Tightly constrained variant aircraft robust incremental model prediction control method
By constructing a nonlinear model of the morphing aircraft and designing an incremental model predictive control method, the control challenges caused by changes in the dynamic characteristics of the morphing aircraft during the deformation process were solved, and optimized control performance and flight stability were achieved under strict constraints.
Patent Information
- Application Number
- CN202510717937.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-30
- Publication Date
- 2025-09-16
AI Technical Summary
The dynamic characteristics of morphing aircraft change significantly during the deformation process, causing system interference and posing challenges to controller design.
A strictly constrained robust incremental model predictive control method for variant aircraft is adopted. By constructing a nonlinear model of the variant aircraft, a highly dynamic controller is designed, and the attitude dynamics model is expanded by the first-order Taylor. An incremental system is obtained. Combining a following PID controller and an incremental linear system, the tracking phase cost function is designed, the optimal control input incremental sequence is solved, and the control strategy is determined.
The variant aircraft can achieve precise reference trajectory tracking and control performance optimization while meeting strict state and input constraints, ensuring flight stability and safety.
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Figure CN120652775A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of aircraft control, and in particular, to a robust incremental model predictive control method for a strictly constrained variant aircraft. Background Art
[0002] Morphing aircraft, a new concept in aviation, can actively change their shape to adapt to dynamic flight environments and accomplish diverse missions. However, this process can lead to significant changes in dynamic characteristics, such as center of gravity shift, moment of inertia changes, and changes in wingspan and surface area, all of which can potentially cause instability. Consequently, this deformation can introduce significant disturbances into the system and pose challenges to controller design. Summary of the Invention
[0003] In order to overcome at least one deficiency in the prior art, the present application provides a robust incremental model predictive control method for a strictly constrained variant aircraft.
[0004] In a first aspect, a robust incremental model predictive control method for a strictly constrained variant aircraft is provided, comprising:
[0005] Constructing a variant aircraft model, which includes an attitude dynamics model and an altitude dynamics model; and setting constraints on the variant aircraft model;
[0006] Design highly dynamic controllers based on highly dynamic models;
[0007] The first-order Taylor expansion of the posture dynamics model in a small neighborhood near the working point is performed to obtain the incremental system; the incremental system is discretized using the Euler numerical method to determine the incremental linear system;
[0008] Based on the following PID controller and incremental linear system, the tracking phase cost is designed;
[0009] Based on the tracking phase cost, a cost function for the control input increment sequence is constructed;
[0010] Solve the cost function to obtain the optimal control input increment sequence;
[0011] The control strategy is determined based on the optimal control input increment sequence.
[0012] In one embodiment, the highly dynamic controller is:
[0013]
[0014] Among them, θ d is the control law, θ0 is the pitch angle of the stable flight operation point, k P 、k I 、k Dare all parameters of the height dynamic controller, h is the height, h d is the height tracking reference value, and t is the time.
[0015] In one embodiment, the incremental linear system is:
[0016] X1(k+1)=A1X1(k)+B1(k)Δu1(k)
[0017] Among them, X1 is the state quantity, k represents the discrete time, A1 and B1 are coefficient matrices, and Δu1 is the control input increment sequence;
[0018]
[0019]
[0020] Among them, A 11 、A 12 、B 11 、p V,α 、p V,θ 、 is the intermediate quantity; T s is the sampling period; := indicates definition;
[0021]
[0022] p V,θ :=-gcos(θ0-α0)
[0023]
[0024] Where ρ is the air density, V0 is the speed at the working point, S ω is the wing reference area, c ij is the element in the i-th row and j-th column of the coefficient matrix C, α0 is the angle of attack at the working point, g is the acceleration of gravity, θ0 is the pitch angle at the working point, is the thrust coefficient, m is the mass of the variant aircraft, is the coefficient vector, c A is the mean aerodynamic chord length, is the coefficient vector, I y is the moment of inertia about the pitch axis, and V is the velocity.
[0025] In one embodiment, the tracking phase cost is:
[0026]
[0027] F r :=[V d θ d ] T
[0028] Among them, l is the tracking stage cost, C1 is the coefficient matrix, X 1,k+i|k F represents the state prediction value at time k+i predicted by the incremental linear system at the current time k, r is the reference signal, and represents the square of the weighted Euclidean norm, Q1 and R1 are weight matrices; Δu 1,k+i|k represents the control input increment sequence at time k+i predicted by current time k, V d is the reference signal of speed, θ d is the control law, := indicates the definition.
