Stabilization control method for roll angle of unmanned aerial vehicle

Abstract

The invention discloses a stabilization control method for a roll angle of an unmanned aerial vehicle, and belongs to the field of stabilization control of an attitude angle of a four-rotor aircraft, and the method comprises the steps: constructing a three-order linearization dynamic model; processing the third-order linearization dynamic model by adopting a pole assignment technology to obtain a stable state matrix; constructing a Jordan standard form based on characteristic value components of the stable state matrix, obtaining a positive characteristic value matrix and a real number matrix, and deducing a high-dynamic output matrix; designing a high-dynamic roll angle stabilization control signal based on the positive eigenvalue matrix and the high-dynamic output matrix; a command rolling torque is inversely solved by using a high-dynamic rolling angle stabilization control signal; and inputting the command rolling torque into the coupling rolling torque output characteristic model to obtain an actual rolling torque value of the rolling channel of the unmanned aerial vehicle. According to the invention, the high dynamic convergence capability of the roll angle is improved, so that the unmanned aerial vehicle can more quickly and stably realize stabilization control of the roll angle in a complex environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of attitude angle stabilization control of quadrotor aircraft, and in particular relates to a stabilization control method for the roll angle of an unmanned aerial vehicle. Background Art

[0002] The widespread application of unmanned aerial vehicles (UAVs) in complex and dynamic scenarios, such as logistics inspection, disaster relief, and aerial mapping, has led to increasingly stringent requirements for their high-maneuverability and rapid attitude stabilization capabilities. Roll angle control, a key component of UAV attitude control, directly determines the vehicle's safety and mission effectiveness in highly dynamic tasks such as high-speed evasion and emergency steering. However, existing roll angle control methods face significant challenges. On the one hand, traditional designs based on simplified models (such as second-order systems) struggle to accurately characterize and compensate for the complex drive system coupling dynamics in the roll channel, resulting in response lag, overshoot, and even instability during high-dynamic maneuvers. On the other hand, existing stabilization controllers often rely on cumbersome parameter tuning to balance response speed and robustness, and are prone to introducing static errors, making it difficult to achieve zero-error tracking while ensuring high dynamic convergence. Therefore, a new roll angle stabilization control method that deeply analyzes the roll coupling dynamics and effectively combines high dynamic convergence with static error suppression is urgently needed to meet the stringent flight quality requirements of modern UAVs under extreme operating conditions. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention proposes a stabilization control method for the roll angle of an unmanned aerial vehicle to solve the problems existing in the above-mentioned prior art.

[0004] To achieve the above objectives, the present invention provides a stabilization control method for the roll angle of an unmanned aerial vehicle, comprising:

[0005] Construct a third-order linearized dynamic model based on the roll channel of the unmanned aerial vehicle;

[0006] The unstable state matrix in the third-order linearized dynamic model is processed by using a pole placement technique to obtain a stable state matrix, and a state-stable dynamic model of the roll channel is constructed;

[0007] Constructing a Jordan canonical form based on the eigenvalue components of the steady-state matrix to obtain a positive eigenvalue matrix and a real number matrix;

[0008] Based on the positive eigenvalue matrix, the real number matrix and the auxiliary matrix, a high dynamic output matrix is ​​derived through equation transformation;

[0009] designing a high-dynamic roll angle stabilization control signal based on the positive eigenvalue matrix and the high-dynamic output matrix;

[0010] Determining the commanded roll torque using the high-dynamic roll angle stabilization control signal;

[0011] The commanded roll torque is input into a coupled roll torque output characteristic model to obtain an actual roll torque value of the roll channel of the unmanned aerial vehicle at each sampling moment.

[0012] Optionally, the process of constructing a third-order linearized dynamic model based on the roll channel of the unmanned aerial vehicle includes:

[0013] Based on the coupled roll torque and command roll torque of the motor drive system, a coupled roll torque output characteristic model is established;

[0014] Construct the linear relationship between roll angle, roll angular velocity and roll torque;

[0015] By defining a roll channel state vector, an unstable state matrix, and a constant control vector, the coupled roll torque output characteristic model is combined with the linearized relationship to obtain a third-order linearized dynamic model based on the coupled roll torque.

[0016] Optionally, the expression of the coupled rolling torque output characteristic model is:

[0017] ;

[0018] Where, for The derivative of is the bandwidth of the motor drive system, is the moment of inertia of the rolling channel, for The reciprocal of The coupled rolling torque generated by the motor drive system of the unmanned aerial vehicle in the rolling channel is is the commanded rolling moment.

