Incremental model prediction control method and device for variant aircraft information, medium and product

Abstract

The invention discloses an incremental model prediction control method and device for variant aircraft information, a medium and a product, and relates to the field of aircraft control, and the method comprises the steps: building a nonlinear model of a variant aircraft based on the variant aircraft information; the information of the variant aircraft comprises speed, angle of attack, pitch angle, height, thrust, resistance, lift force, pitch moment, mass and rotational inertia; classifying and simplifying the nonlinear model of the morphing aircraft according to a time scale separation principle to obtain an incremental equation; based on the incremental equation and an MPC framework, constructing an incremental model prediction controller under input and state constraints; and completing predictive control of information of the variant aircraft based on the incremental model predictive controller. The real-time control precision of the morphing aircraft can be improved while the calculation amount is reduced and the calculation efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of aircraft control, and in particular to an incremental model predictive control method, device, medium and product for variant aircraft information. Background Art

[0002] Most traditional aircraft are designed for specific flight conditions, which results in suboptimal aerodynamic performance in different flight scenarios. Morphing aircraft are a new concept aircraft that can actively switch shapes according to dynamic flight environments or specific mission requirements. They have attracted great attention due to their potential to achieve optimal flight performance in various mission environments, including takeoff, hovering, reconnaissance, attack, and landing. Innovative technologies such as retractable folding wings, morphing swept wings, complex variable geometry wings, and active articulated wings are increasingly becoming the focus of cutting-edge research in aeronautical engineering.

[0003] Morphing aircraft optimize flight performance by dynamically adjusting the aerodynamic structure of the aircraft. However, this deformation can cause significant changes in dynamics, such as changes in the center of gravity, changes in rotational inertia, changes in wingspan and surface area, which can all lead to instability. Therefore, the deformation process brings strong disturbances to the system dynamics and poses challenges to the design of the controller.

[0004] In order to solve the above problems, significant progress has been made in the controller of morphing aircraft. Among them, since linear control theory has been established in control engineering practice for a long time and has shown certain advantages. In the prior art, PID (proportion integration differentiation) control is introduced into the morphing aircraft system. However, due to the nonlinear characteristics and strong coupling of morphing aircraft, traditional linear control methods require small perturbation linearization and decoupling analysis of nonlinear models. However, with the increase of deformation complexity or disturbance level, these methods cannot meet the control performance of morphing aircraft. In recent years, nonlinear control methods with modeling advantages have attracted great attention from scholars and have become the main development direction of morphing aircraft control system design. The nonlinear dynamic inversion (NDI) method uses dynamic inverse transformation and Lyapunov 11 theory to design the control signal of the system. Although dynamic inverse transformation and Lyapunov 11 theory provide robust solutions to a certain extent, they cannot consider input constraints and state constraints.

[0005] Model predictive control (MPC) is an optimization-based control system that has attracted widespread attention due to its ability to systematically handle input and state constraints. The main advantage of MPC is its ability to handle these constraints in a systematic and non-conservative manner while achieving optimal control performance. This unique capability has made it increasingly popular in the field of morphing aircraft control. However, model and parameter uncertainties reduce the prediction accuracy of MPC, which seriously affects the control accuracy. In addition, traditional nonlinear model predictive control (NMPC) has a large computational workload, which also poses a challenge to the real-time control of morphing aircraft. Summary of the invention

[0006] The purpose of this application is to provide an incremental model predictive control method, device, medium and product for variant aircraft information, which can improve the real-time control accuracy of the variant aircraft while reducing the amount of calculation and improving the calculation efficiency.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides an incremental model predictive control method for variant aircraft information, comprising:

[0009] Constructing a nonlinear model of a variant aircraft based on variant aircraft information; the variant aircraft information includes: speed, angle of attack, pitch angle, altitude, thrust, drag, lift, pitch moment, mass and moment of inertia;

[0010] The nonlinear model of the variant aircraft is classified and simplified according to the time scale separation principle to obtain the incremental equation;

[0011] Based on the incremental equations and the MPC framework, an incremental model predictive controller is constructed under input and state constraints;

[0012] Predictive control of variant aircraft information is performed based on the incremental model predictive controller.

[0013] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the incremental model predictive control method for variant aircraft information provided above.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the incremental model predictive control method for variant aircraft information provided above.

