Self-adaption-based autorotorcraft control method

Abstract

According to the self-adaption-based autorotorcraft control method provided by the invention, modeling and control of an autorotorcraft are deeply studied, and a self-adaption rapid tracking controller is designed for a rotation speed ring and a quasi-airframe attitude angle ring according to flight characteristics of the autorotorcraft; according to the method, under the condition that inertial parameters are unknown, parameter identification and rapid instruction tracking can be carried out on the autorotorcraft within finite time, the defect that in the prior art, instruction tracking needs to highly depend on an accurate nonlinear model of the aircraft is overcome, and good control performance is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of aircraft control, and in particular relates to an adaptive autogyro aircraft control method. Background Art

[0002] An autogyro is a low-speed, small rotorcraft that has the flight characteristics of vertical take-off and landing and fixed-point hovering. Unlike traditional rotorcraft, the basic components of this aircraft are composed of a rotating wing that constitutes a lifting surface and an electric propeller installed on the top of the wing. The mechanism is that the thrust generated by the propeller drives the wing to rotate freely around its main axis, and then the rotating wing generates the lift required for flight. Due to its simple mechanics, low fuselage resistance, high flight stability, and high payload, this aircraft has received widespread attention.

[0003] Like traditional rotorcraft, autogyro is also an underactuated system. The dynamic modeling of its horizontal, vertical and rotational motion is a very complicated matter. In addition, considering that some parameters of the aircraft model are difficult to measure in actual situations, it is very challenging to identify the parameters of the unknown model to accurately model the dynamic equations and design a controller to control its rotation speed and attitude angle.

[0004] The article "Spincopter wing design and flight control" by Matko Orsag et al. proposes the basic configuration of an autogyro, i.e. the basic components of this type of autogyro aircraft are a rotating wing that forms the lifting surface and an electric propeller mounted on the top of the wing. The mechanism is that the thrust generated by the propeller drives the wing to rotate freely around its main axis, and then the rotating wing generates the lift required for flight. In this article, the authors establish a simplified dynamic model of the aircraft to reveal its inherent stability and design a control system for it. Finally, based on a large number of simulations of the rotorcraft using the X-Plane software package, the design recommendations for the rotating wing are elaborated in detail.

[0005] However, the article by Matko Orsag et al. only considers vertical and horizontal motion dynamics, while ignoring the more important part of the aircraft attitude, namely rotational dynamics. This simplified dynamic model is difficult to accurately describe the flight state of the actual aircraft, and it is also unable to solve the problem that some parameters in actual situations are unmeasurable, which has obvious technical shortcomings.

[0006] In the article "PID based sliding mode control of asynchronous multi-actuator monocopter" by Hitesh Bhardwaj et al., a dynamic and kinematic model was established for a class of single-rotor aircraft. The flight response of a single-rotor aircraft with dual motors + flaps was studied based on the changes in the control signals assigned to the motors. Numerical simulations of step response, waypoint tracking, and trajectory tracking were performed considering different application scenarios. A PID-based sliding mode control method was proposed to achieve attitude control of asynchronous multi-actuator aircraft. Numerical simulations showed that the controller was able to control the single-rotor aircraft in the presence of noise and had good control performance.

[0007] However, this technology is a controller design implemented by a PID-based sliding mode control method, which is highly dependent on the accurate nonlinear model of the aircraft, that is, the control method needs to measure various parameters of the model. However, in actual situations, the parameters of the model are usually difficult to obtain accurately, and there is always uncertainty in the system. When some model parameters are unknown, the control method cannot guarantee the effective tracking of instructions, and the control effect is very poor. Therefore, parameter identification has become a key technology that needs to be solved urgently. Summary of the invention

[0008] In order to solve the above problems, the present invention provides a control method for a gyroplane based on adaptation, which realizes command tracking and parameter identification of the gyroplane within a limited time when the inertial parameters are unknown.

