Variable sweep aircraft preset performance control method based on adaptive dynamic programming

Abstract

The embodiment of the present disclosure provides a variable sweep aircraft preset performance control method based on adaptive dynamic programming, which belongs to the technical field of control and specifically comprises the following steps: constructing a variable sweep aircraft motion model for attitude control; constructing a preset performance function, obtaining a converted error system through homeomorphism, and designing a virtual controller and a steady-state controller for an attitude angle loop and an attitude angular velocity loop; introducing a cost function to convert the aircraft attitude tracking control problem into the solution of an optimal control problem, constructing an Actor-Critic network based on adaptive dynamic programming technology to solve the optimal compensation controller online; combining the steady-state controller and the optimal compensation controller to obtain the variable sweep aircraft controller, and performing numerical simulation on the performance of the controller based on specific flight conditions. Through the scheme of the present disclosure, the control system has good control accuracy and transient performance, and the robustness to unknown flight environment is improved.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of control technology, and in particular to a method for controlling preset performance of a variable-sweep aircraft based on adaptive dynamic programming. Background Art

[0002] Variable-sweep aircraft are a new type of aircraft that can autonomously adjust the sweep angle of their wings to achieve enhanced aerodynamics and optimal flight performance based on different missions and flight environments. They hold great potential for development and application in aerospace and other fields. Compared to fixed-wing aircraft, the aerodynamics, moment of inertia, and center of mass position undergo significant changes during aircraft deformation, posing new challenges and higher requirements for aircraft control system design. Therefore, enabling attitude control during aircraft deformation is a hot topic in current research.

[0003] Currently, there are several research results on control methods for variable-sweep aircraft. The main research direction focuses on how to effectively estimate the unknown dynamic disturbances and strong uncertainties caused by the deformation process and design corresponding compensatory controllers. Classical control methods suffer from insufficient adaptability and cannot guarantee the stability of the aircraft under uncertain factors. While intelligent control methods can effectively improve the adaptability of aircraft, existing methods often rely on large-scale data collection and offline training. However, the limited state collection capabilities of actual control systems make it difficult to obtain sufficient data for network training.

[0004] It can be seen that there is an urgent need for a variable-sweep aircraft preset performance control method based on adaptive dynamic programming with high control accuracy, fast response speed and strong robustness. Summary of the Invention

[0005] In view of this, an embodiment of the present disclosure provides a variable-sweep aircraft preset performance control based on adaptive dynamic programming, which at least partially solves the problems of poor control accuracy, response speed and robustness in the prior art.

[0006] The present disclosure provides a method for controlling preset performance of a variable-sweep aircraft based on adaptive dynamic programming, including:

[0007] Step 1: Construct a variable-sweep aircraft motion model for attitude control;

[0008] Step 2: Based on the preset performance framework, a preset performance function is constructed. The converted error system is obtained through homeomorphic mapping. The variable-sweep aircraft motion model is divided into two subsystems, the attitude angle loop and the attitude angular velocity loop, and controllers are designed for each subsystem. The virtual controller corresponding to the attitude angle loop and the steady-state controller corresponding to the attitude angular velocity loop are obtained.

[0009] Step 3: Introduce a cost function to transform the aircraft attitude tracking control problem into an optimal control problem. Based on the adaptive dynamic programming technology, an Actor-Critic network is constructed to solve the Bellman equation online and obtain the optimal compensation controller.

[0010] Step 4: derive a variable-sweep aircraft controller based on the steady-state controller and the optimal compensation controller, and perform numerical simulation on the controller performance based on specific flight conditions.