[0015] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the incremental model predictive control method for variant aircraft information provided above.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] The present application provides an incremental model predictive control method, device, medium and product for variant aircraft information. The nonlinear model of the variant aircraft is classified and simplified according to the time scale separation principle to obtain an incremental equation, which reduces the dependence on the nominal model, reduces the amount of calculation and improves the calculation efficiency. Based on the incremental equation and MPC framework, an incremental model predictive controller is constructed under input and state constraints, which is robust to dynamic uncertainties. Based on the incremental model predictive controller, accurate predictive control of variant aircraft information is completed. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0019] Figure 1 This is an application environment diagram of an incremental model predictive control method for variant aircraft information in one embodiment of the present application;

[0020] Figure 2 A flowchart of an incremental model predictive control method for variant aircraft information provided by an embodiment of the present application;

[0021] Figure 3 A schematic diagram of a process of changing the wingspan of a Navion L-17 provided in another embodiment of the present application;

[0022] Figure 4 A schematic diagram of a change process of a variable sweep angle of a Navion L-17 provided in another embodiment of the present application;

[0023] Figure 5 A schematic diagram of a hierarchical control structure provided in another embodiment of the present application;

[0024] Figure 6 A schematic diagram of a control structure of an incremental model predictive control (IMPC) provided in another embodiment of the present application;

[0025] Figure 7 A schematic diagram of reference values ​​of speed and altitude in a flight trajectory of an aircraft provided in another embodiment of the present application;

[0026] Figure 8 A schematic diagram of speed tracking error under different controllers provided in another embodiment of the present application;

[0027] Fig. 9 A schematic diagram of height tracking error under different controllers provided by another embodiment of the present application;

[0028] Fig.10 A schematic diagram of elevator angles under different controllers provided in another embodiment of the present application;

[0029] Fig.11 A schematic diagram of throttle opening under different controllers provided in another embodiment of the present application;

[0030] Fig.12 A schematic diagram of a deformation rate reference value variation provided by another embodiment of the present application;

[0031] Fig.13 A schematic diagram of velocity tracking error under deformation provided by another embodiment of the present application;

[0032] Fig.14 A schematic diagram of height tracking error under deformation provided by another embodiment of the present application;

[0033] Fig.15 A schematic diagram of speed tracking error when σ=5% provided in another embodiment of the present application;

[0034] Fig.16 A schematic diagram of a height tracking error of σ=5% provided in another embodiment of the present application;

[0035] Fig.17 A schematic diagram of a speed tracking error of σ=20% provided in another embodiment of the present application;

[0036] Fig.18 A schematic diagram of a height tracking error of σ=20% provided in another embodiment of the present application;

[0037] Fig.19 A schematic diagram of solution time statistics results provided by another embodiment of the present application;

[0038] Fig. 20 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] Compared with traditional aircraft, morphing aircraft have obvious nonlinearity and high uncertainty, which brings great challenges to controller design.

[0041] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0042] The incremental model predictive control method for variant aircraft information provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the variant aircraft information to the server 104. After the server 104 receives the variant aircraft information, the server 104 constructs a nonlinear model of the variant aircraft for the variant aircraft information. The nonlinear model of the variant aircraft is classified and simplified according to the time scale separation principle to obtain the incremental equation. Based on the incremental equation and the MPC framework, an incremental model predictive controller is constructed under input and state constraints. The predictive control of the variant aircraft information is completed based on the incremental model predictive controller. The server 104 can feedback the obtained predictive control results to the terminal 102. In addition, in some embodiments, the incremental model predictive control method for the variant aircraft information can also be implemented separately by the server 104 or the terminal 102, such as the terminal 102 can directly perform predictive control on the variant aircraft information, or the server 104 can obtain the variant aircraft information from the data storage system and perform predictive control on the variant aircraft information.

[0043] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0044] In an exemplary embodiment, Figure 2 As shown, an incremental model predictive control method for variant aircraft information is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 200 to 203. Among them:

[0045] Step 200: constructing a nonlinear model of the morphing aircraft based on the morphing aircraft information. The morphing aircraft information includes: speed, angle of attack, pitch angle, altitude, thrust, drag, lift, pitching moment, mass and moment of inertia.

[0046] Step 201: Classify and simplify the nonlinear model of the variant aircraft according to the time scale separation principle to obtain the incremental equation.

[0047] Step 202: Based on the incremental equations and the MPC framework, an incremental model predictive controller is constructed under input and state constraints.

[0048] Step 203: Complete predictive control of variant aircraft information based on the incremental model predictive controller.

[0049] Implementing the above steps 200 to 203 can reduce the amount of calculation, improve the calculation efficiency, and improve the real-time control accuracy of the variant aircraft. In addition, the present application also enhances the robustness of the controller.

[0050] In another exemplary embodiment of the present application, a variant aircraft Navion L-1726 is considered, in which both the wingspan and the wing sweep angle are symmetrically changed. The wingspan change range is defined as the change range from the original wingspan to twice the wingspan, and the wing sweep angle change range is 0° to 40°. Figure 3 and Figure 4 The changes of the aircraft's wingspan and sweep angle are shown respectively.

[0051] Wingspan change rate ξ b and the sweep angle change rate ξ s They are defined as:

[0052] Where b is the wingspan, b min and b max are the wingspan without deformation and the maximum wingspan respectively. s is the sweep angle, s min and max They are the aircraft's undeformed sweep angle and maximum sweep angle respectively.

[0053] Here we assume that only the longitudinal motion of the aircraft during the wing deformation process is considered. The longitudinal dynamic equation during the deformation process is expressed as:

[0054]

[0055] Where V is velocity, α is angle of attack, θ is pitch angle, and q is pitch rate. h is altitude, and g is gravity constant. T, D, and L represent thrust, drag, and lift, respectively. M is pitch moment. m and I y Represents the mass of the aircraft and its moment of inertia about the pitch axis. is the derivative of *.