[0019] Alternatively, the expression of the third-order linearized dynamic model based on the coupled rolling torque is:

[0020] ;

[0021] Where, is the unstable state matrix, is the constant control vector, is the roll channel state vector, for The derivative of for The reciprocal of is the commanded rolling moment.

[0022] Optionally, the process of constructing a Jordan canonical form based on the eigenvalue components of the steady-state matrix to obtain a positive eigenvalue matrix and a real number matrix includes:

[0023] Classify the eigenvalues ​​of the steady-state matrix to obtain negative real roots and conjugate complex roots;

[0024] Constructing a positive eigenvalue matrix from the opposite numbers of the negative real roots;

[0025] Forming a complex Jordan block with the opposites of the conjugate complex roots;

[0026] Constructing a Jordan canonical form based on the positive eigenvalue matrix and the complex Jordan block;

[0027] The real number matrix is ​​obtained by solving the conversion relationship between the real number matrix and the Jordan canonical form.

[0028] Optionally, the process of deriving a high dynamic output matrix by equation transformation based on the positive eigenvalue matrix, the real number matrix and the auxiliary matrix includes:

[0029] Transposing the equation of the real number matrix and introducing an auxiliary matrix, and eliminating intermediate variables through matrix operations to obtain a simplified equation containing a positive eigenvalue matrix;

[0030] A high dynamic output matrix is ​​defined based on the simplified equation.

[0031] Optionally, the method further comprises constructing a minimum phase dynamic system for controller design based on the high dynamic output matrix and the state stable dynamic model of the roll channel;

[0032] The expression of the minimum phase dynamic system for controller design is:

[0033] ;

[0034] Where, for The derivative of is the output variable, is the positive eigenvalue matrix, T is the transpose, For high dynamic output matrix, is the constant control vector, This is the high dynamic roll angle stabilization control signal that needs further design.

[0035] Optionally, the designed expression of the high dynamic roll angle stabilization control signal is:

[0036] ;

[0037] Where, For the high dynamic roll angle stabilization control signal that needs further design, is the proportional stabilization term, is the integral stabilization term, T is the transpose, For high dynamic output matrix, For a certain moment, is a design parameter greater than zero, is the roll channel state vector, is a positive eigenvalue matrix, From the initial time to The points, is the constant control vector.

[0038] The present invention also provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a stabilization control method for the roll angle of an unmanned aerial vehicle is implemented.

[0039] The present invention also provides a storage medium storing a computer program, which, when executed by a processor, implements a stabilization control method for the roll angle of an unmanned aerial vehicle.

[0040] Compared with the prior art, the present invention has the following advantages and technical effects:

[0041] First, by constructing a third-order linearized dynamic model including the coupled roll torque, the complex dynamic characteristics of the roll channel are more accurately characterized. Second, the pole placement technique is used to process the unstable state matrix, effectively improving the stability of the system. Third, by constructing the Jordan canonical form and deriving the high-dynamic output matrix, the placement of conjugate complex roots is achieved, significantly improving the dynamic response speed of the roll angle control. Finally, a composite control signal containing proportional and integral terms is introduced to effectively suppress static errors while ensuring rapid convergence. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0043] Figure 1 This is an overall flow chart of a high-dynamic stabilization control method for the roll angle of an unmanned aerial vehicle according to an embodiment of the present invention;

[0044] Figure 2 2 is a comparison diagram of roll angle convergence characteristics of embodiments of the present invention. DETAILED DESCRIPTION

[0045] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0046] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0047] like Figure 1 As shown, this embodiment provides a stabilization control method for the roll angle of an unmanned aerial vehicle, comprising the following steps:

[0048] Step S1: constructing a third-order linearized dynamic model based on coupled rolling torque for the roll channel of the unmanned aerial vehicle.

[0049] Furthermore, the process of constructing the third-order linearized dynamic model includes:

[0050] First, the motor drive system on the unmanned aerial vehicle generates a coupled rolling torque in the rolling channel. and the commanded roll moment The following dynamic equations are satisfied (coupled rolling torque output characteristic model):

[0051] (1)

[0052] in, for The derivative of is the bandwidth of the motor drive system, is the moment of inertia of the rolling channel, for The reciprocal of .

