A data-driven flight control method for variant aircraft

Abstract

This invention provides a data-driven flight control method for variator aircraft. This method requires no prior model knowledge; it only needs to collect the input, output, and state data of the variator aircraft over a period of time, and then use the collected data to design a feedback controller. The feedback controller enables stable control of the variator aircraft. Specifically, it mainly includes four parts: 1. Constructing a linear model of the variator aircraft under a certain operating condition, but this linear model is not involved in the controller design; it is only used to assist in simulation experiments. 2. Designing an open-loop system expression using discrete data. 3. Designing a generalized feedback controller based on the open-loop system expression. 4. Optimizing the generalized feedback controller using a linear quadratic programmer to obtain the optimal feedback gain. Therefore, compared with other common variator aircraft flight control methods, the method proposed in this invention can significantly reduce the time and financial costs of controller development.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) flight control, and specifically relates to a data-driven variant flight control method. Background Technology

[0002] Variants are a new type of aircraft with deformable structures that can adaptively change their shape according to changes in the flight environment and the requirements of flight missions. Compared with traditional fixed-shape aircraft, variants have significant advantages in several aspects: First, variants can improve aerodynamic characteristics, reduce flight energy consumption, and expand the flight envelope by utilizing deformable structures; second, variants can enhance control capabilities through active deformation-assisted maneuvering; and third, variants can adapt to various flight environments and missions by changing their configuration, thus expanding their application range.

[0003] From a control science perspective, changes in the shape of a morphing aircraft cause alterations in physical parameters such as center of gravity, moment of inertia, wing area, and span. Simultaneously, aerodynamic parameters, including aerodynamic forces and moments, undergo significant changes during deformation, leading to substantial alterations in the dynamic characteristics of the morphing aircraft. This renders traditional aircraft modeling and control methods inapplicable. In control system modeling, due to the strong coupling and nonlinearity inherent in morphing aircraft, the model must accurately represent the system's nonlinear characteristics and the changing physical and aerodynamic parameters. Regarding deformation decision-making and control, the increasing complexity of deformation capabilities and environmental adaptability makes it challenging for morphing aircraft to make reasonable deformation decisions and control in real-time based on changes in the flight environment. Finally, in flight attitude control, the increasing complexity of models and the growing deformation capabilities place higher demands on the design of flight controllers. Faced with these challenges, the design of morphing aircraft control systems has become one of the current research frontiers and hot topics. Summary of the Invention

[0004] Current variator flight control technologies are mostly based on variator modeling, which is a time-consuming and labor-intensive process, whether precise or coarse. To address these issues, this invention provides a data-driven variator flight control method. This method requires no prior model knowledge; it only needs to collect the variator's input, output, and state data over a period of time, and then use the collected data to design a feedback controller to achieve stable control of the variator.

[0005] Specifically, the data-driven variator flight control method proposed in this invention mainly consists of four steps: 1. Constructing a linear model of the variator under a certain operating condition. It should be noted that this linear model does not participate in the controller design but is only used to assist in simulation experiments. 2. Designing an open-loop system expression using discrete data. 3. Designing a generalized feedback controller based on the open-loop system expression. 4. Optimizing the generalized feedback controller using a linear quadratic programmer to obtain the optimal feedback gain.

[0006] The technical solution of this invention is as follows:

[0007] A data-driven longitudinal flight control method for a variant aircraft includes the following steps:

[0008] Step 1: Using the speed and altitude of the mutated aircraft as state variables, and throttle and elevator deflection as control variables, construct a discrete linear model of the mutated aircraft under the given operating conditions:

[0009] x(k+1)=Ax(k)+Bu(k)

[0010] In the formula, A is the system matrix; B is the input matrix; and the state vector x(k) = [VH] T V is the flight speed, H is the flight altitude; control variable u(k) = [Δδ e Δδ t ] T ,Δδ e and Δδ t These are respectively the rudder control command and the throttle control command;

[0011] For any variable, denoted as z, this invention defines three special symbolic arithmetic rules as follows:

[0012]

[0013]

[0014] Z i,N =[z(i) z(i+1) … z(i+N-1)]

[0015] In the formula, z is a variable name with no actual meaning, and can be matched according to the actual situation; k represents the kth sampling time; T represents the total number of sampling points; the subscripts i, t, and N are all positive integers;

[0016] Step 2: Design the open-loop system expression using discrete data:

[0017] Step 2.1: Sample the control input u of the variator aircraft, sampling data at T time points to obtain u(0), u(1), ... u(T-1), and define U.0,1,T for

[0018] U 0,1,T =[u(0) u(1) ... u(T-1)]

[0019] Data is sampled from the state vector x of the mutated aircraft for a total of T+1 time points, resulting in x(0), x(1), ... x(T), and X is defined. 0,T and X 1,T for

[0020] X 0,T =[x(0) x(1) … x(T-1)]

[0021] X 1,T =[x(1) x(2) … x(T)]

[0022] Step 2.2: Represent the open-loop system of the variator aircraft using discrete data.

