external disturbance moment of pitch.

[0150] The uncertainty of aerodynamic parameters is:

[0151] ΔC mx =ΔC my =ΔC mz = ±50%

[0152] Where, ΔC mx Indicates the percentage deviation of the rolling moment coefficient, ΔC my Indicates the percentage deviation of the pitch moment coefficient, ΔC mz Indicates the yaw moment coefficient deviation percentage.

[0153] Based on the above data, a single-point attitude closed-loop control simulation experiment was conducted on the variant aircraft:

[0154] Target attitude angle definition:

[0155]

[0156] Among them, α d represents the reference posture angle vector of the first body axis, β d represents the reference posture angle vector of the second body axis, σ d A vector representing the reference pose angle of the third body axis.

[0157] Equivalent rudder angle constraint of high-speed morphing aircraft:

[0158] δ x ∈[-50°,50°],δ y ∈[-50°,50°],δ z ∈[-50°,50°]

[0159] Among them, δ x Denotes the roll equivalent rudder angle, δ y Denotes the equivalent rudder angle of yaw, δ z Indicates the pitch equivalent rudder angle.

[0160] The initial working condition settings for the single-point attitude control simulation of the high-speed variant aircraft are shown in Table 2:

[0161]

[0162]

[0163] Table 2

[0164] The controller parameters are shown in Table 3:

[0165]

[0166] Table 3

[0167] The attitude angle tracking curve of the variant aircraft is obtained as follows: Figure 2 As shown in the figure, the horizontal axis represents the flight time in seconds (s), the vertical axis represents the angle in degrees (°), the solid line represents the actual attitude angle curve of the variant aircraft, and the dotted line represents the command attitude angle curve of the variant aircraft (that is, the command given by the control system when the optimal control amount calculated according to the method of the present application is controlled). Figure 2 a is the angle of attack curve of the variant aircraft, Figure 2 b is the sideslip angle curve of the variant aircraft, Figure 2 c is the roll angle curve of the variant aircraft.

[0168] The attitude angle tracking error curve is as follows: Figure 3 As shown in the figure, the horizontal axis represents the flight time in seconds (s), and the vertical axis represents the error angle in degrees (°). Figure 3 a is the angle of attack tracking error curve, Figure 3 b is the sideslip angle tracking error curve, Figure 3 c is the roll angle tracking error curve.

[0169] The attitude angle command tracks the rudder angle change curve as follows Figure 4 As shown in the figure, the horizontal axis represents the flight time in seconds (s), and the vertical axis represents the angle in degrees (°). Figure 4 a is the aileron rudder deflection curve, Figure 4 b is the pitch rudder angle curve, Figure 4 c is the rudder deflection curve.

[0170] Conduct a full-range ballistic flight attitude closed-loop control simulation experiment on the variant aircraft:

[0171] Equivalent rudder angle constraint of high-speed morphing aircraft:

[0172] δ x ∈[-30°,30°],δ y ∈[-30°,30°],δ z ∈[-30°,30°]

[0173] The initial working condition settings for the single-point attitude control simulation of the high-speed variant aircraft are shown in Table 4:

[0174] Initial state Initial value of the state quantity Height (m) 70000 Longitude (°) 0 Latitude (°) 0 Speed ​​(m / s) 6500 Track angle (°) 0 Heading angle (°) 0 Angle of attack (°) 0 Sideslip angle (°) 0 Roll angle (°) 0 Roll angular rate (°) 0 Yaw angular rate (°) 0 Pitch rate (°) 0

[0175] Table 4

[0176] The controller parameters are shown in Table 5:

[0177]

[0178] Table 5

[0179] The obtained attitude angle command tracking curve is as follows: Figure 5 As shown in the figure, the horizontal axis represents the flight time, the vertical axis represents the angle, the unit is degree (°), the solid line represents the actual attitude angle curve of the variant aircraft, and the dotted line represents the command attitude angle curve of the variant aircraft. Figure 5 a is the angle of attack curve of the variant aircraft, Figure 5 b is the sideslip angle curve of the variant aircraft, Figure 5 c is the roll angle curve of the variant aircraft.

