CN112527007A - Direct self-adaptive fuzzy logic control method for inhibiting wing rock caused by large attack angle - Google Patents

Direct self-adaptive fuzzy logic control method for inhibiting wing rock caused by large attack angle Download PDF

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CN112527007A
CN112527007A CN202011481888.7A CN202011481888A CN112527007A CN 112527007 A CN112527007 A CN 112527007A CN 202011481888 A CN202011481888 A CN 202011481888A CN 112527007 A CN112527007 A CN 112527007A
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fuzzy logic
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wing
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CN112527007B (en
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黄凯
赖冠宇
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Guangdong University of Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
    • G05D1/0825—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability using mathematical models
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Abstract

The invention provides a direct self-adaptive fuzzy logic control method for inhibiting wing rolling caused by a large attack angle, relates to the technical field of airplane wing rolling control, solves the problems that the existing control method for the wing rolling caused by the large attack angle has complex controller design, and the problems of difficult parameter solution and limited control precision of the controller are solved, firstly, a wing rock motion model is established, then the wing rock motion model is converted into a nonlinear system model, and a direct adaptive fuzzy logic system is established, the invention adopts direct adaptive fuzzy logic control, only one adaptive law exists, the calculated amount and the complexity of the controller design are reduced, and a direct self-adaptive fuzzy logic system is constructed on the basis of the analysis of the wing rock motion model, so that strict convergence certification and stability analysis can be performed, and the reliability of a closed-loop control system is greatly improved.

Description

Direct self-adaptive fuzzy logic control method for inhibiting wing rock caused by large attack angle
Technical Field
The invention relates to the technical field of airplane wing rock control, in particular to a direct self-adaptive fuzzy logic control system for inhibiting wing vibration caused by a large attack angle.
Background
The fighter plane is a military plane for eliminating enemy planes in the air, is a main machine type for military air combat, and occupies an irreplaceable position in both ground and air combat. The mission requirement of a high performance fighter is to operate normally at high angles of attack, and at high angles of attack the unsteady aerodynamic effects produce a wing roll phenomenon that manifests as extreme periodic oscillations in the roll. When the fighter plane enters a large attack angle area, the aerodynamic and flight characteristics are greatly changed, such as nonlinearity, asymmetry, cross coupling and the like of aerodynamic force, so that the stability and the maneuverability of the plane are sharply changed, a plurality of special flight phenomena occur, such as wing shaking, upward pitching, nose sideslip, over-stall rotation, deep stall, tail spin and the like, and the flight state is often dangerous and uncontrollable, such as separation as soon as possible, and unexpected serious consequences can be caused to pilots and fighters.
The traditional flight control method is that for example, most adaptive PID controllers based on a self-circulation wavelet neural network recognizer are designed based on a linear model, a local linear or global linear fitting method is adopted, the design of the controllers is complex, and the parameter of the controllers is difficult to solve, or as disclosed in China patent (publication No. CN111610794A) of 9.1.2020, a large attack angle dynamic inverse control method based on a sliding-film interference observer is adopted, aiming at the flight state of the large attack angle, a 'time scale separation' method is adopted, airplane state variables are decomposed into two groups of subsystems based on different time scales, the control laws are respectively solved by using the dynamic inverse method, and then the uncertainty of the dynamic inverse design method is compensated by combining a supercoiled sliding-mode interference observer, so that a stable controller of a fighter disturbed attitude system is designed. By reasonably selecting the parameters of the controller, the error can be stably bounded, the good tracking performance and stability of the flight control system under a large attack angle of the fighter are ensured, the dangerous states of deep stall, tail spin and the like are ensured to be changed in time, and the method has good reference significance for the practical application of engineering, but the control precision of the control method based on the interference observer is limited, so that the convergence and the stability of the closed-loop control system cannot be guaranteed by an exact theory.