[0053] Next, the linearized dynamic model of the UAV roll channel dynamics near the equilibrium point can be expressed as:

[0054] (2)

[0055] in, is the roll angle, for The derivative of is the roll angular velocity, for The derivative of is the actual rolling moment. In order to embed the rolling moment output characteristic model into the linearized dynamic model, it is necessary to define the rolling channel state vector , unstable state matrix and constant control vector , the specific expression is as shown in formula (3).

[0056] (3)

[0057] So, in Under the conditions established, the third-order linearized dynamic model based on the coupled rolling torque can be obtained:

[0058] (4)

[0059] Step S2: using pole placement technology to process the unstable state matrix existing in the third-order linearized dynamic model based on the coupled rolling torque, thereby constructing a state-stable dynamic model for the rolling channel.

[0060] Furthermore, the process of constructing a state-stable dynamic model for the rolling channel includes:

[0061] Unstable state matrix The obvious sign of is that its eigenvalue contains zero or positive numbers. The controller is in an unstable state matrix If the roll angle trajectory is directly controlled when there is a problem, it will be difficult to achieve the desired convergence characteristics, and there may even be a risk of divergence of the roll angle. Therefore, it is necessary to use the pole placement technique to adjust the unstable state matrix. Do further processing to make it a stable state matrix The key steps of the pole placement technique are as follows:

[0062] (1) is the unstable state matrix Choose the appropriate eigenvalue (negative root or conjugate negative root);

[0063] (2) Use the pole configuration function provided by MATLAB to solve the gain matrix ;

[0064] (3) Using equation Solve for the steady-state matrix .

[0065] Next, it is only necessary to introduce the data term into the third-order linearized dynamic model based on the coupled rolling moment The state-stable dynamic model facing the rolling channel can be obtained:

[0066] (5)

[0067] Step S3: constructing the Jordan canonical form based on the eigenvalue components of the steady-state matrix, thereby obtaining a positive eigenvalue matrix and a real number matrix. The specific implementation process includes:

[0068] The present invention provides a general method for solving external eigenvalue matrix and real number matrix, which is not affected by the stable state matrix. Dimension limit. Next, derive the state matrix of dimension n The corresponding external eigenvalue matrix and real matrix solution method.

[0069] In order to construct the Jordan standard form, we first need to calculate the steady-state matrix (symbol The eigenvalue components in a matrix with dimension n are classified into two categories: negative real roots and conjugate complex roots. The Jordan block composed of the opposite numbers of negative real roots is represented by (symbol Indicates that the dimension of a matrix is ​​m), that is, the positive eigenvalue matrix; the Jordan block composed of the opposite numbers of the conjugate complex roots is represented by (symbol These two types of Jordan blocks form a stable state matrix Jordan Standard :

[0070] (6)

[0071] in, m rows A matrix whose columns are all zero, for The matrix with m rows and columns containing all zero elements is used to ensure that the Jordan standard form is a square matrix. The properties of , we can obtain the following equation for real matrices:

[0072] (7)

[0073] in, is a real matrix.

[0074] Step S4: Using the auxiliary matrix, the real matrix, and some equation transformations, a high-dynamic output matrix is ​​derived, thereby constructing a minimum-phase dynamic system for controller design. The specific implementation process includes:

[0075] First, multiply the left side of equation (7) for a real matrix by You can get:

[0076] (8)

[0077] Then, after transposing equation (8), we can obtain the following result:

[0078] (9)

[0079] Then define two auxiliary matrices and (symbol Represents a matrix composed of OK elements):

[0080] (10)

[0081] in, is the identity matrix with m rows and m columns. Multiply both sides of equation (9) by the matrix Then we can get the following equation:

[0082] (11)

[0083] Since the equation Therefore, equation (11) can be further expressed as:

[0084] (12)

[0085] because , so equation (12) can be further rewritten as:

[0086] (13)

[0087] in, It can be further simplified into the following form:

[0088] (14)

[0089] Substituting equation (14) into (13) yields:

[0090] (15)

[0091] Observing equation (15), we can find that the positive eigenvalue matrix has appeared , which is the decisive factor affecting the high dynamic output matrix. Therefore, the high dynamic output matrix can be defined as , equation (15) becomes the following form:

[0092] (16)

[0093] With high dynamic output matrix , you can define the following output variables :

[0094] (17)

[0095] For output variables Taking the derivative, we can get the following result:

[0096] (18)

[0097] in, for Finally, by substituting the state-stable dynamic model (5) and equation (16) for the rolling channel into (18), the minimum phase dynamic system for controller design can be constructed:

[0098] (19)

[0099] in This is the high dynamic roll angle stabilization control signal that needs further design.