[0011] According to a specific implementation of the embodiment of the present disclosure, step 1 specifically includes:

[0012] Step 1.1: Establish a variable-sweep aircraft motion model for attitude control

[0013]

[0014] Where, is the attitude angle vector, Represents the pitch angle, yaw angle and roll angle respectively, w=[w x w y w z ] T is the attitude angular velocity vector, w x ,w y ,w z Denote the roll angular velocity, yaw angular velocity and pitch angular velocity respectively, I b is the moment of inertia matrix without considering deformation, ΔI is the additional moment of inertia matrix caused by deformation, M a is the aerodynamic torque, M f is the additional moment caused by deformation, is the position vector of the wing installation point in this system, is the rotation vector of the wing center of mass relative to the wing mounting point, d is the lumped disturbance, and the matrix R,w × The expression is as follows:

[0015]

[0016] Step 1.2, define the state variables x1 = Θ, x2 = ω, and define the variable sweep aircraft controller u = [δ x δ y δ z ] T By the steady-state controller u d and the optimal compensation controller u f Composition, δ x ,δ y ,δ z are the equivalent rudder angles of the roll, yaw, and pitch channels, respectively, and the variable-sweep aircraft motion model for attitude control is obtained:

[0017]

[0018] Where,

[0019] u=u d +u f

[0020] I=I b +ΔI

[0021] f2=I -1 (-ω × I b ω+M0)

[0022]

[0023] g2=I -1 g

[0024]

[0025] in, is the operating torque coefficient, is the damping torque coefficient, Q is the dynamic pressure, V is the speed of the aircraft, S is the reference area, and L is the reference length.

[0026] According to a specific implementation of the embodiment of the present disclosure, step 2 specifically includes:

[0027] Step 2.1, design the controller for the attitude angle loop subsystem and define the attitude angle tracking instruction as x 1d , then the attitude angle tracking error e1(t)=[e 11 e 12 e 13 ] T =x1-x 1d , design the preset performance function ρ i (t), i=1, 2, 3 represent three different channels, and the attitude angle tracking error satisfies the constraint: -ρ i (t)<e 1i (t)<ρ i (t);

[0028] Step 2.2: Design a homeomorphic mapping function to transform the performance constraint space into the unconstrained space and obtain the transformed error system:

[0029]

[0030] Where, ε1=[ε 11 ε 12 ε 13 ] Tis the error variable after mapping,

[0031] Step 2.3, design the virtual controller x corresponding to the attitude angle loop 2d for

[0032] x 2d =R -1 [x 2c,1 x 2c,2 x 2c,3 ] T

[0033]

[0034] Where, K1=[K 11 K 12 K 13 ] T is the virtual controller gain;

[0035] Step 2.4, define the attitude angular velocity tracking error e2 = x2-x 2d , whose derivative is the error system:

[0036]

[0037] Step 2.5: Design the steady-state controller u corresponding to the attitude angular velocity loop based on the preset performance framework d as follows:

[0038] u d =g2 -1 (-f2(x 2d )-K2e2+ξ2)

[0039] Where ξ1 is the value of the virtual controller x 2d The estimated value of the reciprocal, K2 is the steady-state controller gain.

[0040] According to a specific implementation of the embodiment of the present disclosure, step 3 specifically includes:

[0041] Step 3.1, set the steady-state controller u d Substituting into the attitude angular velocity loop, the error system is transformed into:

[0042]

[0043] Where, f e =f2-f2(x 2d ), is the estimated error of the virtual controller derivative;

[0044] Step 3.2, design the cost function as:

[0045] J(e2(t))=r(e2(t),u f (t))+γr(e2(t+Δt),u f (t+Δt))+γ 2 r(e2(t+2Δt),u f (t+2Δt))+···

[0046] =r(e2(t),u f (t))+γJ(e2(t+Δt))

[0047] In the formula, r(e2(t),u f (t)) = e2(t) T Qe2(t)+u f (t) T Ru f (t) is the utility function at the current sampling moment, Δt is the sampling step, γ is the discount coefficient, Q, R are positive definite matrices;

[0048] Step 3.3, design the optimal cost function J * (e2(t)), satisfies the Bellman equation:

[0049]

[0050] Step 3.4: Construct the Critic network and the Actor network to fit the optimal cost function J * and the optimal compensation controller u f as a solution to the optimal control problem.

