A Control Method for Disturbed Modular Aircraft Based on Switching Model Prediction

Abstract

This invention discloses a control method for disturbed variable-configuration aircraft based on switching model prediction. It addresses the shortcomings of traditional non-optimal control methods for disturbed variable-configuration aircraft, which lack the ability to generate optimal control surface commands based on different performance optimization indices under flight capability constraints and cannot guarantee the stability of the closed-loop system under small disturbances. The proposed control method enables robust optimization control of variable-configuration aircraft under multiple constraints, ensuring the feasibility of the optimization control algorithm and the stability of the closed-loop system, thus contributing to improved flight performance of variable-configuration aircraft.

Description

Technical Field

[0001] This invention belongs to the field of optimized control of variable-structure aircraft and relates to a control method for a subsonic small-disturbance variable-span aircraft, which is used to realize the optimized control of autonomous control surface deflection on a variable-structure aircraft. Background Technology

[0002] Traditional fixed-configuration aircraft, due to their relatively simple structure and single flight mode, often can only meet specific mission requirements. With the continuous development of science and technology, to cope with complex flight environments and diverse mission demands, future aircraft development is moving towards unmanned, autonomous, and intelligent capabilities. As a major development direction for next-generation aircraft technology, variable-configuration aircraft possess various modification methods to adjust local or overall shapes. Compared to fixed-configuration aircraft, variable-configuration aircraft have a wider flight speed range and a superior aerodynamic shape, significantly expanding existing flight capability boundaries and enabling efficient flight across wider airspaces, at greater altitudes, and with longer endurance.

[0003] Generally, disturbed discrete aircraft can be constructed into multiple discrete configurations according to flight performance requirements (ignoring the modification process). Therefore, subsystems for each discrete configuration can be established based on the dynamic parameters of different discrete configurations, and a switching logic model can be used to create an aircraft configuration switching system. Currently, the control methods for disturbed discrete aircraft configuration switching systems mainly include traditional linear control methods and emerging intelligent control methods. Although traditional linear control methods (PID, disturbance rejection control methods, etc.) can complete the aircraft control task through multi-stage piecewise or decoupling methods, they still lack specific control strategies to cope with the flight capability constraints of different configurations under small disturbances and different performance optimization indices. Although intelligent control methods can achieve control based on optimization objectives under constraints through neural networks, reinforcement learning, etc., intelligent control methods often rely on massive amounts of flight data and the related learning strategies lack theoretical support.

[0004] Therefore, designing robust optimization control strategies for aircraft configuration switching systems to achieve optimal control of specific flight targets under various constraints and small disturbances is one of the current challenges and urgent problems to be solved. It is necessary to propose a control method for disturbed configuration-switching aircraft based on switching model prediction. Summary of the Invention

[0005] The technical problem solved by this invention is to overcome the shortcomings of the prior art. This invention proposes a control method for disturbed morphologically alternating aircraft based on switching model prediction, which solves the problem that the existing control methods for morphologically alternating switching systems of disturbed aircraft lack the ability to process constraints and achieve specific optimization objectives under small disturbances without relying on flight data.

[0006] The technical solution of this invention is:

[0007] A control method for a disturbed morphologically modified aircraft based on switching model prediction, comprising the following steps:

[0008] (1) Determine the linearized switching dynamics model of the disturbed aircraft under different configurations and determine the variable constraints under each configuration; the variable constraints include disturbance variable constraints, flight state constraints, and control surface saturation constraints.

[0009] (2) Based on the disturbance variable constraints, construct the switching disturbance invariant set for all configurations of the aircraft to obtain the switching configuration-dependent residence time constraints of different configuration subsystems; the different configuration subsystems refer to the aircraft dynamics model under different configuration variables;

[0010] (3) Based on the flight state constraints, control surface saturation constraints determined in step (1) and the switching disturbance invariant set determined in step (2), calculate the compressed set of flight state and control surface saturation constraints under different aircraft configurations.

[0011] (4) Design the optimal control objective function for different aircraft configurations;

[0012] (5) Based on the constrained compact set and the optimization control objective function, establish the switching model predictive control problem for each configuration of the aircraft in the nominal system of the variable configuration aircraft.