[0009] A method for controlling an autogyro aircraft based on adaptation, using a set rotation speed adaptive controller to control the rotation speed of the autogyro aircraft;

[0010] Wherein, the rotation speed adaptive controller is:

[0011]

[0012] Among them, τ y To control the quasi-body coordinate system The y-axis on the y The control torque, are the set rotation speed control gains, and The body coordinate system Relative to the inertial coordinate system The three axes can measure the rotation speed, is the tracking error of the rotation speed, and ω y The body coordinate system Relative to the inertial coordinate system The y-axis measures the rotation speed, For the given rotation speed command The derivative of represents the auxiliary variable related to the positive symmetric inertia tensor I of the autogyro The estimated value of I x To align the body coordinate system of the autogyro The angular momentum of the x-axis rotation, I y To align the body coordinate system of the autogyro The y-axis rotation moment, I z To align the body coordinate system of the autogyro The angular momentum of the z-axis rotation, I xz To align the body coordinate system of the autogyro The angular momentum generated about the z-axis when the x-axis rotates.

[0013] Furthermore, a set attitude angle controller is also used to control the pitch angle and yaw angle of the autogyro aircraft;

[0014] The attitude angle controller is:

[0015]

[0016] Among them, the control torque τ x To control the quasi-body coordinate system The x-axis on the y The control torque, τ z To control the quasi-body coordinate system The z-axis on the y The control torque, are all set attitude angle control gains, and represents the auxiliary variable related to the positive symmetric inertia tensor I of the autogyro The estimated value of ψ is the yaw angle of the autogyro, φ is the roll angle of the autogyro, and g is the relationship between the yaw angle ψ, the roll angle φ and the estimated value The auxiliary matrix is represents the auxiliary variable related to the positive symmetric inertia tensor I of the autogyro The estimated value of f is the regression matrix related to the yaw angle ψ and the roll angle φ, Φ 3The yaw angle ψ, roll angle φ and three-axis measurable rotation speed The auxiliary variable is related to the attitude angle tracking error.

[0017] Furthermore, the relationship between the yaw angle ψ, the pitch angle θ and the roll angle φ of the autogyro aircraft is as follows:

[0018]

[0019] in, is the first-order derivative of the roll angle φ, is the second-order derivative of the roll angle φ, ω x The body coordinate system Relative to the inertial coordinate system The x-axis measures the rotation speed, ω x The first derivative of z The body coordinate system Relative to the inertial coordinate system The z-axis can measure the rotation speed, ω z The first derivative of is the first-order derivative of ψ, is the second-order derivative of θ.

[0020] Furthermore, the calculation formula of the auxiliary variable r related to the attitude angle tracking error is as follows:

[0021]

[0022] Among them, v e is the tracking error of the attitude angle, and v e =vv d , v is the quasi-body coordinate system Relative to the inertial coordinate system Measurable attitude angle, v d is the given attitude angle instruction, v e The first derivative of v , β v , γ v are all setting coefficients, and α v >0,β v >0,0<γ v <1.

[0023] Furthermore, the calculation formula of the regression matrix f related to the yaw angle ψ and the roll angle φ is as follows:

[0024]

[0025] Among them, ve is the tracking error of the attitude angle, and v e =vv d , v is the quasi-body coordinate system Relative to the inertial coordinate system Measurable attitude angle, v d is the given attitude angle command, and φ d is the given roll angle command, is φ d The first derivative of is φ d The second derivative of d is the given pitch angle command, is θ d The first derivative of is θ d The second derivative of v , β v , γ v are all setting coefficients, and α v >0,β v >0,0<γ v <1, I represents the positive definite symmetric inertia tensor of the autogyro, the first auxiliary variable related to the yaw angle ψ The second auxiliary variable related to the yaw angle ψ

[0026] Furthermore, the yaw angle ψ, roll angle φ and three-axis rotation speed can be measured The relevant auxiliary variable Φ 3 The calculation formula is as follows:

[0027]

[0028] Among them, ω x The body coordinate system Relative to the inertial coordinate system The x-axis measures the rotational speed, ω z The body coordinate system Relative to the inertial coordinate system The z-axis measures the rotational speed.