[0051] According to a specific implementation of the embodiment of the present disclosure, step 3.4 specifically includes:

[0052] Step 3.4.1, construct a critic network to fit the optimal cost function, wherein the critic network includes an input layer, a hidden layer and an output layer, and its input vector C I =[e2 T x 2d T ] T , the output vector The cost function estimate Expressed as:

[0053]

[0054] Where W c is the weight parameter of the Critic network, Φ c (·) is the activation function;

[0055] Step 3.4.2: Build the Actor network to fit the optimal compensation controller u f , wherein the Actor network includes an input layer, a hidden layer and an output layer, and its input vector A I =e2, output vector A O =u f , the optimal compensation controller u f Expressed as:

[0056] u f =W a T Φ a (A I )

[0057] Where W a is the weight parameter of the Actor network, Φ a (·) is the Actor network activation function.

[0058] According to a specific implementation of the embodiment of the present disclosure, before step 3.4.1, the method further includes:

[0059] The estimation error of the Critic network is defined as:

[0060]

[0061] The objective function of critic network weight update is designed as:

[0062]

[0063] Based on the gradient descent strategy, the update strategy of the Critic network weight parameters is obtained as follows:

[0064]

[0065] Where η c is the Critic network learning rate;

[0066] The weight parameters of the Critic network are updated according to the update strategy of the Critic network weight parameters.

[0067] According to a specific implementation of the embodiment of the present disclosure, before step 3.4.2, the method further includes:

[0068] The estimated error of the Actor network is defined as:

[0069]

[0070] The objective function for updating the Actor network weights is designed as:

[0071]

[0072] Based on the gradient descent strategy, the update strategy for the Actor network weight parameters is obtained as follows:

[0073]

[0074] Where η a is the Actor network learning rate, is the gradient of the activation function with respect to the input;

[0075] Update the weight parameters of the Actor network according to the update strategy of the Actor network weight parameters.

[0076] The preset performance control scheme for a variable-sweep aircraft based on adaptive dynamic programming in the embodiment of the present disclosure includes: step 1, constructing a variable-sweep aircraft motion model for attitude control; step 2, constructing a preset performance function based on a preset performance framework, obtaining a converted error system through homeomorphic mapping, dividing the variable-sweep aircraft motion model into two subsystems, an attitude angle loop and an attitude angular velocity loop, and performing controller design on each subsystem to obtain a virtual controller corresponding to the attitude angle loop and a steady-state controller corresponding to the attitude angular velocity loop; step 3, introducing a cost function to convert the aircraft attitude tracking control problem into a solution to an optimal control problem, constructing an Actor-Critic network based on adaptive dynamic programming technology to solve the Bellman equation online to obtain an optimal compensation controller; step 4, obtaining a variable-sweep aircraft controller based on the steady-state controller and the optimal compensation controller, and performing numerical simulation of the controller performance based on specific flight conditions.

[0077] The beneficial effects of the disclosed embodiments are as follows: Through the disclosed solution, a basic controller designed based on a preset performance framework and backstepping control principles can ensure that the attitude angle error remains within the preset performance envelope throughout the entire flight process, with rapid convergence speed, effective suppression of overshoot, and greatly improved steady-state and transient performance of the control system. At the same time, to increase the robustness and adaptability of the controller in different flight environments, an adaptive dynamic programming technique is used to design a feedback controller. This controller eliminates the need for offline training and can directly perform online learning. It is updated in real time based on data such as the current tracking error to compensate for disturbances caused by uncertainties such as aerodynamic parameters, thereby improving control accuracy, response speed, and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0079] Figure 1 A flow chart of a method for controlling preset performance of a variable-sweep aircraft based on adaptive dynamic programming provided by an embodiment of the present disclosure;

[0080] Figure 2 A system block diagram of a variable-sweep aircraft preset performance control method based on adaptive dynamic programming provided by an embodiment of the present disclosure;