[0013] The nominal system of the modified aircraft refers to a linearized switching dynamics model without disturbance;

[0014] (6) For the switching model predictive control problem described in step (5), determine the feasibility conditions for configuration-dependent residence time in a certain configuration;

[0015] (7) For the switching model predictive control problem described in step (5), determine the configuration-dependent residence time stability condition for staying in a certain configuration;

[0016] (8) Based on the configuration-dependent residence time constraint determined in step (2), the configuration-dependent residence time feasibility condition determined in step (6), and the configuration-dependent residence time stability condition determined in step (7), determine the maximum lower bound constraint of configuration-dependent residence time.

[0017] (9) Based on the maximum lower bound constraint, according to the aircraft configuration at each sampling time, solve the switching model predictive control problem under the corresponding configuration in step (5) to obtain the optimal control surface command at the current time; repeat step (5) to obtain the optimal control surface command at the next time.

[0018] Furthermore, the linearized switching dynamics model for different configurations of the disturbed aircraft described in step (1) is specifically as follows:

[0019] The linearized state-space equations for the longitudinal aircraft's metamorphic switching are as follows:

[0020]

[0021] In the formula Δα k Let Δω be the angle of attack bias at time k. k Let Δδ be the pitch angular velocity deviator at time k. k Let be the control input for the control surface bias at time k; the variable configuration switching law σ(k) is a piecewise constant function of time k, Σ is the set of all possible switching configurations of the aircraft, Σ:={1,2,…,m,…,M}, where m represents the code of the dynamic system of the aircraft with different configurations; M represents the number of configurations that the aircraft can switch to, A σ(k) Represents the system matrix and B under different transformation switching laws. σ(k) Represents the input matrix and d under different modal switching laws. k This represents the disturbance variable experienced by the aircraft;

[0022] To simplify, let Δq k =[Δα k Δω k ] T Let k be the state of the spacecraft at time k;

[0023] Determine the set of constraints for aircraft state, control surface bias, and disturbance variables under different configurations:

[0024] Δq k ∈Θ σ(k) ,Δδ k ∈U σ(k) ,d k ∈D

[0025] In the formula Θ σ(k) U σ(k) Let be the constraint sets under different configurations of aircraft state and control surface bias, respectively, and D be the constraint set of disturbance variables.

[0026] Furthermore, in step (2), a switching disturbance invariant set is constructed for all configurations of the aircraft, specifically as follows:

[0027] Step (2a): Let j = 1, calculate v = τ for each different configuration subsystem. m ,τ m +1,…,2τ m -1, m∈Σ steps reachable set

[0028]

[0029] In the formula, τ m This indicates the configuration switching dependence of different configuration aircraft dynamics system codes on dwell time. K m ,m∈Σ is the configuration-dependent feedback control gain, Am The system matrix and B represent the system matrix under the dynamic system designation of different configuration aircraft. m Let O0 represent the input matrix under the code name of the dynamic system of different configuration aircraft, where O0 = {0} and the zero-step reachable set is... j represents the number of algorithm iterations, and j is a positive integer, starting from j=1;

[0030] Step (2b): Calculate the convex hull of the v-step reachable set for each subsystem with different configurations.

[0031] Ω j =co{O j}

[0032]

[0033] co represents the convex hull calculation operation;

[0034] Step (2c): If Ω j =Ω j-1 If not satisfied, let j = j + 1, that is, increment the value of j by 1, and repeat steps (2a) to (2b) until Ω. j =Ω j-1 Obtain the switching interference invariant set Ω ∞ ;

[0035] The minimum configuration for the existence of the switching disturbance invariant set depends on the residence time τ. m "This refers to the obtained switching configuration-dependent residence time constraint."

[0036] Furthermore, the calculation method for the flight state and control surface saturated constraint compact set under different aircraft configurations in step (3) is as follows:

[0037]

[0038] In the formula These are the constraint sets for different configurations of the aircraft state and control surface offsets after compression; Θ m U m These are the sets of aircraft states and control surface bias constraints under the dynamic system codes of different aircraft configurations.