[0023]

[0024] in This is the right inverse operation;

[0025] Step 3: Design a generalized feedback controller:

[0026] u(k)=Kx(k)

[0027] Where K is the feedback gain; based on the open-loop system expression obtained in step 2, a generalized feedback controller is added to obtain the variator closed-loop system as follows:

[0028] x(k+1)=X 1,T G K x(k)

[0029] Among them G K The relationship between K and U is: K = U 0,1,T G K ;

[0030] Step 4: Optimize the generalized feedback controller using a linear quadratic programmer to obtain the optimal feedback gain:

[0031] Using a linear quadratic programmer, the optimization problem for finding the optimal feedback gain K is established as follows:

[0032] min Q trace(X 0，T Q)+trace(X 0，T )

[0033]

[0034] In the formula, trace() calculates the trace of the matrix; by solving the optimization problem, the value of the intermediate parameter Q is obtained, and then according to K=U 0,1,T Q(X 0,T Q) -1 To obtain the optimal feedback gain K;

[0035] Step 5: Implement flight control of the variator aircraft using the optimal generalized feedback controller.

[0036] The present invention also proposes a computer-readable storage medium and a corresponding computer system.

[0037] A computer-readable storage medium storing a computer-executable program, which, when executed, is used to implement the above-described method.

[0038] A computer system includes: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described method.

[0039] Beneficial effects

[0040] This invention proposes a data-driven flight control method for variator aircraft. This method does not rely on any prior knowledge of the variator aircraft's mathematical model. It only requires collecting the variator's input, output, and state data over a period of time, and then using this collected data to design a feedback controller. This feedback controller enables stable control of the variator aircraft. Therefore, compared to other common variator aircraft flight control methods, the method proposed in this invention can significantly reduce controller development costs.

[0041] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0042] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0043] Figure 1 This is a flowchart of the method of the present invention.

[0044] Figure 2 This is a simulation experiment result diagram of the present invention. Detailed Implementation

[0045] The embodiments of the present invention are described in detail below. These embodiments are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0046] This embodiment proposes a data-driven flight control method for variator aircraft to meet the flight control requirements of variator aircraft. This method does not require any prior model knowledge. It only needs to collect the input data, output data and state data of the variator aircraft over a period of time, and then use the collected data to design a feedback controller. The feedback controller can achieve stable control of the variator aircraft.

[0047] Specifically, this embodiment mainly consists of four parts: 1. Constructing a linear model of the variator aircraft under a certain operating condition. It should be noted that this linear model does not participate in the controller design; it is only used to assist in the simulation experiment. 2. Designing an open-loop system expression using discrete data. 3. Designing a generalized feedback controller based on the open-loop system expression. 4. Optimizing the generalized feedback controller using a linear quadratic programmer to obtain the optimal feedback gain.

[0048] (1) Using the speed and altitude of the mutated aircraft as state variables and the throttle and elevator deflection as control variables, a discrete linear model of the mutated aircraft under the set operating conditions is constructed. It should be noted that the discrete linear model does not participate in the controller design, but is only used to assist the simulation experiment.

[0049] x(k+1)=Ax(k)+Bu(k)

[0050] In the formula, A is the system matrix; B is the input matrix; and the state vector x(k) = [VH] T V is the flight speed, H is the flight altitude; control variable u(k) = [Δδ e Δδ t ] T ,Δδ e and Δδ t These are respectively the rudder control command and the throttle control command;

[0051] Furthermore, for any variable, assuming it to be z, three special symbolic arithmetic rules are defined in this embodiment as follows:

[0052]

[0053]

[0054] Z i,N =[z(i) z(i+1) … z(i+N-1)]

[0055] In the formula, z is a variable name with no actual meaning, and can be matched according to the actual situation; k represents the kth sampling time; T represents the total number of sampling points; the subscripts i, t, and N are all positive integers.

[0056] (2) Design open-loop system expressions using discrete data:

[0057] Step 2.1: Sample the control input u of the variator aircraft, sampling data at T time points to obtain u(0), u(1), ... u(T-1), and define U. 0,1,T for

[0058] U 0,1,T =[u(0) u(1) ... u(T-1)]

[0059] Data is sampled from the state vector x of the mutated aircraft for a total of T+1 time points, resulting in x(0), x(1), ... x(T), and X is defined. 0,T and X 1,T for

[0060] X 0,T =[x(0) x(1) ... x(T-1)]

[0061] X 1,T =[x(1) x(2) ... x(T)]

[0062] Step 2.2: Let It can be seen that S is a column full rank, meaning that for any given v, the system of equations v = Sg must have a solution, and the solution can be expressed in the form of... in This is the right inverse operation. Therefore, we can obtain And because [BA]S = X 1,T , [BA]S = X 1,T Substitute In the middle, then substitute We can obtain:

[0063]

[0064] Therefore, the open-loop system of the variator aircraft is represented by discrete data as follows:

[0065]

[0066] in This is the right inverse operation;

[0067] Step 3: Design a generalized feedback controller:

[0068] u(k)=Kx(k)

[0069] Where K is the feedback gain; substituting the generalized feedback controller into the discrete linear model, we can obtain:

[0070]

[0071] In the formula, G K The relationship between K and U is: K = U 0,1,T G K Therefore, the closed-loop system of the variant aircraft, after adding a generalized feedback controller to the open-loop system expression, can be obtained as follows:

[0072] x(k+1)=X 1,T G K x(k)

[0073] Step 4: Optimize the generalized feedback controller using a linear quadratic programmer to obtain the optimal feedback gain:

[0074] Using a linear quadratic programmer, the optimization problem for finding the optimal feedback gain K is established as follows:

[0075] min Q trace(X 0,T Q)+trace(X 0,T )

[0076]

[0077] In the formula, trace() calculates the trace of the matrix; by solving the optimization problem, the value of the intermediate parameter Q is obtained, and then according to K=U 0,1,T Q(X 0,T Q) -1 The optimal feedback gain K is obtained.

[0078] Step 5: Implement flight control of the variator aircraft using the optimal generalized feedback controller.

[0079] This embodiment also proposes a computer-readable storage medium and a corresponding computer system based on the above method.

[0080] A computer-readable storage medium storing a computer-executable program, which, when executed, is used to implement the above-described method.

[0081] A computer system includes: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the above-described method.

[0082] To demonstrate the effectiveness of the method proposed in this embodiment, a simulation experiment was conducted. In the simulation, the discrete system of the aircraft under the conditions of V = Mach 1 and H = 10 km is assumed to be: x(k+1) = Ax(k) + Bu(k), where matrices A and B are in the form of:

[0083]

[0084] In the formula, matrix A represents the first four columns, and matrix B represents the last two columns. Assuming the number of sampling points T = 15, the optimal feedback gain K can be calculated as follows:

[0085]

[0086] At t=100s, a wind disturbance with a maximum amplitude of 10m / s is introduced into the system. Additionally, at t=200s, the mutated aircraft undergoes structural changes. Figure 2 The changes in the aircraft's speed and altitude are shown, demonstrating that the aircraft can still maintain stable flight even under conditions of strong winds and changes in its airframe structure, indicating that the data-driven method proposed in this invention is effective.

[0087] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention.

Claims

1. A data-driven longitudinal flight control method for a variant aircraft, characterized in that: Includes the following steps: Step 1: Design the open-loop system expression using discrete data: Step 1.1: Sample the control input u of the variator aircraft, sampling data at T time points to obtain u(0), u(1), ... u(T-1); where the control input u = [Δδ e Δδ t ] T ,Δδ e and Δδ t These are respectively the rudder control command and the throttle control command; Define U 0,1,T for U 0,1,T = [u(0) u(1) ... u(T-1)] Data is sampled from the state vector x of the mutated aircraft for a total of T+1 time points, resulting in x(0), x(1), ..., x(T); where the state vector x = [VH]. T V is the flight speed, and H is the flight altitude; And define X 0,T and X 1,T for X 0,T =[x(0) x(1) … x(T-1)] X 1,T =[x(1) x(2) … x(T)] Step 1.2: Represent the open-loop system of the variator aircraft using discrete data. in This is the right inverse operation; Step 2: Design a generalized feedback controller: u(k)=Kx(k) Where K is the feedback gain; based on the open-loop system expression obtained in step 1, a generalized feedback controller is added to obtain the variator closed-loop system as follows: x(k+1)=X 1,T G K x(k) Among them G K The relationship between K and U is: K = U 0,1,T G K ; Step 3: Optimize the generalized feedback controller using a linear quadratic programmer to obtain the optimal feedback gain: Using a linear quadratic programmer, the optimization problem for finding the optimal feedback gain K is established as follows: min Q trace(X 0,T Q)+trace(X 0,T ) In the formula, trace() calculates the trace of the matrix; by solving the optimization problem, the value of the intermediate parameter Q is obtained, and then according to K=U 0,1,T Q(X 0,T Q) -1 To obtain the optimal feedback gain K; Step 4: Implement flight control of the variator aircraft using the optimal generalized feedback controller.

2. A computer-readable storage medium storing a computer-executable program, characterized in that: The computer executable program, when executed, is used to implement the method of claim 1.

3. A computer system, comprising: One or more processors, a computer-readable storage medium, for storing one or more programs, characterized in that: when the one or more programs are executed by the one or more processors, the one or more processors implement the method of claim 1.

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