[0056] The thrust, drag, lift and pitching moment in formula (1) are:

[0057]

[0058] In the formula, is the engine thrust coefficient, δ t is the throttle opening, S ω Wing reference area, c A is the aerodynamic mean chord length, ρ is the atmospheric density, δ e is the elevator angle. L is the lift coefficient, C D is the drag coefficient, C m are the pitching moment coefficients, respectively expressed as:

[0059]

[0060] Coefficient C = (c ij ) 7×3 , expressed as:

[0061]

[0062] In the formula, represents the lift coefficient when the angle of attack is 0, represents the first-order lift coefficient of the angle of attack, represents the drag coefficient when the angle of attack is 0, is the first-order drag coefficient for the angle of attack, represents the quadratic drag coefficient of the angle of attack, represents the pitch moment coefficient when the angle of attack is 0, represents the pitch moment coefficient of the first-order term of the angle of attack, and C represents the aerodynamic coefficient matrix.

[0063] Finally, the longitudinal aerodynamic model of the variable-wingspan and variable-sweep-angle aircraft is re-expressed as an affine nonlinear model, and the nonlinear model of the variant aircraft can be obtained, which is expressed as:

[0064]

[0065] Where x represents the system state, x=[V α θ qh]T , f(x) represents the offset function of the nonlinear system, g(x) represents the input matrix function of the nonlinear system, u represents the input variable of the nonlinear system, u=[δ e δ t ] T , h(x) represents the function caused by deformation. d represents the deformation variable of the body, d=[ξ b ξ s ] T .

[0066] Among them, f(x), g(x) and h(x) are expressed as:

[0067]

[0068] g(x):=[g (i,j) ] 5×2 .

[0069]

[0070]

[0071] h(x):=[h (i,j) ] 5×2 .

[0072]

[0073]

[0074] In the formula, f i Represents the values ​​of each element of the offset function matrix, i = 1, 2, ..., 5. c 11 、c 31 、c 41 、c 51 、c 12 、c 61 、c 71 、c 32 、c 33 、c 42 、c 52 、c 12 、c 22 、c 62 、c 72 、c 43 、c 53 、c 63 and c 73 All represent the values ​​of each element of the matrix C, g (i,j) Represents the value of each element of the input function matrix, represents the lift coefficient with respect to the elevator variable, Represents the pitching moment coefficient about the elevator variable.

[0075] Based on the description of this embodiment, the deformation process is considered as a disturbance to the dynamics of the aircraft. In addition, aerodynamic modeling errors are inevitable. Therefore, a robust controller is required.

[0076] In another exemplary embodiment of the present application, a robust controller is developed for a morphing aircraft with variable wingspan and variable sweep capability, taking into account state constraints and input constraints, so that the developed controller enables the morphing aircraft to track a predetermined speed and altitude while resisting model uncertainty. At the same time, in order to ensure flight safety, the angle of attack must be kept within an acceptable range. In addition, the actuators of the aircraft are limited by the power system and energy supply design, thereby limiting the input to a certain range.

[0077] The goal of this embodiment is to design a robust optimal controller that satisfies both the safety constraints of the variant aircraft information and the physical constraints of the variant aircraft information, such as the flight angle of attack, elevator angle, and throttle opening. Based on this, the following boundary constraints are considered:

[0078] α min ≤α≤α max ,

[0079] In the formula, α min represents the minimum angle of attack, α max represents the maximum angle of attack, represents the minimum elevator angle, represents the maximum elevator angle, Indicates the minimum throttle opening. Indicates the maximum throttle opening.

[0080] In aircraft safety constraints, the angle of attack is defined as the angle between the chord line and the relative airflow. Generally speaking, an increase in the angle of attack results in an increase in lift. However, every wing has a critical angle of attack, beyond which the airflow separates, resulting in a stall. Keeping the angle of attack below this critical value is essential to prevent a stall.

[0081] In another exemplary embodiment of the present application, in order to enhance robustness and reduce the controller's dependence on the model, the affine nonlinear model (i.e., nonlinear model) of the variant aircraft is approximated as an incremental model, and classified and simplified according to the time-scale separation rule (TSS). Among them, time scale separation: In many complex systems, different processes usually operate on different time scales. Time scale separation can simplify the analysis by decoupling fast dynamics from slow dynamics. Typically, this involves assuming that fast variables quickly reach a quasi-stable state relative to slow variables. Mathematically, a small parameter ε represents the ratio of the fast time scale to the slow time scale. By considering the limit ε→0, simplified models can be derived that capture the basic behavior of the system without the computational complexity of simulating all time scales simultaneously. Based on this, the implementation process of step 201 above can be:

[0082] Step 1: According to the time scale separation principle and the difference between position response and attitude response, the variant aircraft system is divided into a fast subsystem and a slow subsystem.

[0083] In the variant aircraft system, the time scales of various states are very different. The state variables are divided into four levels according to the time scale, as shown in Table 1.