[0029] In one embodiment, the cost function for the sequence of control input increments is:
[0030]
[0031] X 1,k+i+1|k =A1X 1,k+i|k +B1Δu 1,k+i|k
[0032]
[0033] in, Input increment sequence for optimal control, is the control input increment sequence, N is the prediction time domain, l is the tracking stage cost, X 1,k+i|k It represents the state prediction value at time k+i predicted by the incremental linear system at the current time k, Δu 1,k+i|k represents the control input increment sequence at time k+i predicted by current time k, α k+i+1|k represents the angle of attack at time k+i+1 predicted by the current time k, is a set of state quantities, u1(k) is the control input at the current moment k, is the input set, j is the intermediate variable, and A1 and B1 are coefficient matrices.
[0034] In one embodiment, the control strategy is:
[0035]
[0036] in, is the optimal control input at time k+1, u1(k) is the control input at the current time k, Input increment sequence for optimal control In the first column, e1(k) is the error between the actual system state and the nominal system state, and K(k) is the control gain at the current time k.
[0037] In a second aspect, a robust incremental model predictive control device for a strictly constrained variant aircraft is provided, comprising:
[0038] A model building module is used to build a variant aircraft model, which includes an attitude dynamics model and an altitude dynamics model; and set constraints on the variant aircraft model;
[0039] Highly dynamic controller design module, used to design highly dynamic controllers based on highly dynamic models;
[0040] The incremental linear system determination module is used to perform a first-order Taylor expansion on the posture dynamics model in a small neighborhood near the working point to obtain the incremental system; the incremental system is discretized using the Euler numerical method to determine the incremental linear system;
[0041] Tracking stage cost design module, used to design tracking stage cost based on following PID controller and incremental linear system;
[0042] A cost function building module is used to build a cost function on the control input increment sequence according to the tracking stage cost;
[0043] A solution module is used to solve the cost function and obtain the optimal control input increment sequence;
[0044] The control strategy determination module is used to determine the control strategy according to the optimal control input increment sequence.
[0045] Compared with the prior art, the present application has the following beneficial effects: the robust incremental model predictive control method for a strictly constrained variant aircraft of the present application, first, establishes a nonlinear model of the variant aircraft based on the dynamic characteristics of the aircraft, then, in order to reduce the dependence on the mathematical model of the variant aircraft, the continuous-time nonlinear system is reformulated as an incremental system through time delay estimation, and incremental model predictive control is developed based on the incremental system, then, in order to enhance the robustness of the incremental model predictive control in meeting state constraints, additional control terms are designed, and stricter state and input constraints are adopted, and feedback control is used to keep the state tracking error between the actual system and the nominal system within a reachable set. Finally, in order to verify the effectiveness of the method of the present application, numerical simulations were carried out, and the results showed that the method of the present application can achieve tracking performance while meeting strict constraints. BRIEF DESCRIPTION OF THE DRAWINGS
[0046] The present application may be better understood by referring to the following description in conjunction with the accompanying drawings, which together with the following detailed description are incorporated into and form a part of this specification. In the drawings:
[0047] Figure 1A flowchart of a robust incremental model predictive control method for a strictly constrained variant aircraft is shown;
[0048] Figure 2 A schematic diagram of the deformed aircraft is shown;
[0049] Figure 3 Flight instructions for the variant aircraft are shown;
[0050] Figure 4 A schematic diagram of the height response is shown;
[0051] Figure 5 shows a schematic diagram of velocity tracking error;
[0052] Figure 6 A schematic diagram of the angle of attack response is shown. DETAILED DESCRIPTION
[0053] Exemplary embodiments of the present application are described below with reference to the accompanying drawings. For the sake of clarity and conciseness, not all features of actual embodiments are described in this specification. However, it should be understood that in the process of developing any such actual embodiment, many implementation-specific decisions may be made to achieve the developer's specific goals, and these decisions may vary from one implementation to another.
[0054] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, the accompanying drawings only show the device structure closely related to the solution according to the present application, while other details that are not closely related to the present application are omitted.
[0055] It should be understood that the present application is not limited to the described embodiments due to the following description with reference to the accompanying drawings. In this document, where feasible, the embodiments may be combined with each other, features between different embodiments may be replaced or borrowed, and one or more features may be omitted in one embodiment.