[0029] Furthermore, the positive definite symmetric inertia tensor I of the autogyro aircraft is as follows:

[0030]

[0031] in,

[0032] Furthermore, the motors of the autogyro aircraft are installed at the ends of the two sets of rotors located on the same straight line, and the attitude kinematic equation of the autogyro aircraft is as follows:

[0033]

[0034] Where ψ is the quasi-body coordinate system of the autogyro The roll angle of the x-axis rotation, φ is the quasi-body coordinate system of the autogyro The yaw angle of the y-axis rotation, θ is the quasi-body coordinate system of the autogyro The pitch angle of the z-axis rotation, is the first-order derivative of ψ, is the first-order derivative of φ, is the first-order derivative of θ.

[0035] Furthermore, the estimated value The method to obtain is as follows:

[0036] The memory-enhanced adaptive law is constructed as follows:

[0037]

[0038] in, for The estimated value of for The first derivative of k γ is the setting coefficient, and 0 <k γ <1, tracking error vector Γ, Γ α , Γ β Both represent adaptive gain, and Γ>0, Γ α >0,Γ β >0, M is the memory feature matrix, For estimated value The memory-enhanced estimation error matrix is L is the memory enhancement coefficient matrix, and L = MW;

[0039] Solve the memory-enhanced adaptive law to obtain an estimated value

[0040] Furthermore, the calculation method of the memory feature matrix M is:

[0041]

[0042] Where t is the integration time, ν is the integration variable, Φ F (ν) is the regression diagonal matrix Φ in the frequency domain, and the regression diagonal matrix Φ=diag(Φ 1 ,[Φ2 ,Φ 3 ]); Φ 1 , Φ 2 All three axes can measure the rotation speed The relevant auxiliary variables, and

[0043]

[0044] Beneficial effects:

[0045] 1. The present invention provides a control method for a gyroplane based on adaptation, conducts in-depth research on the modeling and control of the gyroplane, and designs adaptive fast tracking controllers for the rotation speed loop and the quasi-body attitude angle loop according to the flight characteristics of the gyroplane, respectively. In the case of unknown inertial parameters, it can not only realize parameter identification and fast command tracking of the gyroplane within a limited time, but also overcome the defect in the prior art that command tracking needs to be highly dependent on the accurate nonlinear model of the aircraft, and has good control performance.

[0046] 2. The present invention provides an adaptive autogyro aircraft control method. When the aircraft model parameters are unmeasurable in actual scenarios, rotational dynamics are added on the basis of fully considering horizontal and vertical movements, thereby establishing a more accurate dynamic and kinematic model for the autogyro aircraft, making the description of the nonlinear model of the autogyro aircraft more accurate.

[0047] 3. The present invention provides an adaptive control method for a gyroplane, which introduces an adaptive control strategy based on memory enhancement to achieve control and unknown parameters. The simultaneous identification can achieve accurate estimation of unknown parameters while ensuring the stability of the closed-loop system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 The schematic diagram of the autorotating rotor structure and the definition of the coordinate system provided by the present invention;

[0049] Figure 2 The attitude angle response provided by the present invention;

[0050] Figure 3 The attitude angular velocity response provided by the present invention;

[0051] Figure 4 The unknown parameter W provided by the present invention 1 An estimated value of

[0052] Figure 5 The unknown parameter W provided by the present invention 2 An estimated value of

[0053] Figure 6 The unknown parameter W provided by the present invention 3 The estimated value of . DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0055] The present invention aims to consider the kinematic and dynamic modeling of the horizontal, vertical and rotational motions of the autogyro aircraft, and taking into account the problem that the parameters of the aircraft model are unmeasurable in actual situations, perform parameter identification to obtain an accurate dynamic model, and finally design an adaptive controller to achieve command tracking of the aircraft's rotation speed and attitude angle within a finite time, so as to achieve effective control of the aircraft model with unknown parameters.

[0056] Specifically, the specific implementation process of the solution of the present invention is as follows:

[0057] Step 1: Establish the dynamic equations of the autogyro

[0058] In adaptive control and parameter identification, the concept of finite excitation of the signal v(t) is crucial to ensure parameter convergence. The definition of finite excitation (FE) is as follows:

[0059] Definition 1: Consider a bounded signal v(t) and a finite time interval [T 0 ,T 0 +△T],△T>0, if there exists a positive constant α>0 such that

[0060]

[0061] Where I represents an identity matrix with appropriate dimension, then it means that the bounded signal v(t) satisfies the finite excitation condition.