[0084] Table 1 Time scale of state variables

[0085] grade System Status First layer (very fast variables) q Second layer (fast variables) α,θ The third layer (slow variables) V The fourth layer (extremely slow variables) h

[0086] Considering that the position response is slower than the attitude response, the entire aircraft control system is divided into a fast subsystem [i.e., attitude system (system I)] and a slow subsystem [i.e., position system (system II)]. The desired pitch angle θ generated by the upper controller [i.e., the slow subsystem controller, referred to as the slow controller (controller II)] is tracked by controlling the pitch angle θ. d , so as to achieve the tracking of height h. Therefore, the following Figure 5 The two-layer control structure shown.

[0087] Step 2: Based on the nonlinear model of the variant aircraft, the state vector, input vector and deformation input vector of the fast subsystem are determined with the expected speed and expected pitch angle as tracking references.

[0088] For example, in system I, at the desired speed V d and the desired pitch angle θ d As a tracking reference, the state vector of system I is x1 = [V α θ q] T , where the input vector u1 = [δ e δ t ] T , deformation input vector d1 = [ξb ξ s ] T .

[0089] Step 3: Construct the dynamic equation of the fast subsystem based on the state vector, input vector and deformation input vector of the fast subsystem. The dynamic equation of the fast subsystem is expressed as:

[0090]

[0091] In the formula, represents the state derivative of the fast subsystem, f1(x1) represents the offset function matrix of the fast subsystem, g1(x1) represents the input function matrix of the fast subsystem, and h1(x1) represents the function matrix caused by the deformation of the fast subsystem.

[0092]

[0093]

[0094]

[0095]

[0096]

[0097] In the formula, f 1(1) 、f 1(2) 、f 1(3) and f 1(4) All represent the values ​​of each element of the offset function matrix f1(x1), c 61 、c 62 、c 63 、c 72 、c 73 and c 71 All represent the values ​​of each element of the matrix C, g 1(1,1) , g 1(2,1) , g 1(3,1) , g 1(4,1) , g 1(3,2) , g 1(4,2) , g 1(1,2) and g 1(2，2) Both represent the input function matrix g 1(·,·) Each element of (x1), represents the lift coefficient with respect to the elevator variable, represents the pitch moment coefficient about the elevator variable, h (1，1) 、h (2，1) 、h (3，1) 、h (4，1) 、h (1，2) 、h (2，2) 、h (3，2)and h (4，2) Both represent the function matrix h caused by deformation 1(·,·) The elements of (x1).

[0098] Step 4: Based on the nonlinear model of the variant aircraft, the dynamic equation of the slow subsystem is constructed with the expected pitch angle as the tracking reference and the height as the state variable. The dynamic equation of the slow subsystem is expressed as:

[0099]

[0100] In the formula, x2=h represents the state variable of system II. u2 is the virtual input of system II, which is controlled by θ d Tracking reference value h d , that is u2=θ d .

[0101] Step 5: Approximate the dynamic equation of the fast subsystem and the dynamic equation of the slow subsystem respectively, and perform incremental processing to obtain incremental equations. The incremental equations include the incremental equation of the fast subsystem and the incremental equation of the slow subsystem.

[0102] In practical applications, before designing the controller, the uncertain model dynamic equations [i.e., formula (6) and formula (12)] are approximated to enhance the robustness of the system through the incremental system. Take system I as an example. Consider the following nonlinear affine model:

[0103]

[0104] At the current working point (x 1,0 ,u 1,0 ), formula (13) is rewritten as a first-order Taylor series expansion:

[0105]

[0106] Where θ is the partial differential operator and ε1 is the negligible high-order term. And Δx1=x1-x 1,0 , Δu1=u1-u 1,0 .

[0107] According to Table 1 and the TSS principle, the influence of the fast incremental term on the system is much greater than that of the slow incremental term, so the slow incremental term in the equation can be ignored, that is, the velocity dynamics equation ignores the incremental terms of height and velocity, while the angle of attack and pitch dynamics equation ignores the incremental terms of height, velocity, angle of attack and pitch.

[0108] After first-order Taylor expansion and TSS, the system equation of system I is expressed as:

[0109]

[0110] Therefore, in order to realize IMPC in a digital environment, a discrete time system is required. The state differential component at time (k+1) is calculated by using Euler numerical differentiation method. and the state differential of the state at time k Discretize:

[0111]

[0112]

[0113] Where x1(*) is the value of the state variable x1 at time *. s represents the time interval of state update. The incremental element Δx is discretely represented as the difference between consecutive discrete points x(k)-x(k-1), and ω(k) is the discretization error. Since the sampling frequency is fast enough, the discretization error can be ignored. In discrete time, formula (15) can be expressed as:

[0114]

[0115]

[0116]

[0117] q(k+1)=2q(k)-q(k-1)+g 1(4,1) Δδ e +g 1(4,2) Δδ t (twenty one)

[0118] Define X1(k) = [x1(k) x1(k-1)] T , the linear equations (i.e. the incremental equations of system I) are obtained as follows:

[0119] X1(k+1)=A1X1(k)+B1ΔU1(k) (22)

[0120] Wherein, X1(k+1) represents the state variable of the fast subsystem at time k+1, A1 represents the first intermediate matrix, X1(k) represents the state variable of the fast subsystem at time k, B1 represents the second intermediate matrix, ΔU1(k) represents the input vector increment of the fast subsystem at time k, and U1(k) represents the input vector of the fast subsystem at time k.