[0180] The attitude angle tracking error curve is as follows: Figure 6 As shown in the figure, the horizontal axis represents the flight time, and the vertical axis represents the error angle, the unit is degree (°). Figure 6 a is the angle of attack tracking error curve, Figure 6 b is the sideslip angle tracking error curve, Figure 6 c is the roll angle tracking error curve.

[0181] The attitude angle command tracks the rudder angle change curve as follows Figure 7 As shown in the figure, the horizontal axis represents the flight time, and the vertical axis represents the angle, the unit is degree (°). Figure 7 a is the aileron rudder deflection curve, Figure 7 b is the pitch rudder angle curve, Figure 7 c is the rudder deflection curve.

[0182] It can be seen that controlling the variant aircraft by the method of the present application can effectively improve the accuracy of controlling the variant aircraft and reduce errors.

[0183] This application adopts a data-driven modeling method based on the Koopman operator to model high-speed variant aircraft, without the need to obtain the specific dynamic form of the variant aircraft a priori; in addition, since this application uses the form of a linear polyhedral system for modeling, the uncertainty of the model is concentrated in the convex combination of the linear polyhedral system, which can be handled in a targeted manner by designing a robust controller.

[0184] This application designs a data-driven robust predictive controller to solve the optimal control quantity under the condition of considering constraints, realize high-precision tracking control of the attitude angle curve, and improve the robustness of the control system.

[0185] The robust predictive control device for a variant aircraft provided in this application is exemplarily described below.

[0186] like Figure 8 As shown, an embodiment of the present application provides a robust predictive control device for a variant aircraft, and the robust predictive control device 800 for a variant aircraft includes:

[0187] The first construction module 801 is used to construct a motion equation of the target variant aircraft around the center of mass, and obtain a system attitude angle equation of the target variant aircraft based on the motion equation around the center of mass;

[0188] Acquisition module 802 is configured to acquire state variables and control variables of the target variant aircraft at multiple historical moments and construct a Koopman nominal model based on the Koopman theory using all state variables, all control variables, and the system attitude angle equation. The Koopman nominal model is configured to describe the relationship between the state variables and control variables of the target variant aircraft.

[0189] A second construction module 803 is configured to construct a linear polyhedral system of the target variant aircraft based on the Koopman nominal model; the linear polyhedral system includes the Koopman nominal model and the constraints of the Koopman nominal model;

[0190] The third construction module 804 is used to construct a robust predictive controller of the target variant aircraft based on the linear polyhedral system; the robust predictive controller is used to describe the relationship between the reference state variable, state variable and control variable of the target variant aircraft;

[0191] The solving module 805 is used to solve the robust predictive controller to obtain the optimal control quantity of the target variant aircraft, and control the target variant aircraft according to the optimal control quantity.

[0192] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0193] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0194] like Figure 9 As shown, an embodiment of the present application provides a terminal device, and the terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 9 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the steps of any of the above method embodiments when executing the computer program D102.

[0195] Specifically, when the processor D100 executes the computer program D102, it constructs the motion equation of the target variant aircraft around the center of mass, and obtains the system attitude angle equation of the target variant aircraft based on the motion equation around the center of mass, and then obtains the state variables and control variables of the target variant aircraft at multiple historical moments, and based on the Koopman theory, uses all state variables, all control variables, and system attitude angle equations to construct a Koopman nominal model, and then constructs a linear polyhedral system of the target variant aircraft based on the Koopman nominal model, and then constructs a robust predictive controller of the target variant aircraft based on the linear polyhedral system, and finally solves the robust predictive controller to obtain the optimal control quantity of the target variant aircraft, and controls the target variant aircraft according to the optimal control quantity. Among them, the state variables, control variables and system attitude angle equations of the target variant aircraft are used to construct a Koopman nominal model, and a linear polyhedral system is constructed based on the Koopman nominal model, so that the strongly coupled and strongly nonlinear control system is converted into a linear control system, which improves the solvability and robustness of predictive control of the target variant aircraft. Even if there are deviations in the identification of relevant parameters, the optimal control quantity can still be provided for the target variant aircraft, thereby improving the accuracy, stability and robustness of the control of the variant aircraft, ensuring that the variant aircraft accurately and quickly tracks the given reference attitude to achieve long-term, long-distance, large-maneuver high-speed gliding flight.