[0039] Furthermore, the optimization control objective function designed in step (4) is:

[0040]

[0041] In the formula Δq k+i|k =[Δα k+i|k Δω k+i|k ] T ,Δδ k+i|k These represent the predicted state of the variable-structure aircraft and the control surface offset at time k+i, respectively, with N... mQ represents the length of the prediction time domain under different configurations. m With R m These are the weight matrices of the aircraft state variables and the positive definite matrices of the control surface bias weights, respectively, for different configurations. Let P be the terminal cost function, where P is the terminal cost function. m This is the positive definite matrix of terminal state weights.

[0042] Furthermore, step (5) establishes the switching model predictive control problem for each configuration of the aircraft under the nominal system of the variable-configuration aircraft as follows:

[0043]

[0044] In the formula For nominal aircraft configuration switching system, among which, This is the nominal control surface offset. For nominal flight conditions,

[0045] Predict the optimal control surface bias sequence at time k. Let Ξ be the initial state value. m This represents the set of terminal constraints for different configurations of the aircraft.

[0046] Furthermore, the feasibility condition for configuration-dependent residence time in step (6) under a certain configuration is as follows:

[0047]

[0048] In the formula τ m For the configuration-dependent dwell time of the aircraft to satisfy the feasibility conditions, the initial feasible region for the switching model predictive control problem to be continuously solvable under configuration m is:

[0049]

[0050] Furthermore, the configuration-dependent residence time stability condition under a certain configuration in step (7) is:

[0051]

[0052] In the formula τ m ′ represents the minimum configuration-dependent dwell time for the aircraft to satisfy the feasibility conditions, E ∞ To shrink the terminal set, the terminal set is calculated as follows:

[0053] Step (7a): Define the v-step backward reachable set of set Z as:

[0054] Step (7b): Calculate v = τ for each different configuration subsystem m ,τ m +1,…,2τm -1,m∈Σ step backward reachable set intersection

[0055]

[0056] In the formula, λ∈(0,1) is the contraction factor, and Ε0=∩ m∈Σ Ξ m j is a positive integer representing the number of algorithm iterations;

[0057] Step (7c): Calculate E j =Ε j-1 ∩Λ;

[0058] Step (7d): If E j =Ε j-1 If not satisfied, j = j + 1, repeat steps (7a) to (7b) until E. j =Ε j-1 Obtain the shrink terminal set E ∞ .

[0059] Furthermore, step (8) determines the maximum lower bound constraint on configuration-dependent residence time as follows:

[0060] τ m ≥τ m ,τ m ≥τ m ′and τ m ≥τ m ",m∈Σ

[0061] In the formula τ m This is a constraint on the maximum lower bound of configuration-dependent dwell time for different aircraft configurations.

[0062] Furthermore, in step (9), based on the aircraft configuration at each sampling time, the model predictive control problem under the corresponding configuration in step (5) is solved to obtain the optimal control surface command at the current time. Specifically

[0063]

[0064] In the formula The optimal control sequence for the control surface is obtained by solving the model predictive control problem under the corresponding configuration in step (5) at time k. The first element; repeat step (5) to obtain the optimal control surface command for the next moment.

[0065] The advantages of this invention compared to the prior art are:

[0066] 1. The model predictive control method for the configuration switching system of disturbed aircraft proposed in this invention, compared with non-optimized traditional control methods, can design optimization objective functions according to different optimization indices under different configurations, thereby achieving optimal control surface control for different configurations. Furthermore, compared with traditional control methods, the control method of this invention can effectively handle flight state and control surface deflection constraints, achieving optimal control surface control under complex constraints of the configuration-switching aircraft.

[0067] 2. Under bounded small disturbances, the control method of the present invention, compared with the model prediction robust control method without dynamic model switching, can realize small disturbance robust model prediction control under the switching of different configuration subsystems of the aircraft, while ensuring the feasibility of the switching model prediction algorithm and the stability of the closed-loop system. Attached Figure Description

[0068] Figure 1 This is a flowchart of the method of the present invention;

[0069] Figure 2 These are schematic diagrams of fixed-configuration and variable-configuration aircraft. Detailed Implementation