[0100] Step S5: Introduce two key parameters, the positive eigenvalue matrix and the high dynamic output matrix, to further design the high dynamic roll angle stabilization control signal in the minimum phase dynamic system for controller design. The specific implementation process includes:

[0101] The entire high dynamic roll angle stabilization control signal consists of two parts: proportional stabilization term and integral stabilization term. The proportional stabilization term should include high dynamic output matrix parameters, which can improve the roll channel state vector The convergence rate of The roll angle is included ,therefore Will follow The present invention uses the proportional stabilization term Designed as follows:

[0102] (20)

[0103] in is a design parameter greater than zero. However, driven by the proportional stabilization term with high dynamic characteristics, the roll angle will experience a large static error as it approaches the equilibrium point. Therefore, to reduce the impact of the static error on the convergence performance of the roll angle, the following integral stabilization term is further introduced:

[0104] (twenty one)

[0105] in, For a certain moment, symbol From the initial time to Therefore, the high dynamic roll angle stabilization control signal is designed as:

[0106] (twenty two)

[0107] The design parameters The smaller the setting, the larger the output value of the integral stabilization term, and the stronger the ability to suppress static errors. However, a larger integral stabilization term will slow down the convergence speed of the roll angle, so the design parameters While satisfying the given static error index, a larger value should be selected as much as possible to achieve high dynamic convergence of the roll angle.

[0108] Step S6: derive a specific expression of the commanded roll torque by using the high-dynamic roll angle stabilization control signal.

[0109] From the minimum phase dynamic system designed for controller, it can be found that the high dynamic roll angle stabilization control signal and commanded roll moment , gain matrix The following equality relationship is satisfied:

[0110] (twenty three)

[0111] The moment of inertia of the rolling channel It is an inherent parameter of the unmanned aerial vehicle, and the moment of inertia measurement device can measure its accurate value. is a gain matrix that needs to be set manually, so it is also known. In addition, the roll channel state variable It can be accurately fed back or estimated by sensors and is therefore also known. Then, combined with the expression (22) of the high-dynamic roll angle stabilization control signal, the following command roll torque can be inversely solved:

[0112] (twenty four)

[0113] Step S7: Input the specific expression of the commanded roll torque into the coupled roll torque output characteristic model to obtain the specific value of the actual roll torque that should be dynamically provided to the roll channel of the unmanned aerial vehicle at each sampling moment. The specific implementation process includes:

[0114] The equation Substituting the coupled rolling moment output characteristic model (1) into the equation, we can obtain:

[0115] (25)

[0116] Therefore, it is only necessary to substitute the designed command roll torque (24) into (25) to obtain the specific value of the actual roll torque that should be dynamically provided to the roll channel of the unmanned aerial vehicle at each sampling moment.

[0117] As another specific embodiment of this embodiment, a third-order linearized dynamic model based on coupled rolling torque is constructed for the roll channel of an unmanned aerial vehicle. First, a motor drive system of a type of unmanned aerial vehicle generates a coupled rolling torque in the roll channel. and the commanded roll moment The dynamic equation (coupled rolling torque output characteristic model) is satisfied between them. Among them, the bandwidth of the motor drive system is , the moment of inertia of the roll channel .

[0118] Next, in Under the condition that , the third-order linearized dynamic model based on the coupled rolling torque can be obtained, as shown in formula (4).

[0119] The roll channel state vector , unstable state matrix and constant control vector They are:

[0120] (26)

[0121] As another specific embodiment of this embodiment, the pole placement technology is used to process the unstable state matrix existing in the third-order linearized dynamic model based on the coupled rolling torque, thereby constructing a state-stable dynamic model for the rolling channel.

[0122] This embodiment is an unstable state matrix The three poles of the configuration are , , ,in is a negative real number, and is a pair of conjugate complex roots. The output matrix parameters derived from the conjugate complex roots help improve the high dynamic convergence characteristics of the roll angle. Then, the gain matrix is ​​solved using the pole placement function provided by MATLAB. . The known constant control vector and the gain matrix Substituting into the equation , the stable state matrix can be solved:

[0123] (27)

[0124] in, The specific value of can determine its dimension Next, we only need to introduce the data term into the third-order linearized dynamic model based on the coupled rolling moment The state-stable dynamic model facing the rolling channel can be obtained, as shown in formula (5).