[0081] Figure 3 A schematic diagram of a sweep angle variation curve provided in an embodiment of the present disclosure;

[0082] Figure 4 Schematic diagrams of attitude angle tracking curves under two working conditions provided by the embodiment of the present disclosure, wherein (a) is the pitch angle tracking condition, (b) is the yaw angle tracking condition, and (c) is the roll angle tracking condition;

[0083] Figure 5 Schematic diagram of attitude angle error curve provided by an embodiment of the present disclosure, where (a) is pitch angle error, (b) is yaw angle error, and (c) is roll angle error;

[0084] Figure 6 Schematic diagram of actual rudder deflection angle curves for working condition 1 provided by an embodiment of the present disclosure, (a) is the rudder deflection angle curve No. 1, (b) is the rudder deflection angle curve No. 2, (c) is the rudder deflection angle curve No. 3, and (d) is the rudder deflection angle curve No. 4;

[0085] Figure 7 Schematic diagram of actual rudder deflection angle curves for working condition 2 provided by an embodiment of the present disclosure, (a) is the rudder deflection angle curve No. 1, (b) is the rudder deflection angle curve No. 2, (c) is the rudder deflection angle curve No. 3, and (d) is the rudder deflection angle curve No. 4;

[0086] Figure 8 Schematic diagrams of weight parameter change curves of neural networks under two working conditions provided by the embodiments of the present disclosure, wherein (a) is the weight parameter change curve of the Critic network, and (b) is the weight parameter change curve of the Actor network; DETAILED DESCRIPTION

[0087] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0088] The following is a detailed description of the embodiments of the present disclosure, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0089] An embodiment of the present disclosure provides a method for controlling preset performance of a variable-sweep aircraft based on adaptive dynamic programming, which can be applied to aircraft control processes in the aviation field.

[0090] See also Figure 1 , is a flow chart of a variable-sweep aircraft preset performance control method based on adaptive dynamic programming provided by an embodiment of the present disclosure. Figure 1 As shown, the method mainly includes the following steps:

[0091] The overall system framework is as follows Figure 2 As shown. The controller expression is u=u d +u f , where u d is a steady-state controller based on a preset performance framework, u f It is the optimal compensation controller based on the HDP algorithm and is directly output by the Actor network.

[0092] Step 1: Construct a mathematical model of a variable-sweep aircraft for attitude control

[0093] The mathematical model of the variable-sweep aircraft for attitude control is established as follows:

[0094]

[0095] Where, is the attitude angle vector, Represents the pitch angle, yaw angle and roll angle respectively. w=[w x w y w z ] T is the attitude angular velocity vector, w x ,w y ,w z Denote the roll angular velocity, yaw angular velocity and pitch angular velocity respectively, I b is the moment of inertia matrix without considering deformation, ΔI is the additional moment of inertia matrix caused by deformation, is the position vector of the wing installation point in this system, is the rotation vector of the wing center of mass relative to the wing mounting point, d is the lumped disturbance, and the matrix R,w × The expression is as follows:

[0096]

[0097]

[0098] M a is the aerodynamic torque, which is expressed as follows:

[0099]

[0100] Where C mx ,C my ,C mzare the rolling moment, yaw moment, and pitching moment coefficients, respectively. is the control torque coefficient, Q=ρV 2 / 2 is the dynamic pressure, is the atmospheric density, V is the speed of the aircraft, S is the reference area, and L is the reference length.

[0101] M f is the additional moment caused by deformation, and its expression is as follows:

[0102]

[0103] Where w j is the angular velocity of the wing, is the angular acceleration of the wing, R bj is the transformation matrix, which is expressed as follows:

[0104]

[0105] Where R y is the elementary transformation matrix around the y-axis, and θ is the sweep angle.