[0070] Modular aircraft can achieve a superior aerodynamic configuration by adjusting local or overall shapes to adapt to different flight mission requirements. Therefore, compared to fixed-configuration aircraft, they possess a wider range of flight capabilities and have attracted widespread attention. Figure 2 The diagram shows the structural schematics of a fixed-configuration aircraft and a variable-configuration aircraft. However, traditional non-optimal control methods for disturbed variable-configuration aircraft lack the ability to generate optimal control surface commands based on different performance optimization indices under flight capability constraints, and cannot guarantee the stability of the closed-loop system under small disturbances. Although intelligent control methods can achieve control based on optimization objectives under constraints through neural networks, reinforcement learning, and other means, intelligent control methods often rely on massive amounts of flight data, and the related learning strategies lack feasibility and stability theoretical guarantees. To address this issue, this invention proposes a control method for disturbed variable-configuration aircraft based on switching model prediction. This method can achieve robust optimization control of variable-configuration aircraft under multiple constraints, and ensures the feasibility of the optimization control algorithm and the stability of the closed-loop system, thus helping to improve the flight performance of variable-configuration aircraft.

[0071] like Figure 1 As shown, this invention designs a control method for a disturbed variable-structure aircraft based on switching model prediction, achieving robust optimization control of the variable-structure aircraft under small disturbances and multiple constraints. Theoretically, sufficient conditions are proposed to guarantee the feasibility of the optimization control algorithm and the stability of the closed-loop system. The specific steps are as follows:

[0072] (1) Give linearized switching dynamics models of the disturbed aircraft under different discrete configurations and determine the constraints of each variable under each configuration. The variable constraints include disturbance variable constraints, flight state constraints, and control surface saturation constraints.

[0073] The optimized control method of this invention is designed for the longitudinal dynamics model of a subsonic, small-disturbance variant aircraft. In step (1), the dynamics model is the longitudinal dynamics model of the variant aircraft after the small-disturbance linearization method, and the longitudinal dynamics model ignores the yaw motion of the aircraft. The system matrix A for different configurations in the linearized model is... m With B m The value of m∈Σ can be obtained by taking the partial derivative of the Jacobian matrix in the equilibrium flight state.

[0074] The linearized switching dynamics model for different configurations of the disturbed aircraft is as follows:

[0075] The linearized state-space equations for the longitudinal aircraft's metamorphic switching are as follows:

[0076]

[0077] In the formula Δα k Let Δω be the angle of attack bias at time k. k Let Δδ be the pitch angular velocity deviator at time k. k Let be the control input for the control surface bias at time k; the variable configuration switching law σ(k) is a piecewise constant function of time k, Σ is the set of all possible switching configurations of the aircraft, Σ:={1,2,…,m,…,M}, where m represents the code of the dynamic system of the aircraft with different configurations; M represents the number of configurations that the aircraft can switch to, A σ(k) Represents the system matrix and B under different transformation switching laws. σ(k) Represents the input matrix and d under different modal switching laws. k Let Δq represent the disturbance variable experienced by the aircraft; for simplicity, let Δq be the variable. k =[Δα k Δω k ] T Let k be the state of the spacecraft at time k.

[0078] Determine the set of constraints for aircraft state, control surface bias, and disturbance variables under different configurations:

[0079] Δq k ∈Θ σ(k) ,Δδ k ∈U σ(k) ,d k ∈D

[0080] In the formula Θ σ(k) U σ(k) Let be the constraint sets under different configurations of aircraft state and control surface bias, respectively, and D be the constraint set of disturbance variables.

[0081] (2) Based on the disturbance variable constraints, construct the switching disturbance invariant set for all aircraft configurations, and obtain the configuration-dependent residence time constraints for different configuration subsystems.

[0082] The different configuration subsystems refer to the aircraft dynamics models under different configuration variables (such as different spans, chord lengths, etc.).

[0083] The purpose of calculating the switching disturbance invariant set is to determine the maximum impact of the cumulative disturbance variables on the aircraft's switching configuration system, and to prevent the constraints of each variable from being violated due to the presence of disturbance variables. The specific calculation method is as follows:

[0084] Construct a switching disturbance invariant set for all aircraft configurations, specifically as follows:

[0085] Step (2a): Let j = 1, calculate v = τ for each different configuration subsystem. m ,τ m +1,…,2τ m -1, m∈Σ steps reachable set

[0086]

[0087] In the formula, τ m This indicates the configuration switching dependence of different configuration aircraft dynamics system codes on dwell time. K m ,m∈Σ is the configuration-dependent feedback control gain, A m The system matrix and B represent the system matrix under the dynamic system designation of different configuration aircraft. m Let O0 represent the input matrix under the code name of the dynamic system of different configuration aircraft, where O0 = {0} and the zero-step reachable set is... j represents the number of algorithm iterations, and j is a positive integer, starting from j=1;

[0088] Step (2b): Calculate the convex hull of the v-step reachable set for each subsystem with different configurations.