[0056] The present invention provides a robust incremental model predictive control method for a strictly constrained variant aircraft (Tube-based incremental model predictive control, Tube-based IMPC), that is, a "Tube"-based IMPC. Figure 1 The flowchart of the robust incremental model predictive control method for a strictly constrained variant aircraft is shown in Figure 1 , methods include:
[0057] Step S1: constructing a variant aircraft model, which includes an attitude dynamics model and an altitude dynamics model; and setting constraints on the variant aircraft model.
[0058] An aircraft with symmetrical wingspan and sweep angle is used as the reference aircraft. The maximum usable wingspan is defined as twice the wingspan, and the sweep angle ranges from 0° to 40°. Figure 2 A schematic diagram of the deformed aircraft is shown.
[0059] Specifically, similar to traditional aircraft modeling methods, the dynamic model is divided into an attitude dynamics model and an altitude dynamics model according to the rate of change of state variables.
[0060] The posture dynamics model is:
[0061]
[0062] Among them, x1 is the state quantity, is the derivative of the state quantity, f1(x1), g1(x1), h1(x1) are all state matrices, u1 is the control input, and d1 is the deformation interference.
[0063] The highly dynamic model is:
[0064]
[0065] Among them, x2 is the state quantity, is the derivative of the state quantity, and f2(x1) is the state matrix.
[0066] x1=[V α θ q] T
[0067] u1=[δ e δ t ] T
[0068] d1=[ξ b ξ s ] T
[0069] Among them, V is the velocity, α is the angle of attack, θ is the pitch angle, q is the pitch angular velocity, δ e is the elevator deflection, δ t is the throttle opening, ξ b is the span deformation rate, ξ s is the sweep angle deformation rate.
[0070]
[0071] Where m is the mass of the variant aircraft, ρ is the air density, S ω is the wing reference area, c ij is the element in the i-th row and j-th column of the coefficient matrix C, g is the acceleration of gravity, I y is the moment of inertia around the pitch axis. c A is the mean aerodynamic chord length.
[0072] here,
[0073] and:
[0074]
[0075] in, are coefficient vectors; C L is the lift coefficient, C D is the drag coefficient, C M is the pitching moment coefficient.
[0076] f2(x1)=Vsin(θ-α)
[0077]
[0078] in, is the thrust coefficient.
[0079] h1(x1)=[h 1,1 (x1)h 1,2 (x1)]
[0080]
[0081]
[0082] Among them, h 1,1 (x1),h 1,2 (x1) are all intermediate quantities.
[0083] The control objectives for a morphing aircraft include: 1) ensuring the morphing aircraft tracks reference signals for altitude and speed. 2) ensuring flight safety by satisfying state and input constraints. In the context of a morphing aircraft, constraints are imposed on the flight angle of attack, elevator angle, and throttle opening. Therefore, the constraints for the morphing aircraft model are:
[0084] α min ≤α≤α max
[0085] δ e,min ≤δ e ≤δ e,max
[0086] δ t,min ≤δ t ≤δ t,max
[0087] Among them, α min is the lower limit of the angle of attack, α max is the upper limit of the angle of attack, δ e,minis the lower limit of the elevator deflection, δ e,max is the upper limit of the elevator deflection, δ t,min is the lower limit of the throttle opening, δ t,max The upper limit of the throttle opening.
[0088] Step S2: designing a height dynamic controller based on the height dynamics model.
[0089] In this embodiment, the main consideration is to meet the angle of attack constraint when the attitude angle changes greatly, so the controller is designed mainly for attitude dynamics. The controller corresponding to altitude dynamics is designed as a following PID controller.
[0090] Specifically, the highly dynamic controller is:
[0091]
[0092] Among them, θ d is the control law, which serves as the tracking reference value of the attitude dynamic controller, θ0 is the pitch angle of the stable flight operation point, k P 、k I 、k D are all parameters of the height dynamic controller, h is the height, h d is the height tracking reference value, and t is the time.
[0093] In step S3, a first-order Taylor expansion is performed on the posture dynamics model in a small neighborhood near the working point to obtain an incremental system; the incremental system is discretized using the Euler numerical method to determine the incremental linear system.
[0094] Specifically, for posture dynamics, at the current working point (x 1,0 ,u 1,0 ) is expanded in a small neighborhood around the first order Taylor, where x 1,0 is the state quantity at the current working point, u 1,0 is the control input in the current operating point.
[0095]
[0096] Where, Δx1:=x1-x 1,0 , represents the increment of the state quantity, Δu1:=u1-u 1,0 , represents the increment of control input, represents the partial differential operator, ∈1 represents the linearization error caused by the expansion process, which can be ignored when the sampling period is small enough.
