A composite model predictive control method for hypersonic vehicles considering constraints

Abstract

The present invention discloses a method for complex model predictive control of hypersonic aircraft with constraints. First, a dynamic model of the longitudinal motion system of the hypersonic aircraft is established, and the constraints of the control quantity and its increment, as well as the influence of multi-source interference, are fully considered during the modeling process. Second, based on the extended state observer technology, observers are designed for the pitch rate channel and the throttle opening dynamics to achieve lumped interference estimation. Then, a model predictive controller is designed under the constraints of the control quantity amplitude and change rate. Finally, feedforward compensation is performed in combination with the interference estimation information to construct a complex model predictive controller. The present invention significantly improves the anti-interference capability of the system through interference estimation and feedforward compensation, and ensures that the control quantity amplitude and change rate meet the constraint requirements. The present invention has better anti-interference capability and constraint processing capability, and effectively suppresses the impact of interference on the control performance of the hypersonic aircraft.

Description

Technical Field

[0001] The present invention belongs to the technical field of flight control, and in particular relates to a composite model predictive control method for a hypersonic aircraft considering constraints. Background Art

[0002] Hypersonic aircraft have attracted widespread attention due to their advantages such as high flight speed and strong strike capability. However, due to their wide flight envelope and large speed and airspace coverage, control system design not only faces problems such as strong nonlinearity and strong coupling, but is also affected by multiple sources of interference such as external interference, internal parameter perturbations, and unmodeled dynamics. In addition, due to the limitations of actuator bandwidth and energy, the control aspects of hypersonic aircraft also need to meet the limitations of change rate and amplitude.

[0003] To address the effects of disturbances and system nonlinearities, researchers at home and abroad have proposed a variety of advanced control schemes, including adaptive control, robust control, and sliding mode control. While these schemes have improved the system's nonlinearity and disturbance handling capabilities to a certain extent, their interference suppression strategies passively eliminate the effects of disturbances through error signals, making it difficult to rapidly suppress large disturbances. Furthermore, existing methods are limited by the limitations of their algorithms and rarely consider constraints such as the rate of change and amplitude of the control variable. Therefore, a hypersonic vehicle control method that can rapidly suppress disturbances and handle constraints is urgently needed. Summary of the Invention

[0004] Purpose of the invention: The present invention provides a composite model predictive control method for hypersonic aircraft considering constraints, which realizes the effective estimation of multi-source interference and ensures that the hypersonic aircraft has stronger anti-interference performance and constraint processing capability while meeting the control quantity constraints.

[0005] Technical solution: The present invention provides a method for predictive control of a hypersonic vehicle using a composite model with constraints, comprising the following steps:

[0006] (1) Establish a perturbed dynamics model of the hypersonic vehicle longitudinal motion system considering the second-order dynamics of the engine;

[0007] (2) Based on the disturbed dynamics model of the hypersonic vehicle longitudinal motion system, extended state observers are designed for the pitch rate channel and throttle opening dynamics to obtain disturbance estimation information;

[0008] (3) Design a model predictive controller considering the control variable amplitude and change rate constraints to obtain the optimal control variable considering the constraints;

[0009] (4) Construct a composite model predictive controller based on the disturbance estimation information obtained in step (2) and the optimal control quantity considering the constraints obtained in step (3).

[0010] Furthermore, the perturbed dynamics model of the hypersonic vehicle longitudinal motion system in step (1) is:

[0011]

[0012] Where h is the flight altitude; V is the speed; α and γ are the angle of attack and track angle respectively, q is the pitch rate; r is the distance from the center of the earth, r = h + R E , R E is the radius of the Earth; and Represent the first-order derivatives of h, V, γ, α and q respectively; I yy represents the moment of inertia of the hypersonic vehicle around the y-axis; m is the mass of the vehicle itself; μ is the gravitational constant; D q is the disturbance of the pitch rate channel; L, D, T, and M are lift, drag, engine thrust, and pitch moment, respectively;

[0013] The relationship between the engine throttle opening dynamics and the throttle opening command is:

[0014]

[0015] Among them, δ e represents the elevator deflection angle, c e Indicates the proportional coefficient; β is the engine throttle opening, β c is the throttle opening command; ω n is the angular frequency, represents damping, both are known constants, D β is the disturbance in the throttle opening dynamics;