[0121] in:

[0122]

[0123] X1(k)=[V(k)V(k-1)α(k)α(k-1)θ(k)θ(k-1)q(k)q(k-1)]T .

[0124] ΔU1(k)=[Δδ e (k)Δδ t (k)] T .

[0125]

[0126]

[0127]

[0128] Similarly, the discrete increment equation of system II is:

[0129] X2(k+1)=A2X2(k)+B2ΔU2(k) (23)

[0130] In the formula, X2(k+1) represents the state variable of the slow subsystem at time k+1, A2 represents the third intermediate matrix, X2(k) represents the state variable of the slow subsystem at time k, X2(k) = [h(k) h(k-1)] T B2 represents the fourth intermediate matrix, g2=T s V(k)cos(θ(k)-α(k)). ΔU2(k) represents the input vector increment of the slow subsystem at time k, ΔU2(k)=Δθ d (k). U2(k) represents the input vector of the slow subsystem at time k.

[0131] In order to ensure the approximation accuracy required by the incremental system, the sampling period must be selected to be small enough. The control accuracy is consistent with the sampling rate. For high-precision aircraft systems, the sampling rate can generally reach 100 Hz or even higher. The discrete incremental method of this embodiment can fully meet this requirement.

[0132] In another exemplary embodiment of the present application, in the controller design of variant aircraft information, it is usually required that the aircraft can quickly and smoothly track a given trajectory, while ensuring the smoothness of the system input and the stability of the closed-loop system. In order to ensure that the variant aircraft information flies along the desired trajectory quickly and stably, it is necessary to define a stage cost function that takes into account the tracking error and input. Based on this, the implementation process of the above step 202 can be:

[0133] Step 1: Use the reference state vector to describe the stage loss and build the stage cost model.

[0134] Taking the fast subsystem controller as an example, using the reference state vector Describe the stage loss. Considering that the complete reference trajectory of the aircraft is predetermined, the constructed stage cost model is:

[0135]

[0136] Among them, the symbol and represents the weighted Euclidean norm. Q1 is the tracking error weight matrix, R1 is the input weight matrix of system I, k+i|k For state prediction and system input. is the loss value in the tracking phase.

[0137] Step 2: Determine the optimal control problem with constraints based on the stage cost model.

[0138] Taking the fast subsystem controller as an example, according to formula (24), the IMPC is developed by formulating the following constrained optimal control problem (OCP), which is expressed as:

[0139]

[0140]

[0141]

[0142] In equation (24), the first term on the right side of the equation reflects the control system's ability to track the desired trajectory. Its purpose is to minimize the tracking error and ensure that the aircraft accurately flies along the predetermined path. The second term is used to reduce the degree of input variation, reflecting the system input's requirements for smoothness and energy efficiency. These two terms can ensure optimal performance.

[0143] Step 3: Based on the incremental equation, determine the state prediction sequence and the incremental control sequence respectively. The state prediction sequence includes the state prediction sequence of the fast subsystem and the state prediction sequence of the slow subsystem. The incremental control sequence includes the incremental control sequence of the fast subsystem and the incremental control sequence of the slow subsystem.

[0144] Taking the fast subsystem controller as an example, according to formula (22), Defined as the state prediction sequence of system I, Defined as the incremental control sequence of system I.

[0145] Step 4: Construct an incremental model prediction model based on the state prediction sequence and the incremental control sequence. The incremental model prediction model includes the incremental model prediction model of the fast subsystem and the incremental model prediction model of the slow subsystem. Based on the state prediction sequence and incremental control sequence defined above, the incremental model prediction model of the fast subsystem (i.e., the incremental model prediction process of system I) can be expressed as:

[0146]

[0147] Where M1 is a matrix function about matrix A1, C1 is a matrix function of matrices A1 and B1.

[0148] Step 5: In the MPC framework, based on the incremental model prediction model, the constrained optimal control problem is transformed into a quadratic programming problem to obtain an incremental model prediction controller. The incremental model prediction controller includes an incremental model prediction controller for the fast subsystem and an incremental model prediction controller for the slow subsystem.

[0149] As a summary, Figure 6 The structure and control signal flow of the proposed IMPC are demonstrated.

[0150] In another embodiment of the present application, a series of simulations conducted on variant aircraft verify the effectiveness of the proposed IMPC.