[0062] The following lemma describes the Lyapunov representation of a nonlinear system that converges quickly in finite time.

[0063] Lemma 1: Consider a nonlinear system where x is the state vector and t is time. Assume that there exists a continuous positive definite functional V(x,t) such that

[0064]

[0065] Among them, α>0, β>0, and 0<γ<1, then the nonlinear system can converge to its equilibrium point in a finite time.

[0066]

[0067] like Figure 1 As shown, define the inertial coordinate system Body fixed coordinate system Quasi-body coordinate system

[0068] According to the definition of the above coordinate system, the attitude kinematic equation can be expressed as:

[0069]

[0070] in, The body coordinate system is defined as Relative to the inertial coordinate system The measurable angular velocity of the autogyroplane. ψ is the angular velocity of the autogyroplane around the quasi-body coordinate system. The yaw angle of the x-axis rotation, φ is the quasi-body coordinate system of the autogyro The roll angle of the y-axis rotation, θ is the quasi-body coordinate system of the autogyro The pitch angle of the z-axis rotation, is the first-order derivative of ψ, is the first-order derivative of φ, is the first-order derivative of θ.

[0071] Assuming that the moment of inertia of the autogyro is constant, the attitude dynamics equation can be written as:

[0072]

[0073] in, Represent the positive definite symmetric inertia tensor of the autogyro:

[0074]

[0075] The formula satisfies That is, I is a reversible matrix. x To align the body coordinate system of the autogyro The angular momentum of the x-axis rotation, I y To align the body coordinate system of the autogyro The y-axis rotation moment, I z To align the body coordinate system of the autogyro The angular momentum of the z-axis rotation, I xz To align the body coordinate system of the autogyro The angular momentum generated about the z-axis when the x-axis rotates. represents the control torque, T represents the transposition, G = -ω × I y ω y e yrepresents the gyroscopic torque, where Symbol ω × represents the skew-symmetric matrix of ω, specifically

[0076]

[0077] Compared to other VTOL aircraft such as quadrotors, autogyro aircraft continuously rotate around their main axis during flight. Therefore, it makes more sense to consider the attitude in the quasi-body coordinate system rather than the body coordinate system. In addition, the lift of the fuselage is related to the rotation speed but not the yaw angle, so the rotation speed is another important factor to consider. Therefore, the ultimate goal of this paper is to design an adaptive controller that can achieve command tracking and parameter identification in a finite time when the inertial parameters are unknown.

[0078] Step 2: Design the rotation speed controller

[0079] The autogyro aircraft rotates at a speed ω during flight. y around The shaft rotates continuously, ω y The kinetic equation of can be obtained according to (5), namely

[0080]

[0081] Given rotation speed command The tracking error of the rotational velocity can be defined as:

[0082]

[0083] Its time derivative can be expressed as:

[0084]

[0085] in, are the unknown coefficients related to the parameters of the inertia tensor.

[0086] In order to track the rotation speed instruction within a limited time We propose the following adaptive controller:

[0087]

[0088] in, It represents the constant control gain. express The estimated value of is the regression vector.

[0089] Note 1: Note that due to the control torque τ y Contained in the regression vector Φ 1Therefore, the adaptive control rate formula (11) cannot be implemented in practical applications. Add a term on both sides of formula (11) Then divide by Then we have

[0090]

[0091] This makes it possible to control the rotation speed.