[0016] The actual control variable in the longitudinal motion model of the hypersonic aircraft is the elevator deflection angle δ e and the engine throttle opening command β c , the control target is to fly the aircraft at the desired flight altitude h r and speed V r Flight; elevator rudder deflection angle δ e and the engine throttle opening command β c The constraints on amplitude and rate of change need to be met:

[0017]

[0018] The state space model is used to represent the perturbed dynamics model (1)-(2) of the longitudinal motion system of a hypersonic vehicle, and the system state x and control variable u are defined as:

[0019] u=[β c δ e ]

[0020] Ignoring the influence of disturbances in the system (1)-(2), at the equilibrium point u=u0=[β c 0 δ e 0 ] T The linearization nearby can be obtained:

[0021]

[0022] Among them: A m is the state transfer matrix, B m is the input matrix, C m is the output matrix, D m is the direct transfer matrix; considering the influence of the pitch rate channel and the throttle opening dynamic disturbance, the disturbed dynamic in equation (3) is abbreviated as:

[0023]

[0024] Among them, a 52 ,a 54 ,a 55 ,b 52 ,a 76 ,a 77 ,b 71 A m and B m The corresponding coefficient term in D q =b 52 ·d q , D β =b 71 ·d β are the interferences of the pitch angle rate and throttle opening channels respectively.

[0025] Furthermore, the lift, drag, engine thrust, and pitching moment are respectively:

[0026] L=QSC L ,D=QSC D ,T=QSC T ,

[0027]

[0028] Where Q is the dynamic pressure, ρ is the atmospheric density, S is the reference area, is the average aerodynamic chord length of the wing; C L , C D , C M , C T They are the aerodynamic coefficients of lift, drag, pitching moment and engine thrust coefficient:

[0029] C L =0.6203α,C D =0.6450α 2 +0.0043378α+0.003772,

[0030] C M (α)=-0.035α 2 +0.036617α+5.3261×10 -6 ,C M (δ e )=c e (δ e -α)

[0031]

[0032] Among them, δ e represents the elevator deflection angle, c e Indicates the proportional coefficient; β is the engine throttle opening, β c is the relationship between the throttle opening command.

[0033] Furthermore, the implementation process of step (2) is as follows:

[0034] Design an extended state observer for the dynamics of the disturbed system and estimate D q 、D β :

[0035]

[0036] in, and is the extended state observer dynamics; and They are and The derivative of and is the observer gain and is a positive constant; To interfere with D q The estimated value of To interfere with D β estimated value.

[0037] Furthermore, the implementation process of step (3) is as follows:

[0038] Define a new augmented state x for the system ignoring the influence of interference A :

[0039] e=[hh r VV r ]T

[0040] Considering the reference instruction h r and V r Usually a constant value, the augmented state x A Dynamics:

[0041]

[0042] in, is the derivative of the control variable u, A, B, and C are constant matrices; combined with the longitudinal motion control target of the hypersonic aircraft system, the cost function is introduced:

[0043]

[0044] Where 0≤τ≤T p , R=diag{r1 r2},W=diag{w1 w2};in the prediction interval [0 T P ] introduces the Laguerre function and uses two sets of standard orthogonal basis functions to approximate Then we get:

[0045]

[0046] Where N1 and N2 represent the number of terms in the function. and are a set of orthogonal basis functions; and is the coefficient vector to be determined,

[0047] According to the properties of Laguerre function:

[0048]

[0049] Where p = [p1 p2] is the time scale factor of the Laguerre function; considering the orthogonality of the Laguerre function, that is, Is the identity matrix, choose a sufficiently large prediction step size T P , so that τ ≥ T P hour, That is, the selected prediction range is greater than the effective time of the control signal, and the second term of formula (8) can be calculated as:

[0050]

[0051] Based on the current state, predict the tracking error of the system at the future moment:

[0052]

[0053] Formula (11) is rewritten as:

[0054]

[0055] Combining equations (8)-(12) we get the cost function expression:

[0056]

[0057] Where η = [η1 η2] T , η is the variable to be optimized. Without considering the constraints, the η that minimizes the cost function is * for:

[0058] η * =-Ω -1 Ψx A (t) (14)

[0059] At any future time t+τ, the control quantity derivative can be obtained by solving equation (9): The optimal value of is:

[0060]

[0061] By applying the rolling optimization principle, that is, the control action only uses the derivative of the future signal at τ = 0; without considering the constraints, for any time t, the optimal control variable change rate is:

[0062]

[0063] The feedback gain matrix is:

[0064]

[0065] Without considering the constraints, the optimal control quantity is obtained:

[0066]

[0067] Considering the constraints of the control variable amplitude and change rate, the model predictive controller is designed: For the constraint of the control variable amplitude, if the sampling interval is Δt, the control signal at time t is:

[0068]

[0069] Where, L(0)=[L 1 (0) L 2 (0)], η=[η1 η2] T ,L(0) T η is the control derivative at the beginning of the optimization window. At any future time τ, the control signal is obtained by adding the control amount at the previous time to the integral of the control signal derivative:

[0070]

[0071] If the sampling interval is Δt, the future control signal at time t+τ is:

[0072]

[0073] The inequality constraint is of the form:

[0074] u min -u(t-Δt)≤C u η≤u max -u(t-Δt) (21)

[0075] in, and are the upper and lower bounds of the control amplitude constraint, respectively, taking τ = 0, C u =[L 1 (0) T Δt L 2 (0) T Δt] T ; In order to constrain the rate of change of the controlled variable, a constraint is imposed on the derivative of the control signal at time t. The constraint is expressed as:

[0076]

[0077] The control derivative constraint for the start of the optimization window is implemented as follows:

[0078]

[0079] in, and are the upper and lower bounds of the control variable change rate constraint respectively;

[0080] It is known that the cost function (13) is a quadratic objective function. Combining the control range and change rate constraints of (13), (21) and (23) is a typical quadratic programming problem:

[0081] min J=η T Ωη+2η T ψx A (t) (24)

[0082] stMη≤γ (25)where:

[0083]

[0084] Solve the optimal coefficient solution of the control quantity under the constraints Then we can get the control variable change rate For the optimal control quantity under the constraints, the integral of the control quantity change rate is obtained:

[0085]

[0086] Furthermore, the composite model predictive controller in step (4) is:

[0087]

[0088] in,

[0089] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: The present invention combines the constraint processing advantages of model predictive control and the rapid interference processing advantages of active interference rejection, realizes the effective estimation of multi-source interference in the longitudinal motion system of hypersonic aircraft, and compensates for the adverse effects of multi-source interference in the form of feedforward interference estimation information, ensuring that the hypersonic aircraft has stronger anti-interference performance and constraint processing capabilities while meeting the control quantity constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is a flow chart of the present invention;

[0091] Figure 2 The height and tracking error response curves of the composite model predictive controller CCMPC with constraints considered in the method proposed in the present invention, the model predictive controller MPC used for comparison, and the model predictive controller CMPC with constraints considered;

[0092] Figure 3 The speed and tracking error response curves of the three methods are shown in Figure 2.

[0093] Figure 4 Here are the response curves of the elevator and its rate of change under the three methods;

[0094] Figure 5 These are the response curves of the throttle opening command and its rate of change under three methods. DETAILED DESCRIPTION

[0095] The present invention will be further described in detail below with reference to the accompanying drawings.

[0096] like Figure 1 As shown, the present invention provides a hypersonic aircraft composite model predictive control method considering constraints, and the specific steps are as follows:

[0097] S1. Establish a disturbed dynamic model of the longitudinal motion system of a hypersonic vehicle considering the second-order dynamics of the engine.

[0098] Establish a perturbed dynamics model of the hypersonic vehicle longitudinal motion system considering the second-order dynamics of the engine:

[0099]

[0100] Where h is the flight altitude; V is the speed; α and γ are the angle of attack and track angle respectively, q is the pitch rate; r is the distance from the center of the earth, r = h + R E , R E is the radius of the Earth; and Represent the first-order derivatives of h, V, γ, α and q respectively; I yy represents the moment of inertia of the hypersonic vehicle around the y-axis; m is the mass of the vehicle itself; μ is the gravitational constant; D q is the disturbance of the pitch rate channel; L, D, T, and M are lift, drag, engine thrust, and pitch moment, respectively, and their expressions are as follows:

[0101] L=QSC L , D=QSC D , T=QSC T ,

[0102] M=QSc[C M (α)+C M (δ e )+C M (q)]

[0103] Where ρ is the atmospheric density, S is the reference area, Q is the dynamic pressure, is the average aerodynamic chord length of the wing; C L , C D , C M , C T They are the aerodynamic coefficients of lift, drag, pitching moment and engine thrust coefficient:

[0104] C L =0.6203α,C D =0.6450α 2 +0.0043378α+0.003772,

[0105] C M (α)=-0.035α 2 +0.036617α+5.3261×10 -6 ,C M (δ e )=c e (δ e -α),

[0106]

[0107] Among them, δ e represents the elevator deflection angle, ce Represents the proportional coefficient; β is the engine throttle opening, and its dynamics is related to the throttle opening command β c Relationship:

[0108]

[0109] Among them, ω n is the angular frequency, represents damping, both are known constants, D β It is the disturbance in the throttle opening dynamics.

[0110] The hypersonic vehicle parameters and atmospheric parameters under certain cruise conditions are shown in Table 1.

[0111] Table 1 Parameter list

[0112]

[0113] The model of the hypersonic aircraft is linearized under cruise conditions (considering engine dynamics). Here, the equilibrium cruise conditions are considered to be V0 = 15060 ft, γ0 = 0, h0 = 110000 ft, q0 = 0, α0 = 0.0312 rad, β0 = 0.1762,

[0114] From the above analysis, we can know that the actual control variable in the longitudinal motion model of the hypersonic aircraft is the elevator rudder deflection angle δ e and the engine throttle opening command β c , the control goal is "the aircraft flies at the desired altitude h r = 609.6 m and speed V r =30.48m / s flight". Taking into account the actual situation in the project, the rudder deflection angle δ of the elevator e and the engine throttle opening command β c The constraints on amplitude and rate of change need to be met:

[0115]

[0116] Using the state space model to represent the hypersonic vehicle dynamics model (1)-(2), the system state x and control variable u can be defined as:

[0117] u=[β c δ e ]

[0118] Ignoring the influence of disturbances in the system (1)-(2), at the equilibrium point u=u0=[β c 0 δ e 0] T The linearization nearby can be obtained:

[0119]

[0120] in:

[0121]

[0122] Considering the influence of the pitch rate channel and the throttle opening dynamic disturbance, the disturbed dynamics in equation (3) can be simplified as:

[0123]

[0124] in, a 77 =-2ζω n , D q =b 52 ·d q , D β =b 71 ·d β are the interferences of the pitch angle rate and throttle opening channels respectively.

[0125] S2. Design extended state observers for the pitch rate channel and throttle opening dynamics respectively.

[0126] The extended state observers are designed for the pitch angle rate and throttle opening channels respectively. The specific design is as follows: the extended state observer is designed for the disturbed system dynamics (4), and D is estimated. q 、D β

[0127]

[0128] in, and is the extended state observer dynamics; and They are and The derivative of and is the observer gain and is a positive constant; To interfere with D q The estimated value of To interfere with D β estimated value.

[0129] S3. Design a model predictive controller considering the control variable amplitude and change rate constraints.

[0130] Design a model predictive controller without considering constraints. The specific design is as follows:

[0131] Define a new augmented state x for system (3) A :

[0132] e=[h-609.6V-30.48] T

[0133] Considering the reference instruction h r and V r Usually a constant, then according to system (3) we can get the augmented state x A Dynamics:

[0134]

[0135] in, is the derivative of the control variable u, A, B, C are constant matrices, and their values ​​are:

[0136] C=[o 2×7 I 2×2 ].