[0089] Ω j =co{O j}

[0090]

[0091] co represents the convex hull operation.

[0092] Step (2c): If Ω j =Ω j-1 If not satisfied, let j = j + 1, that is, increment the value of j by 1, and repeat steps (2a) to (2b) until Ω. j =Ω j-1 Obtain the switching interference invariant set Ω ∞;

[0093] The minimum configuration for the existence of the switching disturbance invariant set depends on the residence time τ. m "This refers to the obtained switching configuration-dependent residence time constraint."

[0094] The calculation of the v-step reachable set in step (2a) is for the error system constructed by the actual dynamic model of the disturbed modified aircraft (the model in step (1)) and the nominal dynamic model that ignores disturbance variables (the model in step (5)).

[0095]

[0096] Flight state error in the formula

[0097] In step (2a), the number of steps to calculate the reachable set is designed as v = τ. m ,τ m +1,…,2τ m -1,m∈Σ, because the aircraft configuration switching sequence cannot be exhaustively enumerated, but can be divided into sub-sequence units that satisfy the configuration-dependent dwell time constraint. Therefore, only the sub-switching sequence units are considered when calculating the reachable set.

[0098] The reason for calculating the convex hull of the reachable set of each subsystem with different configurations in step (2b) instead of the union is that the union calculation cannot guarantee the convexity of the set, while the convex hull calculation can guarantee the convexity of the disturbance-invariant set, which facilitates the subsequent design of predictive controllers based on convex optimization.

[0099] (3) Calculate the flight state and control surface saturation constraint compact set under different aircraft configurations.

[0100] To facilitate the subsequent switching model predictive control problem in step (5) of the nominal system design, the switching disturbance invariant set Ω calculated in step (2) is used as a basis. ∞ The Pontryagin set subtraction method is used to calculate the compact sets for different flight states and control surface constraints. Configuration-dependent feedback gain K is then calculated. m It can be obtained by solving the Riccati equation of the linear quadratic regulator (LQR).

[0101] The calculation method is as follows:

[0102]

[0103] In the formula These are the constraint sets for different configurations of the aircraft state and control surface offsets after compression; Θ m U m These are the sets of aircraft states and control surface bias constraints under the dynamic system codes of different aircraft configurations.

[0104] (4) Design the optimal control objective function for different aircraft configurations.

[0105] Unlike the single objective function in traditional model predictive controller design, the objective function optimized in step (4) can be designed according to different aircraft configurations, including parameters such as: prediction time domain N. m Positive definite weight matrix Q m R m and P m Terminal constraints m Predicting the time domain N m Weight matrix Q m R m It can be designed according to the predictive control performance requirements of the aircraft, P m It can be obtained by solving the LQR Riccati equation.

[0106] The designed optimization control objective function is:

[0107]

[0108] In the formula Δq k+i|k =[Δα k+i|k Δω k+i|k ] T ,Δδ k+i|k These represent the predicted state of the variable-structure aircraft and the control surface offset at time k+i, respectively, with N... m Q represents the length of the prediction time domain under different configurations. m With R m These are the weight matrices of the aircraft state variables and the positive definite matrices of the control surface bias weights, respectively, for different configurations.

[0109] Let P be the terminal cost function, where P is the terminal cost function. m This is the positive definite matrix of terminal state weights.

[0110] (5) Based on the obtained constraint compaction set and objective function, establish the switching model predictive control problem for each configuration of the aircraft for the nominal system of the variable configuration aircraft.

[0111] The switching model predictive control problem designed in step (5) targets the nominal dynamic system model of the aircraft dynamics model. The cumulative effect of the disturbance variables on the flight process of the aircraft dynamics model in step (1) has been eliminated by the design of the compact constraint set in step (3). Therefore, solving the optimization control problem (5) for the nominal dynamic system of the aircraft avoids the constraint violation problem caused by the cumulative error of the disturbance variables.