[0151] The nonlinear longitudinal model of the morphing aircraft is derived from the detailed aerodynamic data in the literature "Wang Z, Hou M and Hao M.Morphing-Aided Maneuver Control of Morphing Aircraft with Variable Wing Span and Sweep Angle. In: International Conference on Guidance, Navigation and Control (eds Yan L, Duan H and Deng Y)) Singapore, 2023, pp. 1335-1344. Springer Nature Singapore." The effectiveness of the control method designed in this application is verified by simulating the Navion L-17 aircraft model. The parameters of the aircraft are: mass m = 1247 kg, S ω =17.1m 2 , c A =1.737m, the moment of inertia around the vertical axis is I y =4067.45kg·m 2 , atmospheric density is ρ = 1.0555 kg / m 3 .

[0152] The aerodynamic parameters of the variant aircraft information are: thrust coefficient 41.3N / % is 0.3209, is -1.0199. The coefficient matrix C in formula (4) is:

[0153]

[0154] The controller parameters are selected as follows: prediction step N = 60, sampling period T s =5ms, the weight matrices Q and R are expressed as:

[0155]

[0156] Among them, Q L1 and R L2 are diag{100,0} and diag{0.01} respectively, and Q L2 =diag{0.05,0,0,0,0.01,0,0,0}, R L2 =diag{0.001,0.001}.

[0157] In order to verify the IMPC proposed in this application, three scenarios were designed. First, in order to evaluate the tracking control performance and deformation stability of the IMPC method, comparative experiments were carried out with the most advanced controllers under the same objectives and conditions. Secondly, an experimental scenario with model uncertainty was established, and the robustness of the IMPC method provided in this application under inaccurate modeling conditions was verified through comparative experiments. In addition, the computational efficiency of the IMPC method was compared with other nonlinear model predictive control (NMPC) methods, demonstrating its superior efficiency.

[0158] Among them, scenario 1 (tracking feasibility):

[0159] In order to verify the optimal tracking performance of the IMPC proposed in this application, the following target tracking scenario is designed for the variant aircraft.

[0160] The flight path of the aircraft is shown as follows: Figure 7 Initially, the aircraft maintains a constant speed of 50 m / s at an altitude of 4900 m. At t = 20 seconds, the aircraft begins to accelerate, reaching a speed of 60 m / s at t = 50 and maintaining this speed for the rest of the flight. The aircraft begins to descend at t = 30 seconds, and at t = 90 seconds, the aircraft reaches an altitude of 5000 m. Subsequently, the aircraft maintains this speed and altitude for the duration of the flight.

[0161] Aircraft controllers with the same parameters are selected for comparison, including linear variable parameter control (LPV) and adaptive sliding mode control (ASMC). Assume that the wingspan change rate ξ b and the sweep angle change rate ξ sis fixed to 0. The tracking error and control input under the above trajectory are as follows Figures 8 to 11 shown.

[0162] In order to quantitatively analyze the tracking error of the controller, the root mean square (RMS) values ​​of the speed and height tracking errors are calculated, as shown in Table 2.

[0163] Table 2 RMS value of tracking error

[0164] Incremental Model Predictive Control Adaptive sliding mode control Linear variable parameters V 0.01077 0.03853 0.14440 h 0.07406 0.28366 0.89757

[0165] From the simulation results, it can be seen that Figure 8 , Fig. 9 As shown in Table 2, IMPC has smaller tracking error, faster response speed and better tracking performance than other controllers. Fig.10 , Fig.11 As can be seen from Table 2, the control input of IMPC is relatively small and the fluctuation is small, achieving the optimal control effect.

[0166] Subsequently, in order to verify the attitude stability of the variant aircraft during the deformation process, the flight instructions of the aircraft were set as follows: the aircraft flew at a constant speed of 80 m / s at an altitude of 6,000 m, with a wingspan of ξ between 10 and 40 seconds. b Continuous and uniform variation, sweep angle ξ between 50-80 seconds s Continuous uniform change, such as Fig.12 shown.

[0167] The tracking errors of aircraft speed and altitude under variable wingspan and variable sweep angle are as follows: Fig.13 and Fig.14 The RMS values ​​are shown in Table 3.

[0168] Table 3 RMS value of tracking error under deformation

[0169] Incremental Model Predictive Control Adaptive sliding mode control Linear variable parameter method speed 0.00006261 0.00017713 0.00077345 high 0.06244 0.19176 0.31814

[0170] Obviously, the controller eliminates the instability caused by wing deformation and ensures constant speed and constant altitude flight during the process of changing wingspan and sweep angle. After completing the deformation process, the aircraft can maintain the speed and altitude before deformation and enter a new equilibrium state.

[0171] Scenario 2 (Robustness):

[0172] In order to verify the robustness of the proposed method to model uncertainty, parameter uncertainty is introduced into the controlled object in the simulation of variant aircraft.

[0173] The process of building the morphing aircraft model has some approximate steps and the modeling parameters also have uncertainty. Therefore, the aerodynamic modeling in the nonlinear model of the morphing aircraft is inaccurate, and the controller must have the ability to ensure stability in the case of modeling errors. The unpredictable aspects of the model are characterized by parameter perturbations. In this case, the uncertainty of the model parameters is described by adding perturbations to the aerodynamic coefficients as follows:

[0174]

[0175] Where C' is the disturbance value and C is the nominal value. φ(t) obeys the normal distribution function N(0,σ 2 ), the method proposed in this application and the comparison method use the same sequence of disturbance samples. The comparison results are shown in Figures 15 to 18 The RMS values ​​are shown in Table 4.