Disclosure of Invention
In order to solve the problems that the controller is complex in design, the parameter solving of the controller is difficult and the control precision is limited in the existing control method aiming at the wing rolling caused by the large attack angle, the invention provides a direct self-adaptive fuzzy logic control method for inhibiting the wing rolling caused by the large attack angle, the complexity and the calculated amount of the controller design are reduced, the reliability of a control system is improved, and the stability of the wing within any given error is ensured.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
a direct adaptive fuzzy logic control method for inhibiting wing rock caused by a large attack angle at least comprises the following steps:
s1, establishing a wing rock motion model;
s2, defining a standard form of a nonlinear system model, and converting the wing rock motion model into the nonlinear system model according to the standard form of the nonlinear system model;
s3, establishing a direct self-adaptive fuzzy logic system, and definitely inhibiting a direct self-adaptive fuzzy logic control target of the wing rock caused by a large attack angle;
s4, constructing error variables of each order of the direct self-adaptive fuzzy logic system and generating a weight error of an unknown parameter in the self-adaptive process;
s5, constructing a Lyapunov function by utilizing error variables of each order of a direct self-adaptive fuzzy logic system and weight errors of unknown parameters generated in a self-adaptive process, and making the Lyapunov function semi-positive;
s6, establishing a self-adaptive law of self-adaptive parameters in an online updating self-adaptive process by semi-negatively determining the time derivative of the Lyapunov function, and designing a control signal and a virtual controller of a wing rock motion model to ensure the stability of the system.
Preferably, the expression of the wing rock motion model in step S1 is:
Figure BDA0002838389260000021
where φ (t) represents the roll angle, u (t) represents the control signal, α represents the steady state aircraft angle of attack,
Figure BDA0002838389260000022
representing an uncertain aerodynamic disturbance, KiI ═ 0, 1., 4 denotes a known constant, satisfying:
Figure BDA0002838389260000023
Figure BDA0002838389260000024
wherein q represents the free flow pressure, S represents the airfoil area, b represents the airfoil span, IxxShowing the rolling inertia moment, V showing the flying speed,
Figure BDA0002838389260000025
the moment coefficient is represented as a dimensionless coefficient, i is 0, 1.
Preferably, the standard form of the nonlinear system model defined in step S2 is:
Figure BDA0002838389260000031
Figure BDA0002838389260000032
y(t)＝x1(t)
wherein ,
Figure BDA0002838389260000033
is a measurable state variable of a set of nonlinear systems; f. ofi:Ri→R,i＝1,2,…,n；gn:Rn→ R represents the unknown in-system dynamics and unknown control gain, and y (t) epsilon R represents the output of the nonlinear system model; u (t) denotes a control signal.
Preferably, the process of converting the wing rock-and-roll motion model into the nonlinear system model satisfies the following steps:
order to
Figure BDA0002838389260000034
At the moment, the representation of a uniform variable t is omitted, and a mathematical model of the wing rock motion is converted into a nonlinear system model, wherein the expression is as follows:
Figure BDA0002838389260000035
Figure BDA0002838389260000036
wherein ,KiI ═ 0, 1., 4 denotes a known constant; α represents the steady state aircraft angle of attack; u (t) denotes a control signal.
Preferably, the direct adaptive fuzzy logic system of step S3 is:
P(χ(t))＝λ*Tψ(χ(t))
wherein, χ (t) ═ χ1(t),χ2(t),...,χM(t)]T∈RMRepresenting a blurred input vector;
Figure BDA0002838389260000037
a fuzzy weight vector representing an unknown parameter, # t (χ (t)) [ ψ [ #1(χ(t)),ψ2(χ(t)),…,ψN(χ(t))]T∈RNRepresents a known basis function vector, whose expression is:
Figure BDA0002838389260000038
wherein ,
Figure BDA0002838389260000039
taking a Gaussian function as a fuzzy membership function, namely:
Figure BDA00028383892600000310
l＝1,2,…,i＝1,2,…,M.
Figure BDA00028383892600000311
all represent constants.
Preferably, the control targets in step S3 are:
Figure BDA00028383892600000312
where y (t) is defined as phi (t), where phi (t) denotes the roll angle of the aircraft, and ym(t) represents a planned roll angle; delta1Indicating the error, i.e. the roll angle phi (t) of the aircraft and the planned roll angle y as the time approaches infinitym(t) the error value is within the allowable error range [ - δ [ -d ]1,δ1]The roll angle phi (t) of the airplane can gradually converge into any given error range, and the control precision of the roll angle is improved.
Preferably, the error variables of each order of constructing the direct adaptive fuzzy logic system in step S4 are represented as:
zi(t),i＝1,2,…,n；
z1(t)＝y(t)-ym(t)
Figure BDA0002838389260000041
wherein ,ym(t) denotes a given output, αi(t), i equals 11(t) aircraft roll angle y (t) and planned roll angle ym(t) error value, z2(t) is a virtual controller α1(t) and the state variable x2(t), planned roll angular acceleration
Figure BDA0002838389260000042
To an error value therebetween.