[0137] Combined with the longitudinal motion control objectives of the hypersonic aircraft system, the cost function is introduced:

[0138]

[0139] Where 0≤τ≤T p 、T p =18s, In the prediction interval [0T P ] introduces the Laguerre function and uses two sets of standard orthogonal basis functions to approximate Then we can get:

[0140]

[0141] Where N1=9 and N2=15 represent the number of terms in the function. and are a set of orthogonal basis functions; and is the coefficient vector to be determined,

[0142] According to the properties of the Laguerre function, we can get:

[0143]

[0144] Where p = [p1 p2] is the time scale factor of the Laguerre function, and p1 = 18, p2 = 7. Considering the orthogonality of the Laguerre function, that is Is the identity matrix, choose a sufficiently large prediction step size T P , so that τ ≥ T P hour, That is, the selected prediction range is greater than the effective time of the control signal, and the second term of formula (8) can be calculated as:

[0145]

[0146] Based on the current state, predict the tracking error of the system at the future moment:

[0147]

[0148] Formula (11) can be rewritten as:

[0149]

[0150] Combining equations (8)-(12) yields the cost function expression:

[0151]

[0152] Where η = [η1 η2] T , Obviously, this is a typical quadratic objective function, and η is the variable to be optimized. Without considering the constraints, the η that minimizes the cost function can be obtained. * for:

[0153] η * =-Ω -1 Ψx A (t) (14)

[0154] At any future time t+τ, the control quantity derivative can be obtained by solving equation (9): The optimal value of is:

[0155]

[0156] By applying the rolling optimization principle, that is, the control action only uses the derivative of the future signal at τ = 0, the optimal control variable change rate for any time t is obtained without considering the constraints:

[0157]

[0158] The feedback gain matrix is:

[0159]

[0160] The feedback gain matrix is ​​obtained as K mpc :

[0161]

[0162] Without considering the constraints, the optimal control quantity can be obtained:

[0163]

[0164] If the model predictive controller is designed considering the control variable amplitude and change rate constraints, the design process is:

[0165] Regarding the constraint on the control amplitude, if the sampling interval is Δt = 0.01s, the control signal at time t can be calculated as:

[0166]

[0167] Where, L(0)=[L 1 (0) L 2 (0)], η=[η1 η2] T ,L(0) T η is the control derivative at the beginning of the optimization window. At any future time τ, the control signal can be obtained by adding the control amount at the previous time to the integral of the control signal derivative.

[0168]

[0169] If the sampling interval is Δt, the future control signal at time t+τ can be expressed as:

[0170]

[0171] Therefore, the inequality constraint is of the form:

[0172] u min -u(t-Δt)≤C u η≤u max -u(t-Δt) (21)

[0173] in, and are the upper and lower bounds of the control amplitude constraint, respectively, taking τ = 0, C u =[L 1 (0) T Δt L 2 (0) T Δt] T In order to constrain the rate of change of the controlled variable, a constraint is imposed on the derivative of the control signal at time t. The constraint is expressed as:

[0174]

[0175] The control derivative constraint for the start of the optimization window is implemented as follows:

[0176]

[0177] in, and are the upper and lower bounds of the control variable change rate constraint respectively.

[0178] It is known that the cost function (13) is a quadratic objective function. Combining the control range and change rate constraints of (13), (21) and (23) is a typical quadratic programming problem:

[0179] min J=η T Ωη+2η T ψx A (t) (24)

[0180] stMη≤γ (25)where:

[0181]

[0182] The optimal coefficient solution of the control quantity under the constraints can be solved Then we can get the control variable change rate

[0183] The optimal control quantity under the constraints can be obtained by integrating the rate of change of the control quantity:

[0184]

[0185] S4. Construct a composite model predictive controller based on the disturbance estimation information.

[0186] Based on the disturbance estimation information obtained by observers (5)-(6) in S2 and the optimal control quantity considering the constraints obtained in S3, a composite model predictive controller is constructed:

[0187]