[0112] The switching model predictive control problem is as follows:

[0113]

[0114] In the formula For nominal aircraft configuration switching system, among which, This is the nominal control surface offset. For nominal flight conditions,

[0115] Predict the optimal control surface bias sequence at time k. Let Ξ be the initial state value. m This represents the set of terminal constraints for different configurations of the aircraft.

[0116] (6) Provide sufficient conditions to ensure that the configuration switching time and the feasibility of staying in a certain configuration are constrained by the configuration dependence and dwell time of the optimization control problem of (5).

[0117] In step (6), the sufficient condition for ensuring the optimal control problem (5) regarding the aircraft configuration switching time and the feasibility of staying in a certain configuration is the configuration-dependent dwell time constraint is:

[0118]

[0119] The right side of the formula is ∩ n∈Σ,n≠m F n Let τ be the intersection of the feasible regions of the initial values ​​for all configurations of the aircraft except for the m-configuration subsystem. This design ensures the solvability of optimization problem (5) after switching from configuration m to other configurations. m That is, the reachable set of the feasible region of configuration m can reach ∩. n∈Σ,n≠m F n The minimum number of steps. The initial feasible region for making the switching model predictive control problem sustainably solvable under the m-configuration is...

[0120]

[0121] (7) Provide sufficient conditions to guarantee the configuration switching time and stability of the aircraft in a certain configuration in the optimization control problem of (5).

[0122] In step (7), the sufficient condition for ensuring the stability of the aircraft configuration switching configuration in the optimal control problem (5) is the dwell time constraint:

[0123]

[0124] In the formula, the shrink terminal set E ∞ For Ε0=∩ m∈Σ Ξ m Obtained through calculation of the reverse reachable set.

[0125] The shrinking terminal set is calculated as follows:

[0126] Step (7a): Define the v-step backward reachable set of set Z as:

[0127] Step (7b): Calculate v = τ for each different configuration subsystem m ,τ m +1,…,2τ m -1,m∈Σ step backward reachable set intersection

[0128]

[0129] In the formula, λ∈(0,1) is the contraction factor, and Ε0=∩ m∈Σ Ξ m j is a positive integer representing the number of algorithm iterations;

[0130] In calculating the intersection of the reverse reachable sets, switching sequence units under all configuration-dependent dwell times are considered. Introducing a contraction factor λ∈(0,1) ensures that the spacecraft's closed-loop state trajectory enters E. ∞ The convergence after that. Due to The aircraft's status trajectory enters E ∞ Then, a state feedback control law will be adopted, therefore the calculation will be performed to bring the closed-loop system state into a state targeted by... The dwell time constraint τ of the shrinking terminal set m This ensures the convergence of the flight trajectory.

[0131] Step (7c): Calculate E j =Ε j-1 ∩Λ;

[0132] Step (7d): If E j =Ε j-1 If not satisfied, j = j + 1, repeat steps (7a) to (7b) until E. j =Ε j-1 Obtain the shrink terminal set E ∞ .

[0133] (8) Obtain the existence of the switching disturbance invariant set in (2), (6) and (7), the feasibility of the predictive control algorithm and the maximum lower bound constraint of the closed-loop system stability configuration dependent on the dwell time.

[0134] In addition to verifying whether the feasibility and stability structure-dependent residence time constraints in steps (6) and (7) are satisfied, step (8) should also verify whether the constraint on the existence of the switching disturbance invariant set in step (2) is satisfied.

[0135] The maximum lower bound constraint for configuration-dependent residence time is:

[0136] τ m ≥τ m ,τ m ≥τm ′and τ m ≥τ m ",m∈Σ

[0137] In the formula τ m This is a constraint on the maximum lower bound of configuration-dependent dwell time for different aircraft configurations.

[0138] (9) Based on the aircraft configuration at each sampling time, solve the model predictive control problem under the corresponding configuration in (5) to obtain the optimal control surface command at the current time. Repeat step (5) to obtain the optimal control surface command at the next time.

[0139] Obtain the optimal control surface command at the current moment Specifically

[0140]

[0141] In the formula The optimal control sequence for the control surface is obtained by solving the model predictive control problem under the corresponding configuration in step (5) at time k. The first element;

[0142] Step (9) is the same as the traditional model predictive control method, solving for the optimal control sequence of the control surface obtained in (5). The first element This is the predicted optimal control input for the modified aircraft at time k. By repeating the solution to (5), the optimal control surface command for the next time step can be obtained.