[0125] As another specific embodiment of this embodiment, a Jordan canonical form is constructed according to the eigenvalue components of the steady-state matrix, thereby obtaining an external eigenvalue matrix and a real number matrix.

[0126] Eigenvalue The opposite of is 28, so the positive eigenvalue matrix , and the corresponding dimension variables In addition, the conjugate complex roots and The opposite numbers of 、 Therefore, the Jordan block formed by this set of conjugate complex roots Expressed as follows:

[0127] (28)

[0128] The above two types of Jordan block groups and The steady-state matrix Jordan Standard :

[0129] (29)

[0130] Will and Substitute the specific value of into the equation , you can solve it to get the real matrix:

[0131] (30)

[0132] Then define two auxiliary matrices and :

[0133] (31)

[0134] So combined with the auxiliary matrix and real matrices The specific value of , derive the high dynamic output matrix .

[0135] With high dynamic output matrix , you can define output variables , as shown in formula (17).

[0136] For output variables Taking the derivative, we can get formula (18).

[0137] in, for Finally, by substituting the state-stable dynamic model (5) and equation (16) for the rolling channel into (18), the minimum phase dynamic system for controller design can be constructed, as shown in equation (19).

[0138] in, This is the high dynamic roll angle stabilization control signal that needs further design.

[0139] As another specific embodiment of this embodiment, two key parameters, a positive eigenvalue matrix and a high dynamic output matrix, are introduced to further design the high dynamic roll angle stabilization control signal in the minimum phase dynamic system designed for the controller.

[0140] The high dynamic roll angle stabilization control signal is designed as shown in formula (22). Among them, the design parameters are , For a certain moment, symbol From the initial time to 's points.

[0141] As another specific embodiment of this embodiment, the high dynamic roll angle stabilization control signal is used to inversely solve the specific expression of the command roll torque. The command roll moment can be obtained by inversely solving the equation (24). As another specific embodiment of this embodiment, the actual roll moment at each sampling moment is calculated. Substituting the coupled roll moment output characteristic model into equation (25) yields the following equation. Therefore, it is only necessary to convert the designed command roll moment into By substituting it, we can obtain the actual torque that should be provided to the roll channel of the unmanned aerial vehicle at each sampling moment.

[0142] In this embodiment, comparative experiments are conducted to illustrate that the proposed high-dynamic roll angle control method has faster convergence characteristics. For example, a proportional-integral controller (comparison controller) that can only configure negative real roots is also used to control the change trajectory of the roll angle. The desired poles configured are: 、 and The relevant experimental results are as follows. Figure 2 First, define a consistent boundary To measure the dynamic characteristics of the controller. Figure 2It can be found that under the control of the comparative controller, the roll angle of the UAV takes 6.01 seconds to converge to the consistent boundary. However, under the control method proposed by the present invention, the roll angle can converge to the consistent boundary in just 3.90 seconds. Therefore, the control method of the present invention improves the dynamic characteristics of the comparative controller by about 1. times.

[0143] The present invention derives a high-dynamic output matrix by using an auxiliary matrix, a real matrix, and a series of equation transformations. This new construction method can configure conjugate complex roots for the state matrix. Compared with traditional methods, it greatly improves the high-dynamic convergence capability of the roll angle, enabling unmanned aerial vehicles to achieve faster and more stable roll angle stabilization control in complex environments.

[0144] This invention introduces an integral stabilization term with only one master control parameter. This not only significantly reduces the control parameter adjustment cycle but also improves the control system debugging efficiency. Furthermore, static error has always been a significant factor affecting controller convergence. The addition of the integral stabilization term effectively suppresses the adverse effects of static error on controller convergence, making the controller more stable and reliable during operation, better able to cope with various interferences and uncertainties, and thus improving the overall performance of the unmanned aerial vehicle's roll angle stabilization control.