[0097] When comparing different incremental masses in Δx1, the fast incremental terms have a much greater impact on the system than the slow incremental terms. Due to the time scale separation (TSS) principle, the slower terms can be ignored for the system.
[0098] Subsequently, the incremental system is discretized using the Euler numerical method. Considering that the sampling period is small enough, the discretization error can be ignored. The final incremental linear system is:
[0099] X1(k+1)=A1X1(k)+B1(k)Δu1(k)
[0100] Among them, X1 is the state quantity, k represents the discrete time, A1 and B1 are coefficient matrices, and Δu1 is the control input increment sequence;
[0101]
[0102]
[0103] Among them, A 11 、A 12 、B 11 、p V,α 、p V,θ 、 is the intermediate quantity; T s is the sampling period; := indicates definition;
[0104]
[0105] p V,θ :=-gcos(θ0-α0)
[0106]
[0107] Where ρ is the air density, V0 is the speed at the working point, S ω is the wing reference area, c ij is the element in the i-th row and j-th column of the coefficient matrix C, α0 is the angle of attack at the working point, g is the acceleration of gravity, θ0 is the pitch angle at the working point, is the thrust coefficient, m is the mass of the variant aircraft, is the coefficient vector, c A is the mean aerodynamic chord length, is the coefficient vector, I y is the moment of inertia about the pitch axis, and V is the velocity.
[0108] In step S4, the tracking phase cost is designed based on the following PID controller and the incremental linear system.
[0109] In order to achieve accurate reference trajectory tracking while avoiding controller oscillation and improving energy efficiency, a phase cost function is designed that takes into account both the tracking error and the control signal. Given that the reference trajectory within a finite time horizon starting from time k is known in advance, the tracking phase cost is:
[0110]
[0111] F r :=[V d θ d ] T
[0112] Among them, l is the tracking stage cost, C1 is the coefficient matrix, X 1,k+i|k F represents the state prediction value at time k+i predicted by the incremental linear system at the current time k, r is the reference signal, and represents the square of the weighted Euclidean norm, Q1 and R1 are weight matrices; Δu 1,k+i|k represents the control input increment sequence at time k+i predicted by current time k, V d is the reference signal of speed, θ d is the control law, := indicates the definition.
[0113] Step S5: constructing a cost function for the control input increment sequence based on the tracking phase cost.
[0114] Due to the differences between the established nominal system and the actual system, the state disturbance ω usually contains modeling errors, discretization errors, and TDE errors. Therefore, the actual system can be expressed as follows:
[0115] X1(k+1)=A1X1(k)+B1(k)Δu1(k)+ω1(k)
[0116] The disturbance term ω1(k) is assumed to be bounded, and the nominal system is defined as follows:
[0117]
[0118] in, is the nominal state, and is the control input increment of the nominal system.
[0119] It is assumed that the initial state of the nominal system is the same as the current state of the actual system. However, this condition is difficult to strictly meet in practice. For the nominal system, the deviation between the actual system and the nominal system will be compensated by the current control law within the reachable set. Therefore, the cost function for the control input increment sequence is:
[0120]
[0121] X 1,k+i+1|k =A1X 1,k+i|k +B1Δu 1,k+i|k
[0122]
[0123] in, Input increment sequence for optimal control, is the control input increment sequence, N is the prediction time domain, l is the tracking stage cost, X 1,k+i|k It represents the state prediction value at time k+i predicted by the incremental linear system at the current time k, Δu 1,k+i|k represents the control input increment sequence at time k+i predicted by current time k, α k+i+1|k represents the angle of attack at time k+i+1 predicted by the current time k, is a set of state quantities, u1(k) is the control input at the current moment k, is the input set, j is the intermediate variable, and A1 and B1 are coefficient matrices.
[0124]
[0125] in, is the set of state quantities at time k+i, is the original state quantity set, Γ k+i is a reachable set, is the input set at time k+i, is the original input set, K k+i is the control gain at time k+i, Represents set subtraction.
[0126] Step S6: Solve the cost function to obtain the optimal control input increment sequence.
[0127] Step S7: determining a control strategy according to the optimal control input increment sequence.
[0128] Specifically, the control strategy is:
[0129]
[0130] in, is the optimal control input at time k+1, u1(k) is the control input at the current time k, Input increment sequence for optimal control In the first column, e1(k) is the error between the actual system state and the nominal system state, and K(k) is the control gain at the current time k.