[0143] The contents not described in detail in this specification are common knowledge to those skilled in the art.

Claims

1. A control method for a disturbed variable-configuration aircraft based on switching model prediction, characterized in that... The steps are as follows: (1) Determine the linearized switching dynamics model of the disturbed aircraft under different configurations and determine the variable constraints under each configuration; the variable constraints include disturbance variable constraints, flight state constraints, and control surface saturation constraints. (2) Based on the disturbance variable constraints, construct the switching disturbance invariant set for all configurations of the aircraft to obtain the switching configuration-dependent residence time constraints of different configuration subsystems; the different configuration subsystems refer to the aircraft dynamics model under different configuration variables; (3) Based on the flight state constraints, control surface saturation constraints determined in step (1) and the switching disturbance invariant set determined in step (2), calculate the compressed set of flight state and control surface saturation constraints under different aircraft configurations. (4) Design the optimal control objective function for different aircraft configurations; (5) Based on the constrained compact set and the optimization control objective function, establish the switching model predictive control problem for each configuration of the aircraft in the nominal system of the variable configuration aircraft. The nominal system of the modified aircraft refers to a linearized switching dynamics model without disturbance; (6) For the switching model predictive control problem described in step (5), determine the feasibility conditions for configuration-dependent residence time in a certain configuration; (7) For the switching model predictive control problem described in step (5), determine the configuration-dependent residence time stability condition for staying in a certain configuration; (8) Based on the configuration-dependent residence time constraint determined in step (2), the configuration-dependent residence time feasibility condition determined in step (6), and the configuration-dependent residence time stability condition determined in step (7), determine the maximum lower bound constraint of configuration-dependent residence time. (9) Based on the maximum lower bound constraint, according to the aircraft configuration at each sampling time, solve the switching model predictive control problem under the corresponding configuration in step (5) to obtain the optimal control surface command at the current time; repeat step (5) to obtain the optimal control surface command at the next time.

2. The control method for a disturbed modified aircraft based on switching model prediction according to claim 1, characterized in that: The linearized switching dynamics model for different configurations of the disturbed aircraft mentioned in step (1) is as follows: The linearized state-space equations for the longitudinal aircraft's metamorphic switching are as follows: In the formula Δα k Let Δω be the angle of attack bias at time k. k Let Δδ be the pitch angular velocity deviator at time k. k Let be the control input for the control surface bias at time k; the variable configuration switching law σ(k) is a piecewise constant function of time k, Σ is the set of all possible switching configurations of the aircraft, Σ:={1,2,…,m,…,M}, where m represents the code of the dynamic system of the aircraft with different configurations; M represents the number of configurations that the aircraft can switch to, A σ(k) Represents the system matrix and B under different modal switching laws. σ(k) Represents the input matrix and d under different modal switching laws. k This represents the disturbance variable experienced by the aircraft; To simplify, let Δq k =[Δα k Δω k ] T Let k be the state of the spacecraft at time k; Determine the set of constraints for aircraft state, control surface bias, and disturbance variables under different configurations: Δq k ∈Θ σ(k) ,Dd k ∈U σ(k) ,d k ∈D In the formula Θ σ(k) U σ(k) Let be the constraint sets under different configurations of aircraft state and control surface bias, respectively, and D be the constraint set of disturbance variables.