Preferably, in step S4, the expression of the weight error of the unknown parameter generated in the adaptive process is:
Figure BDA0002838389260000043
wherein ,
Figure BDA0002838389260000044
representing the weight error, theta, of the unknown parameter produced during the adaptation process*Represents an unknown parameter generated in the adaptation process, is a constant, theta (t) represents an adaptation parameter in the adaptation process,
Figure BDA0002838389260000045
Figure BDA0002838389260000046
wherein ,
Figure BDA0002838389260000047
dependent on the fuzzy logic weight vector; di(ξi(t)), i ═ 1,2 depends on the approximation error of the wing rock motion model for the direct adaptive fuzzy logic system.
Preferably, the lyapunov function constructed in step S5 is:
Figure BDA0002838389260000048
wherein V (t) represents Lyapunov function, which is half positive, and adaptive law is constructed in the next step to ensure its derivative
Figure BDA0002838389260000049
Semi-negative, and:
Figure BDA00028383892600000410
thereby enabling the aircraft roll angle output to track the planned roll angle output.
Preferably, the step S6 is performed by ensuring the time derivative of the Lyapunov function
Figure BDA00028383892600000411
Semi-negatively determining, and establishing an adaptive law expression of adaptive parameters in an online updating adaptive process as follows:
Figure BDA0002838389260000051
namely, carrying out real-time iterative updating through the derivative expression of the adaptive parameter theta (t); the control signal of the wing rock motion model and the expression of the virtual controller are designed to meet the following requirements:
u(t)＝α2(t)
Figure BDA0002838389260000052
Figure BDA0002838389260000053
wherein ：
Figure BDA0002838389260000054
Figure BDA0002838389260000055
Figure BDA0002838389260000056
Figure BDA0002838389260000057
wherein ,
Figure BDA0002838389260000058
represents a known basis function vector, as defined by ψ (χ (t));
Figure BDA0002838389260000059
and psil(χ (t)) is defined identically; n is a radical ofiRepresenting the number of pieces of user-defined fuzzy logic rules.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides a direct self-adaptive fuzzy logic control method for inhibiting wing rock caused by a large attack angle, which comprises the steps of firstly establishing a wing rock motion model, then converting the wing rock motion model into a nonlinear system model, and establishing a direct self-adaptive fuzzy logic system.
Drawings
FIG. 1 is a flow chart of a direct adaptive fuzzy logic control method for suppressing the rocking of the wing caused by a large attack angle according to an embodiment of the present invention;
fig. 2 shows a schematic view of an angle of attack α and a roll axis of an aircraft according to an embodiment of the invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for better illustration of the present embodiment, certain parts of the drawings may be omitted, enlarged or reduced, and do not represent actual dimensions;
it will be understood by those skilled in the art that certain well-known descriptions of the figures may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
the flow diagram of the direct adaptive fuzzy logic control method for suppressing the wing rock caused by the large attack angle shown in fig. 1 includes the following steps:
s1, establishing a wing rock motion model; the expression of the wing rock motion model is as follows:
Figure BDA0002838389260000061
where φ (t) represents the roll angle, u (t) represents the control signal, α represents the steady state aircraft angle of attack,
Figure BDA0002838389260000062
representing an uncertain aerodynamic disturbance, KiI ═ 0, 1., 4 denotes a known constant, satisfying:
Figure BDA0002838389260000063
Figure BDA0002838389260000064
wherein q represents the free flow pressure, S represents the airfoil area, b represents the airfoil span, IxxShowing the rolling inertia moment, V showing the flying speed,
Figure BDA0002838389260000067
the moment coefficient is represented as a dimensionless coefficient, i is 0, 1.
S2, defining a standard form of a nonlinear system model, and converting the wing rock motion model into the nonlinear system model according to the standard form of the nonlinear system model;
the standard form of the nonlinear system model defined in step S2 is:
Figure BDA0002838389260000065
Figure BDA0002838389260000066
y(t)＝x1(t)
wherein ,
Figure BDA0002838389260000071
is a measurable state variable of a set of nonlinear systems; f. ofi:Ri→R,i＝1,2,...,n；gn:Rn→ R represents the unknown in-system dynamics and unknown control gain, and y (t) epsilon R represents the output of the nonlinear system model; u (t) denotes a control signal.
The process of converting the wing rock motion model into the nonlinear system model meets the following requirements:
order to
Figure BDA0002838389260000072
At the moment, the representation of a uniform variable t is omitted, and a mathematical model of the wing rock motion is converted into a nonlinear system model, wherein the expression is as follows:
Figure BDA0002838389260000073
Figure BDA0002838389260000074
wherein ,KiI ═ 0,1, …,4 denotes a known constant; α represents the steady state aircraft angle of attack; u (t) denotes a control signal; specifically, a schematic diagram of the angle of attack α and the roll axis of the aircraft is shown in fig. 2.