[0131] In a specific embodiment, the effectiveness of the method of the present application is verified by numerical simulation of a variant aircraft model. The parameters of the aircraft are as follows: m = 1247 kg, S ω =17.1m 2 , c A =1.737m, I y =4067.45kg·m 2 , ρ=1.0555kg / m 3 The aerodynamic parameters of the aircraft are selected as follows: The coefficient matrix C is:
[0132]
[0133] The controller parameters are selected as follows: Sampling period T s =5ms, prediction time domain N=60, weight matrices Q1 and R1 are expressed as follows:
[0134]
[0135] where Q L1 and R L1 The values of diag{100,10} and diag{1,0.1} are respectively. Due to the incremental method, the nonlinear system is approximated as a linear incremental system. Therefore, the optimal control problem with tightened constraints is a quadratic programming problem.
[0136] Then, the parameters of the highly dynamic controller are set to: θ0 = 0.001, k P =0.015, k I =0.015, k D =0.0001.
[0137] Figure 3 The flight instructions for the morphing aircraft are shown. First, the aircraft flies at a constant speed of 50 m / s at an altitude of 5000 m. At t = 1 s, the aircraft begins to descend to an altitude of 4900 m and accelerates to 75 m / s. At t = 10 s, the aircraft begins to decelerate to 70 m / s. During the entire flight, the deformation rate is maintained at ξ b =0.8 and ξ s =0.6.
[0138] Aircraft controllers with the same parameters were selected for comparison, including a sliding mode controller with an obstacle Lyapunov function and a nominal increment model predictive control. The simulation results are shown in Figure 2. Figures 4 to 6 As shown, Figure 4 shows a schematic diagram of the height response, Figure 5 shows a schematic diagram of velocity tracking error, Figure 6 A schematic diagram of the angle of attack response is shown.
[0139] Simulation results show that compared with other controllers, the method of this application has smaller tracking error, faster response speed and better tracking performance. Figure 6 The results show that under state constraints, the nominal IMPC method achieves superior optimal performance compared to the sliding mode control (SMC) method, which exhibits conservatism, as evidenced by the large gap between the angle of attack and its upper limit. However, the nominal MPC method violates state constraints and cannot guarantee strict state constraints, which could threaten flight stability and safety in real-world flight scenarios. In contrast, the proposed method (Tube-based IMPC) ensures optimal control performance while strictly adhering to state constraints.
[0140] In summary, the robust incremental model predictive control method for strictly constrained variant aircraft of the present application first establishes a nonlinear model of the variant aircraft based on the dynamic characteristics of the aircraft. Subsequently, in order to reduce the dependence on the mathematical model of the variant aircraft, the continuous-time nonlinear system is reformulated as an incremental system through time delay estimation, and incremental model predictive control is developed based on the incremental system. Then, in order to enhance the robustness of the incremental model predictive control in meeting state constraints, additional control terms are designed, and stricter state and input constraints are adopted. Feedback control is used to keep the state tracking error between the actual system and the nominal system within the reachable set. Finally, in order to verify the effectiveness of the method of the present application, numerical simulations were carried out, and the results showed that the method can achieve tracking performance while meeting strict constraints.
[0141] Based on the same inventive concept as the method for robust incremental model predictive control of a strictly constrained variant aircraft, this embodiment further provides a corresponding apparatus for robust incremental model predictive control of a strictly constrained variant aircraft, including:
[0142] A model building module is used to build a variant aircraft model, which includes an attitude dynamics model and an altitude dynamics model; and set constraints on the variant aircraft model;
[0143] Highly dynamic controller design module, used to design highly dynamic controllers based on highly dynamic models;
[0144] The incremental linear system determination module is used to perform a first-order Taylor expansion on the posture dynamics model in a small neighborhood near the working point to obtain the incremental system; the incremental system is discretized using the Euler numerical method to determine the incremental linear system;
[0145] Tracking stage cost design module, used to design tracking stage cost based on following PID controller and incremental linear system;
[0146] A cost function building module is used to build a cost function on the control input increment sequence according to the tracking stage cost;
[0147] A solution module is used to solve the cost function and obtain the optimal control input increment sequence;
[0148] The control strategy determination module is used to determine the control strategy according to the optimal control input increment sequence.
[0149] The strictly constrained variant aircraft robust incremental model predictive control device of this embodiment has the same inventive concept as the strictly constrained variant aircraft robust incremental model predictive control method mentioned above. Therefore, the specific implementation method of the device can be seen in the embodiment part of the strictly constrained variant aircraft robust incremental model predictive control method mentioned above, and its technical effects correspond to the technical effects of the above method, which will not be repeated here.