[0106] Define the state x1=Θ,x2=ω, define the controller u=[δ x δ y δ z ] T By the steady-state controller u d and the optimal compensation controller u f Composition, δ x ,δ y ,δ z are the equivalent rudder angles of the roll, yaw, and pitch channels respectively, then the mathematical model of the variable-sweep aircraft for attitude control can be rewritten as:

[0107]

[0108] Where,

[0109] u=u d +u f

[0110] I=I b +ΔI

[0111] f2=I -1 (-ω × I b ω+M0)

[0112]

[0113] g2=I -1 g

[0114]

[0115] definition is the actual rudder deflection angle, and its transformation relationship with the equivalent rudder deflection angle, i.e., the controller u, is as follows:

[0116]

[0117] Step 2: Based on the preset performance framework, construct the preset performance function, obtain the converted new error system through homeomorphic mapping, and combine the backstepping control idea to divide the motion model into two subsystems: attitude angle loop and attitude angular velocity loop, and design controllers for each subsystem.

[0118] First, design the controller for the attitude angle loop subsystem. Define the attitude angle tracking instruction as x1d, then the attitude angle tracking error e1(t) = [e 11 e 12 e 13 ] T =x1-x 1d , during the entire flight process, the tracking error e1(t) should always remain within the designed performance envelope, that is, satisfying:

[0119] -ρ i (t)<e 1i (t)<ρ i (t)

[0120] Among them, ρ i (t) is the preset performance function, i=1, 2, 3 represents three different channels, and its form is as follows:

[0121]

[0122] in, Mainly affects the convergence speed of the preset performance function, ρ 0,i >0 is the initial value of the preset performance function, ρ ∞,i >0 is the preset final value of the performance function, which can ensure the steady-state error of the control system.

[0123] Since the introduction of additional performance constraints increases the difficulty of controller design, in order to reduce the complexity of the corresponding controller, a homeomorphic mapping function is designed to achieve the transformation from the performance constraint space to the unconstrained space. This paper adopts a logarithmic mapping, which is expressed as follows:

[0124]

[0125] ε1=[ε 11 ε 12 ε 13 ] Tis the error variable after mapping, and taking its derivative, we can get:

[0126]

[0127] Where,

[0128] Design virtual controller x 2d as follows:

[0129]

[0130] x 2d =R -1 [x 2c,1 x 2c,2 x 2c,3 ] T

[0131] Where, K1=[K 11 K 12 K 13 ] T is the virtual controller gain.

[0132] Design a controller for the attitude angular velocity loop subsystem. 2d By tracking the differentiator, an estimate of the virtual controller derivative is obtained The design of finite time tracking differentiator is as follows:

[0133]

[0134] Where H is the differentiator gain, λ1,λ2>0 are constants, ξ1,ξ2 are the values ​​of the virtual controller x 2d and its derivatives estimated value.

[0135] Define the attitude angular velocity tracking error e2 = x2-x 2d , its derivative can be expressed as:

[0136]

[0137] Design a steady-state controller u based on a preset performance framework d as follows:

[0138] u d =g2 -1 (-f2(x 2d )-K2e2+ξ2)

[0139] Where K2 is the steady-state controller gain.

[0140] Step 3: Introduce the cost function to transform the problem into the solution of the optimal control problem. Based on the adaptive dynamic programming technology, construct the Actor-Critic network to solve the Bellman equation online and obtain the optimal compensation controller.

[0141] The steady-state controller u based on the preset performance framework d Substituting into the attitude angular velocity loop, the new error system form is as follows:

[0142]

[0143] Where, f e =f2-f2(x 2d ), is the estimated error of the virtual controller derivative. Therefore, the problem is transformed into designing an optimal compensation controller for the new error system to minimize the cost function.

[0144] The designed cost function expression is as follows:

[0145] J(e2(t))=r(e2(t),u f (t))+γr(e2(t+Δt),u f (t+Δt))+γ 2 r(e2(t+2Δt),u f (t+2Δt))+···

[0146] =r(e2(t),u f (t))+γJ(e2(t+Δt))

[0147] In the formula, r(e2(t),u f (t)) = e2(t) T Qe2(t)+u f (t) T Ru f (t) is the utility function at the current sampling moment, Δt is the sampling step, γ is the discount coefficient, and Q, R are positive definite matrices.