[0196] The processor D100 may be a central processing unit (CPU), or may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0197] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC, SmartMedia Card), a secure digital (SD, Secure Digital) card, a flash card, etc. equipped on the terminal device D10. Furthermore, the memory D101 may also include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0198] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0199] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0200] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the device / terminal device of the robust predictive control method of the variant aircraft, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electric carrier signal, a telecommunication signal and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0201] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0202] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0203] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A robust predictive control method for a morphing aircraft, characterized in that: include: Constructing a motion equation of a target variant aircraft around its center of mass, and obtaining a system attitude angle equation of the target variant aircraft based on the motion equation around its center of mass; Obtaining state variables and control variables of the target variant aircraft at multiple historical moments, and constructing a Koopman nominal model based on the Koopman theory using all state variables, all control variables, and the system attitude angle equation; the Koopman nominal model is used to describe the relationship between the state variables and control variables of the target variant aircraft; Constructing a linear polyhedral system of the target variant aircraft based on the Koopman nominal model; The linear polytopic system includes the Koopman nominal model and constraints of the Koopman nominal model; A robust predictive controller for the target variant aircraft is constructed based on the linear polyhedral system; the robust predictive controller is used to describe the relationship between the reference state variable, state variable and control variable of the target variant aircraft; The robust predictive controller is solved to obtain an optimal control variable of the target variant aircraft, and the target variant aircraft is controlled according to the optimal control variable.

2. The robust predictive control method for a morphing aircraft according to claim 1, characterized in that: The equation of motion about the center of mass is: Among them, α represents the attitude angle vector of the first body axis, represents the derivative of the attitude angle vector of the first body axis, β represents the attitude angle vector of the second body axis, represents the derivative of the attitude angle vector of the second body axis, σ represents the attitude angle vector of the third body axis, Derivative of the attitude angle vector about the third body axis, ω x Represents the attitude angular velocity vector of the first body axis, ω y Represents the attitude angular velocity vector of the second body axis, ω z represents the attitude angular velocity vector of the third body axis, represents the derivative of the attitude angular velocity vector about the first body axis, represents the derivative of the attitude angular velocity vector about the second body axis, represents the derivative of the attitude angular velocity vector of the third body axis, m represents the mass of the target variant aircraft, V represents the speed of the target variant aircraft, θ represents the track inclination angle of the target variant aircraft, Y t Z is the component of the sum of the nominal aerodynamic force and the additional force in the geocentric coordinate system perpendicular to the plane formed by the prime meridian and the axis pointing to the North Pole. t It represents the component of the sum of the nominal aerodynamic force and the additional force pointing to the North Pole in the geocentric coordinate system under no deformation, g represents the acceleration due to gravity, and I xx The rotational inertia of the target variant aircraft around the first body axis, I yy I represents the rotational inertia of the target variant aircraft around the second body axis, zz I represents the rotational inertia of the target variant aircraft around the third body axis, xy represents the product of inertia, M tx represents the resultant moment about the first body axis, M ty represents the resultant moment about the second body axis, M tz represents the resultant moment about the third body axis.

3. The robust predictive control method for a morphing aircraft according to claim 2, characterized in that: The system attitude angle equation is: Among them, x1 represents the attitude angle state variable, represents the derivative of the attitude angle state variable, x1=[α,β,σ] T , x2 represents the attitude angular velocity state variable, represents the derivative of the attitude angular velocity state variable, x2=[ω x ,ω y ,ω z ] T , M t (u) represents the net external torque, d represents the external disturbance, R represents the matrix of the attitude angle vector, I represents the matrix of the rotational inertia, ω × A matrix representing the attitude angular velocity vector.

4. The robust predictive control method for a morphing aircraft according to claim 3, characterized in that: The method of constructing a Koopman nominal model using all state variables, all control variables, and the system attitude angle equation includes: Based on the Koopman theory, the system optimization problem is defined using all state variables and all control variables; Solving the system optimization problem to obtain a solution result; A Koopman nominal model is constructed according to the solution result and the system attitude angle equation.