[0092] Step 3: Design the attitude angle controller

[0093] Quasi-body coordinate system Relative to the inertial coordinate system The attitude of is determined only by the pitch angle θ and the yaw angle ψ, so the present invention designs a finite time tracking controller for φ and θ. From (4), the second derivatives of φ and θ can be formulated as:

[0094]

[0095] To obtain the attitude angle and control torque The mapping relationship between them, the present invention converts ω in (5) x and ω z The time derivative of is rewritten as:

[0096]

[0097] Substituting equation (4) and equation (14) into equation (13) and sorting out similar terms, we have:

[0098]

[0099] in, and

[0100] Given attitude angle command Then the tracking error of the attitude angle can be expressed as

[0101] v e =vv d (16)

[0102] Among them, v is the quasi-body coordinate system Relative to the inertial coordinate system Measurable attitude angle, v d is the given attitude angle instruction;

[0103] The auxiliary variable r related to the attitude angle tracking error is defined as follows

[0104]

[0105] Its time derivative can be written as

[0106]

[0107] In the formula, α v >0,β v >0, and 0<γ v <1.

[0108] Here is defined

[0109] Substituting equation (15) into equation (18), we can obtain

[0110]

[0111] In the formula, represents the unknown parameters related to the inertia tensor coefficient. Φ 2 , Φ 3 , f represents the regression matrix, that is

[0112]

[0113] Where f is the regression matrix related to the yaw angle ψ and the roll angle φ, Φ 2 and Φ 3 All are related to the yaw angle ψ, roll angle φ and three-axis measurable rotation speed Related auxiliary variables; v d is the given attitude angle command, and φ d is the given roll angle command, is φ d The first derivative of is φ d The second derivative of d is the given pitch angle command, is θ d The first derivative of is θ d The second derivative of x The body coordinate system Relative to the inertial coordinate system The x-axis measures the rotational speed, ω z The body coordinate system Relative to the inertial coordinate system The z-axis measures the rotational speed.

[0114] In order to track the attitude angle command v within a limited time d , the present invention will adopt the following adaptive controller:

[0115]

[0116] in, and Represent the unknown constant W 2 and W 3 The estimated value of .

[0117] Remark 2: Similar to the rotation speed controller (11), the torque control (20) cannot be implemented in practical applications. The controller in (20) can be organized as:

[0118]

[0119] In the formula,

[0120] Step 4: Design memory-enhanced adaptive law

[0121] In the rotation speed adaptive controller and attitude angle controller, the estimated value is an unknown quantity and an estimated value is required Then the estimated value Substitute it into the rotation speed adaptive controller and attitude angle controller to control the rotation speed, pitch angle and yaw angle of the autogyro aircraft.

[0122] Based on this, by substituting the rotation speed controller (11) and the attitude angle controller (20) into (10) and (18), the following closed-loop dynamic equations can be obtained:

[0123]

[0124] The tracking errors are superimposed together, that is, The closed-loop system dynamics equation (22) can be simplified as:

[0125]

[0126] In the formula, 0 <k γ <1.

[0127] Define the remaining term in equation (23): Then there is

[0128]

[0129] Assuming that the initial tracking error is zero, we can transform Equation (24) from the t domain to the s domain in the form of

[0130] Φ(s)W=(sI+k α )η(s)+ε(s) (25)

[0131] Consider a stable first-order low-pass filter of the following form:

[0132]

[0133] Where τ represents the time constant of the filter.

[0134] Multiplying the left and right sides of equation (25) by F(s) respectively, we can obtain the closed-loop system dynamic equation in the s domain after low-pass filtering:

[0135] Φ F (s)W=(sI+k α )η F (s)+ε F (s) (27)

[0136] Wherein, the subscript F represents the signal after filtering by F(s), that is, η F (s) = F(s)η(s). In addition, according to the following transformation:

[0137]

[0138] The closed-loop system dynamics equation (27) in the s domain can be further simplified as:

[0139]

[0140] Assume that ξ is The result of the inverse Laplace transform of , then the time derivative of ξ in the t domain is

[0141]

[0142] where ξ(0) = 0. Therefore, the inverse Laplace transform of equation (29) can be written as

[0143]

[0144] In the formula, Φ F is Φ F The representation of (s) in the t domain can be calculated by the following formula

[0145]

[0146] Where Φ F (0) = 0;

[0147] The integral of the filter regression matrix over time t is defined as the memory feature

[0148]

[0149] t is the integration time, ν is the integration variable, ΦF (ν) is the regression diagonal matrix Φ in the frequency domain, and the regression diagonal matrix Φ=diag(Φ 1 ,[Φ 2 ,Φ 3 ]);

[0150] Considering formula (31), the present invention can obtain the unknown coefficient of memory enhancement:

[0151]

[0152] Then, the memory-augmented estimation error can be defined as

[0153]

[0154] In order to identify the unknown coefficients in a limited time, the following memory-enhanced adaptive law is proposed:

[0155]

[0156] Where Γ>0, Γ α >0,Γ β >0 indicates adaptive gain.