[0176] Table 4 RMS values ​​with model disturbance

[0177]

[0178]

[0179] like Figures 15 to 18 As shown in Table 4, introducing disturbances into the model significantly affects the tracking performance of the ASMC controller. The tracking fluctuation at σ = 20% is greater than σ = 5%. In this case, IMPC effectively resists the disturbance caused by model uncertainty because it is independent of the specific mathematical model.

[0180] Scenario 3 (computational efficiency):

[0181] Statistical analysis of the computational efficiency of the controller in scenario 1. Traditional NMPC uses nonlinear models for prediction, so the cost function to be solved is usually a non-convex function and has a local optimum. Solving the global optimum of predictive control requires more complex calculation methods. The process of using the model for prediction is a recursive process, and the prediction function is a nested function, which brings more complex calculations to the non-convex optimization process of NMPC.

[0182] The non-convex optimization process of solving NMPC using sequential quadratic programming (SQP) and particle swarm optimization (PSO) is a widely used method. This embodiment uses these methods to simulate the deformable aircraft. The calculation time of each cycle model prediction control solution is statistically analyzed, and the sampling period of each controller is set to 0.005s. The statistical results under the same simulation computer are as follows: Fig.19As shown. The simulation was created by an AMDTM (Ryzen 75800H@3.2GHz) CPU computer.

[0183] In order to quantitatively analyze the calculation effect, the mean and standard deviation of the single-step solution time are calculated. The results are shown in Table 5:

[0184] Table 5 Average and standard deviation of solution time (unit: s)

[0185] IMPC SQP-NMPC PSO-NMPC average value 0.00222 0.04729 0.14051 Standard Deviation 0.00084 0.00119 0.00333

[0186] from Fig.19 As can be seen from Table 5, the average solution time of IMPC is much shorter than that of SQP-NMPC (sequential quadratic programming and nonlinear model predictive control) and PSO-NMPC (particle swarm optimization and nonlinear model predictive control), indicating that the average solution time of IMPC is relatively short. With a sampling period of 5 ms, real-time control is guaranteed. In addition, the standard deviation of the IMPC solution time is also small, indicating that the IMPC solution time remains consistent at each step, thus proving its computational stability.

[0187] Aiming at the trajectory tracking problem of variant aircraft, this application proposes an incremental model predictive control method. In order to reduce the dependence on specific mathematical models, the system dynamics equations are approximated by incremental systems. The robustness of the controller is improved in terms of high tracking accuracy. Using an approximate discrete linear system, an IMPC that satisfies state and input constraints is formulated, thereby avoiding the need for a specific mathematical model. In addition, the IMPC is converted to a linear MPC. Compared with the nonlinear MPC, the computational complexity of the IMPC is greatly reduced. Finally, the effectiveness of the proposed IMPC is verified through a series of simulations. The simulation results show that the method provided by this application can achieve optimal control performance under both state constraints and input constraints, eliminates the dependence on specific mathematical models, and improves computational efficiency.

[0188] In summary, the present application provides an incremental model predictive control (IMPC) method for variant aircraft information. First, based on the principle of TSS, a two-layer controller is designed. Then, in order to improve the robustness to model uncertainty, a robust incremental model is developed. Next, for each layer, an incremental model predictive controller is developed by constructing an OCP with input and state constraints, where a discrete incremental model is used to generate state predictions. Each restricted OCP of the IMPC is converted into a QP. Based on this, compared with the prior art, the present application has the following advantages:

[0189] 1) Enhanced robustness: Unlike conventional MPC methods, IMPC does not require a specific dynamic model of the deformable aircraft. This is because the method approximates the continuous nonlinear system in incremental form and uses a linear discrete incremental system23-24 for state prediction.

[0190] 2) Optimal control performance: The discrete incremental controller is designed using the MPC framework without terminal components, where the input and state constraints are expressed as inequality constraints in OCP. Therefore, the optimal control performance is obtained while considering both the input and state constraints.

[0191] 3) High computational efficiency: Relying on the incremental method not only enhances the robustness of the controller, but also can transform complex nonlinear equations into linear equations. The developed IMPC is essentially a global linear MPC, and each constrained OCP is converted into a QP. Compared with the traditional nonlinear MPC25, the algorithm significantly reduces the computational complexity and achieves millisecond-level real-time control.

[0192] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig. 20 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store predictive control data for variant aircraft information. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an incremental model predictive control method for variant aircraft information is implemented.