[0145] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A stabilization control method for the roll angle of an unmanned aerial vehicle, characterized in that: The following steps are involved: Construct a third-order linearized dynamic model based on the roll channel of the unmanned aerial vehicle; The unstable state matrix in the third-order linearized dynamic model is processed by using a pole placement technique to obtain a stable state matrix, and a state-stable dynamic model of the roll channel is constructed; Constructing a Jordan canonical form based on the eigenvalue components of the steady-state matrix to obtain a positive eigenvalue matrix and a real number matrix; Based on the positive eigenvalue matrix, the real number matrix and the auxiliary matrix, a high dynamic output matrix is ​​derived through equation transformation; designing a high-dynamic roll angle stabilization control signal based on the positive eigenvalue matrix and the high-dynamic output matrix; Determining the commanded roll torque using the high-dynamic roll angle stabilization control signal; The commanded roll torque is input into a coupled roll torque output characteristic model to obtain an actual roll torque value of the roll channel of the unmanned aerial vehicle at each sampling moment.

2. The stabilization control method for the roll angle of an unmanned aerial vehicle according to claim 1, characterized in that: The process of constructing a third-order linearized dynamic model based on the roll channel of the unmanned aerial vehicle includes: Based on the coupled roll torque and command roll torque of the motor drive system, a coupled roll torque output characteristic model is established; Construct the linear relationship between roll angle, roll angular velocity and roll torque; By defining a roll channel state vector, an unstable state matrix, and a constant control vector, the coupled roll torque output characteristic model is combined with the linearized relationship to obtain a third-order linearized dynamic model based on the coupled roll torque.

3. The stabilization control method for the roll angle of an unmanned aerial vehicle according to claim 2, characterized in that: The expression of the coupled rolling torque output characteristic model is: ； Where, for The derivative of is the bandwidth of the motor drive system, is the moment of inertia of the rolling channel, for The reciprocal of The coupled rolling torque generated by the motor drive system of the unmanned aerial vehicle in the rolling channel is is the commanded rolling moment.

4. The stabilization control method for the roll angle of an unmanned aerial vehicle according to claim 3, characterized in that: The expression of the third-order linearized dynamic model based on the coupled rolling torque is: ； Where, is the unstable state matrix, is the constant control vector, is the roll channel state vector, for The derivative of for The reciprocal of is the commanded rolling moment.

5. The stabilization control method for the roll angle of an unmanned aerial vehicle according to claim 4, characterized in that: The process of constructing the Jordan canonical form based on the eigenvalue components of the steady-state matrix and obtaining the positive eigenvalue matrix and the real number matrix includes: Classify the eigenvalues ​​of the steady-state matrix to obtain negative real roots and conjugate complex roots; Constructing a positive eigenvalue matrix from the opposite numbers of the negative real roots; Forming a complex Jordan block with the opposites of the conjugate complex roots; Constructing a Jordan canonical form based on the positive eigenvalue matrix and the complex Jordan block; The real number matrix is ​​obtained by solving the conversion relationship between the real number matrix and the Jordan canonical form.

6. The stabilization control method for the roll angle of an unmanned aerial vehicle according to claim 5, characterized in that: Based on the positive eigenvalue matrix, the real number matrix and the auxiliary matrix, the process of deriving the high dynamic output matrix through equation transformation includes: Transposing the equation of the real number matrix and introducing an auxiliary matrix, and eliminating intermediate variables through matrix operations to obtain a simplified equation containing a positive eigenvalue matrix; A high dynamic output matrix is ​​defined based on the simplified equation.

7. The stabilization control method for the roll angle of an unmanned aerial vehicle according to claim 6, characterized in that: The method further includes constructing a minimum phase dynamic system for controller design based on the high dynamic output matrix and the state stable dynamic model of the roll channel; The expression of the minimum phase dynamic system for controller design is: ； Where, for The derivative of is the output variable, is the positive eigenvalue matrix, T is the transpose, For high dynamic output matrix, is the constant control vector, This is the high dynamic roll angle stabilization control signal that needs further design.

8. The stabilization control method for the roll angle of an unmanned aerial vehicle according to claim 7, characterized in that: The expression of the designed high dynamic roll angle stabilization control signal is: ； Where, For the high dynamic roll angle stabilization control signal that needs further design, is the proportional stabilization term, is the integral stabilization term, T is the transpose, For high dynamic output matrix, For a certain moment, is a design parameter greater than zero, is the roll channel state vector, is a positive eigenvalue matrix, From the initial time to The points, is the constant control vector.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the stabilization control method for the roll angle of the unmanned aerial vehicle according to claim 1 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the stabilization control method for the roll angle of an unmanned aerial vehicle as claimed in claim 1 is implemented.

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