[0148] Then the optimal cost function J * (e2(t)) should satisfy the following Bellman equation:

[0149]

[0150] Since the Bellman equation itself is difficult to solve, the HDP algorithm is used to approximate the optimal solution of the Bellman equation, that is, to construct the Critic network and the Actor network respectively to fit the optimal cost function J * and the optimal compensation controller u f .

[0151] The Critic network is constructed to fit the cost function for policy evaluation, and the constructed Critic network comprises an input layer, a hidden layer and an output layer, and the input vector C I T x 2d T T and the output vector Therefore, the cost function estimate value can be represented as:

[0152]

[0153] where W c is the weight parameter of the Critic network, and Φ c (·) is an activation function.

[0154] The estimation error of the Critic network is defined as:

[0155]

[0156] The objective function of the Critic network weight update is designed as:

[0157]

[0158] Based on the gradient descent strategy, the update strategy of the Critic network weight parameter is obtained as:

[0159]

[0160] where η c is the learning rate of the Critic network.

[0161] The Actor network is constructed to fit the optimal compensation controller u f for policy update, and the Actor network comprises an input layer, a hidden layer and an output layer, and the input vector A I = e2, and the output vector A O = u f . Therefore, the optimal compensation controller u f can be represented as:

[0162] u f = W a T Φ a (A I )

[0163] where W a is the weight parameter of the Actor network, and Φ a (·) is an activation function of the Actor network.​​

[0164] The Actor network updates its parameters by minimizing the value function. Therefore, the estimation error of the Actor network is defined as:

[0165]

[0166] The objective function for updating the Actor network weights is designed as:

[0167]

[0168] Based on the gradient descent strategy, the update strategy for the Actor network weight parameters is obtained as follows:

[0169]

[0170] Where η a is the Actor network learning rate, is the gradient of the activation function with respect to the input.

[0171] Step 4: In summary, the preset performance control method based on adaptive dynamic programming, the controller expression is u=u d +u f , where u d is a steady-state controller based on a preset performance framework, u f This is an optimal compensation controller based on the HDP algorithm, directly output by the Actor network. Numerical simulations were performed using MATLAB, where a motion model of a variable-sweep aircraft was constructed. Based on this model, the proposed variable-sweep aircraft preset performance control method based on adaptive dynamic programming was simulated.

[0172] In order to test the robustness of the controller, two different pneumatic deflection conditions are set:

[0173] (1) Working condition 1: atmospheric density deviation -20%, aerodynamic force coefficient deviation +20%, aerodynamic moment coefficient deviation +20%

[0174] (2) Working condition 2: atmospheric density deviation +20%, aerodynamic force coefficient deviation -20%, aerodynamic moment coefficient deviation -20%

[0175] The initial state of the variable sweep aircraft is set as follows: sweep angle θ(0) = 26°, pitch angle Yaw angle ψ(0) = 1°, roll angle γ(0) = 1°, attitude angular rate w x (0) = w y (0) = w z (0) = 0° / s, speed 1.5 Ma, altitude 15 km.

[0176] Default performance controller parameter settings: K1 = [15, 15, 15] T ,K2=diag(25,25,25),ρ0=[5°,5°,5°] T ,ρ ∞ =[0.5°,0.5°,0.5°] T ,l=2,T=3; HDP controller parameter settings: Q=diag(300,300,300), R=diag(10,10,10), γ=0.8, η c =0.6,η a =0.6; Tracking differentiator parameter settings: H=50,λ1=[2,2,2],λ2=[2,2,2].