[0157] The objective of the present invention is to design an adaptive controller for an autogyro aircraft to achieve command tracking and parameter identification in a finite time when the inertial parameters are unknown. In order to demonstrate the superiority of the proposed finite-time adaptive control and identification method (FTACI), the present invention compares it with traditional adaptive control (TAC), finite-time adaptive controller (FTAC) and finite-time adaptive concurrent learning controller (FTACC) to find alternative solutions. Figure 2-Figure 6 Numerical simulation results of various control methods are shown. Figure 2 It can be seen that the proposed control method (FTACI) and FTAC can achieve finite time tracking of attitude angle commands, while FTAC and TAC cannot. For the control of rotation speed, all control methods can achieve their command tracking, such as Figure 3 shown. Figure 4-Figure 6It is shown that the proposed FTACI can achieve finite-time identification of unknown coefficients, while the other three methods cannot. The simulation results show that through this control method, the estimated values ​​will converge to their nominal values ​​within a finite time.

[0158] In summary, the innovative finite time adaptive control and identification method (FTACI) proposed in the present invention realizes command tracking and parameter identification of the autogyro aircraft within a finite time when the inertial parameters are unknown, while other control strategies cannot achieve this goal.

[0159] Of course, the present invention may have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may certainly make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for controlling a gyroplane based on adaptation, characterized in that: A set rotation speed adaptive controller is used to control the rotation speed of the autogyro aircraft; Wherein, the rotation speed adaptive controller is: Among them, τ y To control the quasi-body coordinate system The y-axis on the y The control torque, are the set rotation speed control gains, and The body coordinate system Relative to the inertial coordinate system The three axes can measure the rotation speed, is the tracking error of the rotation speed, and ω y The body coordinate system Relative to the inertial coordinate system The y-axis measures the rotation speed, For the given rotation speed command The derivative of represents the auxiliary variable W1=[W 11 ,W 12 ,W 13 ] T The estimated value of I x To align the body coordinate system of the autogyro The angular momentum of the x-axis rotation, I y To align the body coordinate system of the autogyro The y-axis rotation moment, I z To align the body coordinate system of the autogyro The angular momentum of the z-axis rotation, I xz To align the body coordinate system of the autogyro The angular momentum generated about the z-axis when the x-axis rotates.

2. The adaptive autogyro aircraft control method according to claim 1, characterized in that: A set attitude angle controller is also used to control the pitch and yaw angles of the autogyro aircraft; The attitude angle controller is: Among them, the control torque u=[τ x ,τ z ] T , τ x To control the quasi-body coordinate system The x-axis on the y The control torque, τ z To control the quasi-body coordinate system The z-axis on the y The control torque, are all set attitude angle control gains, and represents the auxiliary variable W2 related to the positive definite symmetric inertia tensor I of the autogyro aircraft = [W 21 ,W 22 ,W 23 ] T The estimated value of ψ is the yaw angle of the autogyro, φ is the roll angle of the autogyro, and g is the relationship between the yaw angle ψ, the roll angle φ and the estimated value The auxiliary matrix is represents the auxiliary variable W3 related to the positive symmetric inertia tensor I of the autogyro aircraft = [W 31 ,W 32 ,W 33 ] T The estimated value of f is the regression matrix related to the yaw angle ψ and the roll angle φ, Φ3 is the regression matrix related to the yaw angle ψ, the roll angle φ and the three-axis measurable rotation speed ω = [ω x ,ω y ,ω z ] T The auxiliary variable is related to the attitude angle tracking error.