[0193] Those skilled in the art will understand that Fig. 20The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0194] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0195] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0196] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0197] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0198] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0199] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An incremental model predictive control method for variant aircraft information, characterized in that: The incremental model predictive control method for variant aircraft information includes: Constructing a nonlinear model of a variant aircraft based on variant aircraft information; the variant aircraft information includes: speed, angle of attack, pitch angle, altitude, thrust, drag, lift, pitch moment, mass and moment of inertia; The nonlinear model of the variant aircraft is classified and simplified according to the time scale separation principle to obtain the incremental equation; Based on the incremental equations and the MPC framework, an incremental model predictive controller is constructed under input and state constraints; Predictive control of variant aircraft information is performed based on the incremental model predictive controller.

2. The incremental model predictive control method for variant aircraft information according to claim 1, characterized in that: The nonlinear model of the variant aircraft is classified and simplified according to the time scale separation principle to obtain the incremental equation, which specifically includes: According to the principle of time scale separation and the difference between position response and attitude response, the variant aircraft system is divided into fast subsystem and slow subsystem. Based on the nonlinear model of the variant aircraft, the state vector, input vector and deformation input vector of the fast subsystem are determined with the expected speed and the expected pitch angle as tracking references; constructing a dynamic equation of the fast subsystem based on a state vector, an input vector and a deformation input vector of the fast subsystem; Based on the nonlinear model of the variant aircraft, the dynamic equation of the slow subsystem is constructed with the expected pitch angle as the tracking reference and the altitude as the state variable; The dynamic equation of the fast subsystem and the dynamic equation of the slow subsystem are respectively approximated and incrementally processed to obtain the incremental equation; the incremental equation includes the incremental equation of the fast subsystem and the incremental equation of the slow subsystem.

3. The incremental model predictive control method for variant aircraft information according to claim 1, characterized in that: Based on the incremental equation and MPC framework, an incremental model predictive controller is constructed under input and state constraints, specifically including: Use reference state vector to describe stage loss and construct stage cost model; determining a constrained optimal control problem according to the stage cost model; Based on the incremental equation, a state prediction sequence and an incremental control sequence are determined respectively; the state prediction sequence includes a state prediction sequence of a fast subsystem and a state prediction sequence of a slow subsystem; the incremental control sequence includes an incremental control sequence of a fast subsystem and an incremental control sequence of a slow subsystem; An incremental model prediction model is constructed based on the state prediction sequence and the incremental control sequence; the incremental model prediction model includes an incremental model prediction model of a fast subsystem and an incremental model prediction model of a slow subsystem; In the MPC framework, the constrained optimal control problem is transformed into a quadratic programming problem based on the incremental model prediction model to obtain the incremental model prediction controller; the incremental model prediction controller includes an incremental model prediction controller of the fast subsystem and an incremental model prediction controller of the slow subsystem.

4. The incremental model predictive control method for variant aircraft information according to claim 2, characterized in that: The increment equation of the fast subsystem is expressed as: X1(k+1)=A1X1(k)+B1ΔU1(k); Wherein, X1(k+1) represents the state variable of the fast subsystem at time k+1, A1 represents the first intermediate matrix, X1(k) represents the state variable of the fast subsystem at time k, B1 represents the second intermediate matrix, ΔU1(k) represents the input vector increment of the fast subsystem at time k, and U1(k) represents the input vector of the fast subsystem at time k.

5. The incremental model predictive control method for variant aircraft information according to claim 2, characterized in that: The incremental equation of the slow subsystem is expressed as: X2(k+1)=A2X2(k)+B2ΔU2(k); In the formula, X2(k+1) represents the state variable of the slow subsystem at time k+1, A2 represents the third intermediate matrix, X2(k) represents the state variable of the slow subsystem at time k, B2 represents the fourth intermediate matrix, ΔU2(k) represents the input vector increment of the slow subsystem at time k, and U2(k) represents the input vector of the slow subsystem at time k.

6. The incremental model predictive control method for variant aircraft information according to claim 3, characterized in that: The incremental model predictive controller of the fast subsystem is expressed as: In the formula, represents the value function of the fast subsystem, H1 represents the Hessian matrix of the fast subsystem controller, represents the incremental control sequence of the fast subsystem, G1 represents the gradient vector of the slow subsystem controller, Indicates the minimum value of the aircraft's angle of attack, represents the sequence of angles of attack, Indicates the maximum value of the aircraft's angle of attack, Indicates the minimum value of the system input, represents the control signal sequence of the fast subsystem, represents the maximum value of the system input, and T represents the transpose of the matrix.

7. The incremental model predictive control method for variant aircraft information according to claim 3, characterized in that: The incremental model predictive controller of the slow subsystem is expressed as: Where V IIN (X(k),k) represents the value function of the slow subsystem, represents the incremental control sequence of the slow subsystem, H2 represents the Hessian matrix of the slow subsystem controller, G2 represents the gradient vector of the slow subsystem controller, U2(k-1) represents the control input of the slow subsystem at time k-1, T represents the transpose of the matrix, represents the control signal sequence of the slow subsystem, I N×1 Represents a vector of all 1s.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the incremental model predictive control method for variant aircraft information according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the incremental model predictive control method for variant aircraft information according to any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the incremental model predictive control method for variant aircraft information according to any one of claims 1 to 7 is implemented.

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