[0177] Figure 3 This is the sweep angle change curve. The sweep angle maintains an initial state of 26° in 0-10s, and starts to change to 50° at a speed of 2° / s after 10s, and then maintains a sweep angle of 50° to continue flying. Figure 4 These are the attitude angle tracking curves under two working conditions. In the case of initial deviation, the attitude angle can quickly track the target instruction. Figure 5 is the attitude angle error curve. It can be seen that the attitude angle error is always kept within the preset performance envelope, and the terminal tracking error is less than 0.5°. Figure 6 is the actual rudder deflection curve under working condition 1, Figure 7 is the actual rudder deflection curve under working condition 2, Figure 8 The weight parameter change curves of the Critic network and the Actor network show that both the Critic network and the Actor network converge quickly under the two working conditions.

[0178] In summary, the variable-sweep aircraft preset performance control method based on adaptive dynamic programming proposed in the present invention can ensure that the aircraft maintains a stable attitude when there is uncertainty in aerodynamic parameters and atmospheric density parameters. The attitude angle during the entire flight process is always within the preset performance envelope, and the terminal error does not exceed 0.5°. The control method has good convergence speed and transient performance.

[0179] The preset performance control method for variable sweep aircraft based on adaptive dynamic programming provided by the embodiment can guarantee that the attitude angle error is always in the preset performance envelope during the whole flight process through the basic controller designed based on the preset performance framework and the backstepping control thought, has high control precision, fast convergence speed, effectively suppresses the overshoot, and greatly improves the steady-state performance and transient performance of the control system; the feedback controller designed by using the adaptive dynamic programming technology can learn online, does not need to be trained offline in advance, and is updated online in real time according to the current tracking error and other data, so as to compensate the disturbance caused by the uncertainty factors such as aerodynamic parameters, and increase the robustness and adaptability of the controller under different flight environments.

[0180] It should be understood that parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0181] The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present disclosure, which should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims

1. A variable-sweep aircraft preset performance control method based on adaptive dynamic programming, characterized in that: include: Step 1: construct a variable-sweep aircraft motion model for attitude control; The step 1 specifically includes: Step 1.1: Establish the attitude motion model of the variable-sweep aircraft around the center of mass Where, is the attitude angle vector, ψ,γ represent the pitch angle, yaw angle and roll angle respectively, w=[w x w y w z ] T is the attitude angular velocity vector, w x ,w y ,w z Denote the roll angular velocity, yaw angular velocity and pitch angular velocity respectively, I b is the moment of inertia matrix without considering deformation, ΔI is the additional moment of inertia matrix caused by deformation, M a is the aerodynamic torque, M f is the additional moment caused by deformation, is the position vector of the wing installation point in this system, is the rotation vector of the wing center of mass relative to the wing mounting point, d is the lumped disturbance, and the matrix R,w × The expression is as follows: Step 1.2, define the state variables x1 = Θ, x2 = ω, and define the variable sweep aircraft controller u = [δ x δ y δ z ] T By the steady-state controller u d and the optimal compensation controller u f Composition, δ x ,δ y ,δ z are the equivalent rudder angles of the roll, yaw, and pitch channels, respectively, and the variable-sweep aircraft motion model for attitude control is obtained: Where, in=in d +in f I=I b +ΔI f2=I -1 (-ω × I b ω+M0) g2=I -1 g in, is the operating torque coefficient, is the damping torque coefficient, Q is the dynamic pressure, V is the speed of the aircraft, S is the reference area, and L is the reference length; Step 2: Based on the preset performance framework, a preset performance function is constructed. The converted error system is obtained through homeomorphic mapping. The variable-sweep aircraft motion model is divided into two subsystems, the attitude angle loop and the attitude angular velocity loop, and controllers are designed for each subsystem. The virtual controller corresponding to the attitude angle loop and the steady-state controller corresponding to the attitude angular velocity loop are obtained. The step 2 specifically includes: Step 2.1, design the controller for the attitude angle loop subsystem and define the attitude angle tracking instruction as x 1d , then the attitude angle tracking error e1(t)=[e 11 e 12 e 13 ] T =x1-x 1d , design the preset performance function ρ i (t), i=1, 2, 3 represent three different channels, and the attitude angle tracking error satisfies the constraint: -ρ i (t)<e 1i (t)<ρ i (t); Step 2.2: Design a homeomorphic mapping function to transform the performance constraint space into the unconstrained space and obtain the transformed error system: Where, ε1=[ε 11 ε 12 ε 13 ] T is the error variable after mapping, Step 2.3, design the virtual controller x corresponding to the attitude angle loop 2d for x 2d =R -1 [x 2c,1 x 2c,2 x 2c,3 ] T Where, K1=[K 11 K 12 K 13 ] T is the virtual controller gain; Step 2.4, define the attitude angular velocity tracking error e2 = x2-x 2d , whose derivative is the error system: Step 2.5: Design the steady-state controller u corresponding to the attitude angular velocity loop based on the preset performance framework d as follows: u d =g2 -1 (-f2(x 2d )-K2e2+ξ2) Where ξ2 is the value of the virtual controller x 2d The estimated value of the derivative, K2 is the steady-state controller gain; Step 3: Introduce a cost function to transform the aircraft attitude tracking control problem into an optimal control problem. Based on the adaptive dynamic programming technology, an Actor-Critic network is constructed to solve the Bellman equation online and obtain the optimal compensation controller. The step 3 specifically includes: Step 3.1, set the steady-state controller u d Substituting into the attitude angular velocity loop, the error system is transformed into: Where, f e =f2-f2(x 2d ), is the estimated error of the virtual controller derivative; Step 3.2, design the cost function as: J(e2(t))=r(e2(t),u f (t))+γr(e2(t+Δt),u f (t+Δt)) +g 2 r(e2(t+2Δt),u f (t+2Δt))+··· =r(e2(t),u f (t))+γJ(e2(t+Δt)) In the formula, r(e2(t),u f (t)) = e2(t) T Qe2(t)+u f (t) T Ru f (t) is the utility function at the current sampling moment, Δt is the sampling step, γ is the discount coefficient, Q, R are positive definite matrices; Step 3.3, design the optimal cost function J * (e2(t)), satisfies the Bellman equation: Step 3.4: Construct the Critic network and the Actor network to fit the optimal cost function J * and the optimal compensation controller u f As a solution to the optimal control problem; Step 4: derive a variable-sweep aircraft controller based on the steady-state controller and the optimal compensation controller, and perform numerical simulation on the controller performance based on specific flight conditions.

2. The method according to claim 1, characterized in that , the step 3.4 specifically includes: Step 3.4.1, construct a critic network to fit the optimal cost function, wherein the critic network includes an input layer, a hidden layer and an output layer, and its input vector C I =[e2 T x 2d T ] T , the output vector The cost function estimate Expressed as: Where W c is the weight parameter of the Critic network, Φ c (·) is the activation function; Step 3.4.2: Build the Actor network to fit the optimal compensation controller u f , wherein the Actor network includes an input layer, a hidden layer and an output layer, and its input vector A I =e2, output vector A O =u f , the optimal compensation controller u f Expressed as: u f =W a T Φ a (A I ) Where W a is the weight parameter of the Actor network, Φ a (·) is the Actor network activation function.

3. The method according to claim 2, characterized in that Before step 3.4.1, the method further includes: The estimation error of the Critic network is defined as: The objective function of critic network weight update is designed as: Based on the gradient descent strategy, the update strategy of the Critic network weight parameters is obtained as follows: Where η c is the Critic network learning rate; The weight parameters of the Critic network are updated according to the update strategy of the Critic network weight parameters.

4. The method according to claim 3, characterized in that , before step 3.4.2, the method further includes: The estimated error of the Actor network is defined as: The objective function for updating the Actor network weights is designed as: Based on the gradient descent strategy, the update strategy for the Actor network weight parameters is obtained as follows: Where η a is the Actor network learning rate, is the gradient of the activation function with respect to the input; Update the weight parameters of the Actor network according to the update strategy of the Actor network weight parameters.

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