5. The robust predictive control method for a morphing aircraft according to claim 4, characterized in that: The system optimization problem is: Where K = [A, B], K represents the approximate Koopman operator, A represents the approximate linear system matrix, B represents the approximate linear control matrix, J(D) represents the objective function for minimizing the truncation error of the approximate Koopman operator, C represents the inverse projection matrix of the approximate Koopman operator, Θ f represents the backward data set that satisfies the state equation under the Koopman dimensionality-increasing mapping, Θ p represents the forward data set that satisfies the state equation under the Koopman dimensionality-increasing mapping, Represents the data set obtained by splicing and collecting state variables and control variables under the dimensionality-increasing mapping: Where X represents all state variables, x(1) represents the state variable at the first historical moment, x(2) represents the state variable at the second historical moment, x(N) represents the state variable at the Nth historical moment, Ψ(x) represents the observed value of the state variable x under the Koopman dimensionality-increasing mapping, x∈{x(1),...,x(N)}, Ψ1(x) represents the first element in the observed value, and Ψ2(x) represents the second element in the observed value. Indicates the Nth observation value k elements, Ψ(x(1)) represents the observed value of the state variable x(1) under the Koopman dimensionality-increasing mapping, Ψ(x(2)) represents the observed value of the state variable x(2) under the Koopman dimensionality-increasing mapping, N ) represents the state variable x N The observed value under the Koopman dimensionality increase mapping, Ψ(x(N+1)) represents the observed value of the state variable x(N+1) under the Koopman dimensionality increase mapping, a (x,u)=[Ψ(x) T u T ] T ,Ψ a (x(1),u(1)) represents the concatenation of the observed value of the state variable x(1) under the Koopman dimensionality-increasing mapping and the observed value of the control variable at the first historical moment, u∈{u(1),...,u(N)}, u(1) represents the control variable at the first historical moment, u(N) represents the control variable at the Nth historical moment, n represents the original state dimension, m represents the control input dimension, N K Indicates the dimension of the upgraded state.

6. The robust predictive control method for a morphing aircraft according to claim 5, characterized in that: The solution result is: The Koopman nominal model is: Ψ(x(k+1))=AΨ(x(k))+Bu(k) x(k)=CΨ(x(k)) Among them, Ψ(x(k+1)) represents the observed value of the state variable x(k+1) under the Koopman dimensionality-increasing mapping, Ψ(x(k)) represents the observed value of the state variable x(k) under the Koopman dimensionality-increasing mapping, x(k) represents the state variable at the current moment, x(k+1) represents the state variable at the next moment of the current moment, and u(k) represents the control variable at the current moment.

7. The robust predictive control method for a morphing aircraft according to claim 6, characterized in that: The linear polytopic system is: Ψ(x(k+1))=A k Ψ(x(k))+B k u(k), x(k)=CΨ(x(k)), Among them, A k Represents the approximate linear system matrix at the current moment, B k Represents the approximate linear control matrix at the current moment, A i Represents the system matrix of the ith vertex of the polytopic set, B i represents the control matrix of the ith vertex of the polytopic set, λ i represents the scheduling value of the ith vertex of the polytopic set, Represents the total number of vertices of the polytopic set.

8. The robust predictive control method for a morphing aircraft according to claim 7, characterized in that: The robust predictive controller is: Among them, γ, Y, Q0 are all optimization variables, A j The system matrix of the j-th vertex of the polytopic set, B j represents the control matrix of the j-th vertex of the polytopic set, represents the state weight matrix, represents the control weight matrix, I n represents the unit matrix of dimension n, represents the intermediate matrix variables that satisfy the control constraints, express Matrix diagonal elements, u j,max represents the constraint limit value of the jth control variable, m represents the control variable dimension, x max represents the state constraint limit value, Ψ(x r ) represents the state variable x r The observed value under the Koopman dimensionality increase mapping, x r represents the reference state variable, and ψ(x(k)) represents the observed value of the state variable x(k) under the Koopman dimensionality-increasing mapping.

9. The robust predictive control method for a morphing aircraft according to claim 8, characterized in that: The optimal control quantity is: in, Indicates the optimal rudder angle control amount.

10. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the robust predictive control method for a variant aircraft according to any one of claims 1 to 9 is implemented.

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