[0188] in,

[0189] In this embodiment, the interference is d β =0.1sin(0.3t);d q =0.06sin(0.3t). Using the Simulink toolbox in the simulation software MATLAB R2020b, a disturbed longitudinal motion model of a hypersonic aircraft considering constraints was constructed for simulation and experiment. Figures 2 to 5The height and speed tracking effects and control quantity response curves under three different methods are shown. Among them, the method of the present invention is a composite constrained model predictive controller (CCMPC); a model predictive controller (MPC) used for comparison; and a constrained model predictive controller (CMPC) used for comparison. Figure 2 The height and tracking error response curves of the three methods are given. It can be seen from the figure that the height tracking error accuracy of the method CCMPC proposed in the present invention is significantly higher than that of the comparison method, and the two comparison methods MPC and CMPC achieve the same tracking accuracy. Figure 3 The speed and tracking error response curves of the three methods are given. It can be seen from the figure that the method of the present invention obtains the best speed tracking accuracy; the two compared methods obtain similar speed tracking accuracy. Figure 4 The response curves of the throttle opening and its rate of change are given. It can be seen from the figure that the proposed method CCMPC and the comparative method CMPC ensure that the throttle opening and its rate of change are within the constraint range, while the throttle opening and its rate of change of the system under the action of the traditional MPC method exceed the constraint range. Figure 5 Response curves for elevator deflection and its rate of change are presented. The figures show that the proposed CCMPC method and the comparative method CMPC ensure that the elevator deflection and its rate of change are within the constraints, while the elevator deflection and its rate of change under the traditional MPC method exceed the constraints. Compared with traditional predictive control methods, the proposed method has better anti-interference and constraint handling capabilities. While satisfying the constraints on the elevator and throttle opening amplitudes and rates of change, it ensures high-precision tracking of the hypersonic vehicle's altitude and speed commands.

[0190] The above-described embodiments merely represent implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for predictive control of a hypersonic vehicle with a composite model considering constraints, characterized in that: The following steps are involved: (1) Establish a perturbed dynamics model of the hypersonic vehicle longitudinal motion system considering the second-order dynamics of the engine; (2) Based on the disturbed dynamics model of the hypersonic vehicle longitudinal motion system, extended state observers are designed for the pitch rate channel and throttle opening dynamics to obtain disturbance estimation information; (3) Design a model predictive controller considering the control variable amplitude and change rate constraints to obtain the optimal control variable considering the constraints; (4) constructing a composite model predictive controller based on the disturbance estimation information obtained in step (2) and the optimal control quantity considering the constraints obtained in step (3); The implementation process of step (2) is as follows: Design an extended state observer for the dynamics of the disturbed system and estimate D q 、D β : in, and is the extended state observer dynamics; and They are and The derivative of and is the observer gain and is a positive constant; To interfere with D q The estimated value of To interfere with D β estimated value of; The implementation process of designing a model predictive controller considering the control variable amplitude and change rate constraints is as follows: Regarding the constraint on the control amplitude, if the sampling interval is Δt, the control signal at time t is: Where, L(0)=[L 1 (0) L 2 (0)], η=[η1 η2] T ,L(0) T η is the control derivative at the beginning of the optimization window. At any future time τ, the control signal is obtained by adding the control amount at the previous time to the integral of the control signal derivative: If the sampling interval is Δt, the future control signal at time t+τ is: The inequality constraint is of the form: u min -u(t-Δt)≤C u η≤u max -u(t-Δt) (21) in, and are the upper and lower bounds of the control amplitude constraint, respectively, taking τ = 0, C u =[L 1 (0) T Δt L 2 (0) T Δt] T ; In order to constrain the rate of change of the controlled variable, a constraint is imposed on the derivative of the control signal at time t. The constraint is expressed as: The control derivative constraint for the start of the optimization window is implemented as follows: in, and are the upper and lower bounds of the control variable change rate constraint respectively; The constraints of control amplitude and rate of change are a typical quadratic programming problem: min J=η T Oh+2h T x A (t) (24) stMη≤γ (25) in: Solve the optimal coefficient solution of the control quantity under the constraints Then we can get the control variable change rate For the optimal control quantity under the constraints, the integral of the control quantity change rate is obtained:

2. The method for predictive control of a hypersonic vehicle with complex model considering constraints according to claim 1, characterized in that: The perturbed dynamics model of the hypersonic vehicle longitudinal motion system in step (1) is: Where h is the flight altitude; V is the speed; α and γ are the angle of attack and track angle respectively, q is the pitch rate; r is the distance from the center of the earth, r = h + R E , R E is the radius of the Earth; and Represent the first-order derivatives of h, V, γ, α and q respectively; I yy represents the moment of inertia of the hypersonic vehicle around the y-axis; m is the mass of the vehicle itself; μ is the gravitational constant; D q is the disturbance of the pitch rate channel; L, D, T, and M are lift, drag, engine thrust, and pitch moment, respectively; The relationship between the engine throttle opening dynamics and the throttle opening command is: Among them, δ e represents the elevator deflection angle, c e Indicates the proportional coefficient; β is the engine throttle opening, β c is the throttle opening command; ω n is the angular frequency, represents damping, both are known constants, D β is the disturbance in the throttle opening dynamics; The actual control variable in the longitudinal motion model of the hypersonic aircraft is the elevator deflection angle δ e and the engine throttle opening command β c , the control target is to fly the aircraft at the desired flight altitude h r and speed V r Flight; elevator rudder deflection angle δ e and the engine throttle opening command β c The constraints on amplitude and rate of change need to be met: The state space model is used to represent the perturbed dynamics model (1)-(2) of the longitudinal motion system of a hypersonic vehicle, and the system state x and control variable u are defined as: Ignoring the influence of disturbance in the perturbed dynamics model (1)-(2) of the longitudinal motion system of the hypersonic aircraft, at the equilibrium point u=u0=[β c 0 δ e 0 ] T The linearization nearby can be obtained: Among them: A m is the state transfer matrix, B m is the input matrix, C m is the output matrix, D m is the direct transfer matrix; considering the influence of the pitch rate channel and the throttle opening dynamic disturbance, the disturbed dynamic in equation (3) is abbreviated as: Among them, a 52 ,a 54 ,a 55 ,b 52 ,a 76 ,a 77 ,b 71 A m and B m The corresponding coefficient term in ; d q , d β are the interferences of the pitch angle rate and throttle opening channels respectively.

3. The method for predictive control of a hypersonic vehicle with complex model considering constraints according to claim 2, characterized in that: The lift, drag, engine thrust, and pitching moment are respectively: Where Q is the dynamic pressure, ρ is the atmospheric density, S is the reference area, is the average aerodynamic chord length of the wing; C L , C D , C M , C T They are the aerodynamic coefficients of lift, drag, pitching moment and engine thrust coefficient: C L =0.6203α,C D =0.6450a 2 +0.0043378a+0.003772,C M (a)=-0.035a 2 +0.036617α+5.3261×10 -6 ,C M (d e )=c e (d e -a) Among them, δ e represents the elevator deflection angle, c e Indicates the proportional coefficient; β is the engine throttle opening, β c is the relationship between the throttle opening command.

4. The method for predictive control of a hypersonic vehicle with complex model considering constraints according to claim 1, characterized in that: The implementation process of step (3) is as follows: Define a new augmented state x for the system ignoring the influence of interference A : Considering the reference instruction h r and V r Is a constant, and the augmented state x A Dynamics: in, is the derivative of the control variable u, A, B, and C are constant matrices; combined with the longitudinal motion control target of the hypersonic aircraft system, the cost function J is introduced: Where 0≤τ≤T p , R=diag{r1 r2},W=diag{w1 w2};in the prediction interval [0 T P ] introduces the Laguerre function and uses two sets of standard orthogonal basis functions to approximate Then we get: Where N1 and N2 represent the number of terms in the function. and are a set of orthogonal basis functions; and is the coefficient vector to be determined, According to the properties of Laguerre function: Where p = [p1 p2] is the time scale factor of the Laguerre function; considering the orthogonality of the Laguerre function, that is, Is the identity matrix, select the prediction step size T P , so that τ ≥ T P hour, That is, the selected prediction range is greater than the effective time of the control signal, and the second term of formula (8) can be calculated as: Based on the current state, predict the tracking error of the system at the future moment: Formula (11) is rewritten as: Combining equations (8)-(12) we get the cost function expression: Where η = [η1 η2] T , η is the variable to be optimized. Without considering the constraints, the η that minimizes the cost function is * for: or * =-Ω -1 Ψx A (t) (14) At any future time t+τ, the control quantity derivative can be obtained by solving equation (9): The optimal value of for: By applying the rolling optimization principle, that is, the control action only uses the derivative of the future signal at τ = 0; without considering the constraints, for any time t, the optimal control variable change rate is obtained for: The feedback gain matrix K mpc for: Without considering the constraints, the optimal control quantity is obtained:

5. The method for predictive control of a hypersonic vehicle with complex model considering constraints according to claim 1, characterized in that: The composite model predictive controller in step (4) is: in,

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