3. The adaptive autogyro aircraft control method according to claim 2, characterized in that: The relationship between the yaw angle ψ, pitch angle θ and roll angle φ of the autogyro is as follows: in, is the first-order derivative of the roll angle φ, is the second-order derivative of the roll angle φ, ω x The body coordinate system Relative to the inertial coordinate system The x-axis measures the rotation speed, ω x The first derivative of z The body coordinate system Relative to the inertial coordinate system The z-axis can measure the rotation speed, ω z The first derivative of is the first-order derivative of ψ, is the second-order derivative of θ.

4. The adaptive autogyro aircraft control method according to claim 2, characterized in that: The calculation formula of the auxiliary variable r related to the attitude angle tracking error is as follows: Among them, v e is the tracking error of the attitude angle, and v e =vv d , v is the quasi-body coordinate system Relative to the inertial coordinate system Measurable attitude angle, v d is the given attitude angle instruction, v e The first derivative of v , β v , γ v are all setting coefficients, and α v >0,β v >0,0<γ v <1.

5. The adaptive autogyro aircraft control method according to claim 2, characterized in that: The calculation formula of the regression matrix f related to the yaw angle ψ and the roll angle φ is as follows: Among them, v e is the tracking error of the attitude angle, and v e =vv d , v is the quasi-body coordinate system Relative to the inertial coordinate system Measurable attitude angle, v d is the given attitude angle command, and v d =[φ d ,θ d ] T ,φ d is the given roll angle command, is φ d The first derivative of is φ d The second derivative of θ d is the given pitch angle command, is θ d The first derivative of is θ d The second derivative of v , β v , γ v are all setting coefficients, and α v >0,β v >0,0<γ v <1, I represents the positive definite symmetric inertia tensor of the autogyro, the first auxiliary variable related to the yaw angle ψ The second auxiliary variable related to the yaw angle ψ 6. The adaptive autogyro aircraft control method according to claim 2, characterized in that: The yaw angle ψ, roll angle φ and three-axis measurable rotation speed ω=[ω x ,ω y ,ω z ] T The calculation formula of the relevant auxiliary variable Φ3 is as follows: Among them, ω x The body coordinate system Relative to the inertial coordinate system The x-axis measures the rotational speed, ω z The body coordinate system Relative to the inertial coordinate system The z-axis measures the rotational speed.

7. The adaptive autogyro aircraft control method according to any one of claims 1 to 6, characterized in that: The positive symmetric inertia tensor I of the autogyro aircraft is as follows: in, 8. The adaptive autogyro aircraft control method according to claim 1, characterized in that: The motors of the autogyro are installed at the ends of two sets of rotors located on the same straight line, and the attitude kinematic equation of the autogyro is as follows: Where ψ is the quasi-body coordinate system of the autogyro The roll angle of the x-axis rotation, φ is the quasi-body coordinate system of the autogyro The yaw angle of the y-axis rotation, θ is the quasi-body coordinate system of the autogyro The pitch angle of the z-axis rotation, is the first-order derivative of ψ, is the first-order derivative of φ, is the first-order derivative of θ.

9. The adaptive autogyro aircraft control method according to claim 2, characterized in that: Estimated value The method to obtain is as follows: The memory-enhanced adaptive law is constructed as follows: in, for The estimated value of for The first derivative of k γ is the setting coefficient, and 0 <k γ <1, tracking error vector Γ, Γ α , Γ β Both represent adaptive gain, and Γ>0, Γ α >0,Γ β >0, M is the memory feature matrix, For estimated value The memory-enhanced estimation error matrix is L is the memory enhancement coefficient matrix, and L = MW; Solve the memory-enhanced adaptive law to obtain an estimated value 10. The adaptive autogyro aircraft control method according to claim 9, characterized in that: The calculation method of the memory feature matrix M is: Where t is the integration time, ν is the integration variable, Φ F (ν) is the regression diagonal matrix Φ in the frequency domain, and the regression diagonal matrix Φ=diag(Φ1,[Φ2,Φ3]); Φ1 and Φ2 are both related to the three-axis measurable rotation speed ω=[ω x ,ω y ,ω z ] T The relevant auxiliary variables, and

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