[0150] The above descriptions are merely examples of various embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be readily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.
Claims
1. A robust incremental model predictive control method for a strictly constrained variant aircraft, characterized by: include: Constructing a variant aircraft model, wherein the variant aircraft model includes an attitude dynamics model and an altitude dynamics model; And set restrictions on variant aircraft models; designing a height dynamic controller based on the height dynamics model; Performing a first-order Taylor expansion on the posture dynamics model in a small neighborhood near the working point to obtain an incremental system; discretizing the incremental system using the Euler numerical method to determine an incremental linear system; Based on the following PID controller and the incremental linear system, designing the tracking phase cost; constructing a cost function for a control input increment sequence based on the tracking phase cost; Solving the cost function to obtain an optimal control input increment sequence; A control strategy is determined according to the optimal control input increment sequence.
2. The method according to claim 1, wherein The highly dynamic controller is: Among them, θ d is the control law, θ0 is the pitch angle of the stable flight operation point, k P 、k I 、k D are all parameters of the height dynamic controller, h is the height, h d is the height tracking reference value, and t is the time.
3. The method according to claim 1, wherein The incremental linear system is: X1(k+1)=A1X1(k)+B1(k)Δu1(k) Among them, X1 is the state quantity, k represents the discrete time, A1 and B1 are coefficient matrices, and Δu1 is the control input increment sequence; Among them, A 11 、A 12 、B 11 、p V,α 、p V,θ 、 is the intermediate quantity; T s is the sampling period; := indicates definition; p V,θ :=-gcos(θ0-α0) Where ρ is the air density, V0 is the speed at the working point, S ω is the wing reference area, c ij is the element in the i-th row and j-th column of the coefficient matrix C, α0 is the angle of attack at the working point, g is the acceleration of gravity, θ0 is the pitch angle at the working point, is the thrust coefficient, m is the mass of the variant aircraft, is the coefficient vector, c A is the mean aerodynamic chord length, is the coefficient vector, I y is the moment of inertia about the pitch axis, and V is the velocity.
4. The method according to claim 1, wherein The tracking phase costs are: F r :=[V d i d ] T Among them, l is the tracking stage cost, C1 is the coefficient matrix, X 1,k+i|k F represents the state prediction value at time k+i predicted by the incremental linear system at the current time k, r is the reference signal, and represents the square of the weighted Euclidean norm, Q1 and R1 are weight matrices; Δu 1,k+i|k represents the control input increment sequence at time k+i predicted by current time k, V d is the reference signal of speed, θ d is the control law, := indicates the definition.
5. The method according to claim 1, wherein The cost function for the control input increment sequence is: X 1,k+i+1|k =A1X 1,k+i|k +B1Δu 1,k+i|k in, Input increment sequence for optimal control, is the control input increment sequence, N is the prediction time domain, l is the tracking stage cost, X 1,k+i|k It represents the state prediction value at time k+i predicted by the incremental linear system at the current time k, Δu 1,k+i|k represents the control input increment sequence at time k+i predicted by current time k, α k+i+1|k represents the angle of attack at time k+i+1 predicted by the current time k, is a set of state quantities, u1(k) is the control input at the current moment k, is the input set, j is the intermediate variable, and A1 and B1 are coefficient matrices.
6. The method according to claim 1, wherein The control strategy is: in, is the optimal control input at time k+1, u1(k) is the control input at the current time k, Input increment sequence for optimal control In the first column, e1(k) is the error between the actual system state and the nominal system state, and K(k) is the control gain at the current time k.
7. A robust incremental model predictive control device for a strictly constrained variant aircraft, characterized in that: include: A model building module is used to build a variant aircraft model, wherein the variant aircraft model includes an attitude dynamics model and an altitude dynamics model; and set constraints on the variant aircraft model; A highly dynamic controller design module, configured to design a highly dynamic controller based on the highly dynamic model; an incremental linear system determination module, configured to perform a first-order Taylor expansion on the posture dynamics model in a small neighborhood near the working point to obtain an incremental system; and discretize the incremental system using an Euler numerical method to determine the incremental linear system; A tracking phase cost design module, configured to design a tracking phase cost based on the following PID controller and the incremental linear system; A cost function building module, configured to build a cost function on a control input increment sequence according to the tracking phase cost; A solution module, configured to solve the cost function to obtain an optimal control input increment sequence; A control strategy determination module is used to determine a control strategy according to the optimal control input increment sequence.