3. The control method for a disturbed modified aircraft based on switching model prediction according to claim 2, characterized in that: In step (2), the switching disturbance invariant set is constructed for all configurations of the aircraft, specifically as follows: Step (2a): Let j = 1, calculate v = τ for each different configuration subsystem. m ,τ m +1,…,2τ m -1, m∈Σ steps reachable set In the formula, τ m This indicates the configuration switching dependence of different configuration aircraft dynamics system codes on dwell time. K m ,m∈Σ is the configuration-dependent feedback control gain, A m The system matrix and B represent the system matrix under the dynamic system designation of different configuration aircraft. m Let O0 represent the input matrix under the code name of the dynamic system of different configuration aircraft, where O0 = {0} and the zero-step reachable set is... j represents the number of algorithm iterations, and j is a positive integer, starting from j=1; Step (2b): Calculate the convex hull of the v-step reachable set for each subsystem with different configurations. Ω j =co{O j } co represents the convex hull calculation operation; Step (2c): If Ω j =Ω j-1 If not satisfied, let j = j + 1, that is, increment the value of j by 1, and repeat steps (2a) to (2b) until Ω. j =Ω j-1 Obtain the switching interference invariant set Ω ∞ ; The minimum configuration for the existence of the switching disturbance invariant set depends on the residence time τ. m "This refers to the obtained switching configuration-dependent residence time constraint." 4. The control method for a disturbed modified aircraft based on switching model prediction according to claim 3, characterized in that: The calculation method for flight state and control surface saturated constraint compact set under different aircraft configurations in step (3) is as follows: In the formula These are the constraint sets for different configurations of the aircraft state and control surface offsets after compression; Θ m U m These are the sets of aircraft states and control surface bias constraints under the dynamic system codes of different aircraft configurations.

5. The control method for a disturbed modified aircraft based on switching model prediction according to claim 1, characterized in that: The optimization control objective function designed in step (4) is as follows: In the formula Δq k+i|k =[Δα k+i|k Δω k+i|k ] T ,Δδ k+i|k These represent the predicted state of the variable-structure aircraft and the control surface offset at time k+i, respectively, with N... m Q represents the length of the prediction time domain under different configurations. m With R m These are the weight matrices of the aircraft state variables and the positive definite matrices of the control surface bias weights, respectively, for different configurations. Let P be the terminal cost function, where P is the terminal cost function. m This is the positive definite matrix of terminal state weights.

6. The control method for a disturbed modified aircraft based on switching model prediction according to claim 5, characterized in that: Step (5) establishes the switching model predictive control problem for each configuration of the aircraft under the nominal system of the variable-configuration aircraft as follows: In the formula For nominal aircraft configuration switching system, among which, This is the nominal control surface offset. For nominal flight conditions, Predict the optimal control surface bias sequence at time k. Let Ξ be the initial state value. m This represents the set of terminal constraints for different configurations of the aircraft.

7. The control method for a disturbed modified aircraft based on switching model prediction according to claim 6, characterized in that: The feasibility condition for configuration-dependent residence time in step (6) under a certain configuration is: In the formula τ m For the configuration-dependent dwell time of the aircraft to satisfy the feasibility conditions, the initial feasible region for the switching model predictive control problem to be continuously solvable under configuration m is:

8. The control method for a disturbed modified aircraft based on switching model prediction according to claim 7, characterized in that: The configuration-dependent residence time stability condition for a certain configuration in step (7) is: In the formula τ m ′ represents the minimum configuration-dependent dwell time for the aircraft to satisfy the feasibility conditions, E ∞ To shrink the terminal set, the terminal set is calculated as follows: Step (7a): Define the v-step backward reachable set of set Z as: Step (7b): Calculate v = τ for each different configuration subsystem m ,τ m +1,…,2τ m -1,m∈Σ step backward reachable set intersection In the formula, λ∈(0,1) is the contraction factor, and Ε0=∩ m∈Σ Ξ m j is a positive integer representing the number of algorithm iterations; Step (7c): Calculate E j =Ε j-1 ∩Λ; Step (7d): If E j =Ε j-1 If not satisfied, j = j + 1, repeat steps (7a) to (7b) until E. j =Ε j-1 Obtain the shrink terminal set E ∞ .

9. The control method for a disturbed modified aircraft based on switching model prediction according to claim 8, characterized in that: Step (8) determines the maximum lower bound constraint of configuration-dependent residence time as follows: τ m ≥τ m ,τ m ≥τ m ′ and τ m ≥τ m ″, m ∈ Σ In the formula τ m This is a constraint on the maximum lower bound of configuration-dependent dwell time for different aircraft configurations.

10. The control method for a disturbed variable-configuration aircraft based on switching model prediction according to claim 1, characterized in that: In step (9), based on the aircraft configuration at each sampling time, the model predictive control problem under the corresponding configuration in step (5) is solved to obtain the optimal control surface command at the current time. Specifically In the formula The optimal control sequence for the control surface is obtained by solving the model predictive control problem under the corresponding configuration in step (5) at time k. The first element; repeat step (5) to obtain the optimal control surface command for the next moment.

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