S3, establishing a direct self-adaptive fuzzy logic system, and definitely inhibiting a direct self-adaptive fuzzy logic control target of the wing rock caused by a large attack angle; the direct adaptive fuzzy logic system of step S3 is:
P(χ(t))＝λ*Tψ(χ(t))
wherein, χ (t) ═ χ1(t),χ2(t),...,χM(t)]T∈RMRepresenting a blurred input vector;
Figure BDA0002838389260000075
a fuzzy weight vector representing an unknown parameter, # t (χ (t)) [ ψ [ #1(χ(t)),ψ2(χ(t)),...,ψN(χ(t))]T∈RNRepresents a known basis function vector, whose expression is:
Figure BDA0002838389260000076
wherein ,
Figure BDA0002838389260000077
taking a Gaussian function as a fuzzy membership function, namely:
Figure BDA0002838389260000078
l＝1,2,...,i＝1,2,...,M.
Figure BDA0002838389260000079
all represent constants.
The direct self-adaptive fuzzy logic control target for inhibiting the wing rock caused by a large attack angle is as follows:
Figure BDA00028383892600000710
where y (t) is defined as phi (t), where phi (t) denotes the roll angle of the aircraft, and ym(t) represents a planned roll angle; delta1Indicating the error, i.e. the roll angle phi (t) of the aircraft and the planned roll angle y as the time approaches infinitym(t) the error value is within the allowable error range [ - δ [ -d ]1,δ1]The roll angle phi (t) of the airplane can gradually converge into any given error range, and the control precision of the roll angle is improved；
S4, constructing error variables of each order of the direct self-adaptive fuzzy logic system and generating a weight error of an unknown parameter in the self-adaptive process;
the error variables of each order for constructing the direct adaptive fuzzy logic system are expressed as follows:
zi(t),i＝1,2,…,n；
z1(t)＝y(t)-ym(t)
Figure BDA0002838389260000081
wherein ,ym(t) denotes a given output, αi(t), i equals 11(t) aircraft roll angle y (t) and planned roll angle ym(t) error value, z2(t) is a virtual controller α1(t) and the state variable x2(t), planned roll angular acceleration
Figure BDA0002838389260000082
To an error value therebetween.
The expression of the weight error of the unknown parameter generated in the adaptive process is:
Figure BDA0002838389260000083
wherein ,
Figure BDA0002838389260000084
representing the weight error, theta, of the unknown parameter produced during the adaptation process*Represents an unknown parameter generated in the adaptation process, is a constant, theta (t) represents an adaptation parameter in the adaptation process,
Figure BDA0002838389260000085
Figure BDA0002838389260000086
wherein ,
Figure BDA0002838389260000087
dependent on the fuzzy logic weight vector; di(ξi(t)), i ═ 1,2 depends on the approximation error of the wing rock motion model for the direct adaptive fuzzy logic system.
S5, constructing a Lyapunov function by utilizing error variables of each order of a direct self-adaptive fuzzy logic system and weight errors of unknown parameters generated in a self-adaptive process, and making the Lyapunov function semi-positive;
the Lyapunov function constructed was:
Figure BDA0002838389260000088
wherein V (t) represents Lyapunov function, which is half positive definite, and adaptive law is constructed in next step to ensure its derivative
Figure BDA0002838389260000089
Semi-negative, and:
Figure BDA00028383892600000810
thereby enabling the aircraft roll angle output to track the planned roll angle output.
S6, establishing a self-adaptive law of self-adaptive parameters in an online updating self-adaptive process by semi-negatively determining the time derivative of the Lyapunov function, and designing a control signal and a virtual controller of a wing rock motion model to ensure the stability of the system.
By ensuring the time derivative of the Lyapunov function
Figure BDA0002838389260000091
Semi-negatively determining, and establishing an adaptive law expression of adaptive parameters in an online updating adaptive process as follows:
Figure BDA0002838389260000092
namely, carrying out real-time iterative updating through the derivative expression of the adaptive parameter theta (t); the control signal of the wing rock motion model and the expression of the virtual controller are designed to meet the following requirements:
u(t)＝α2(t)
Figure BDA0002838389260000093
Figure BDA0002838389260000094
wherein ：
Figure BDA0002838389260000095
Figure BDA0002838389260000096
Figure BDA0002838389260000097
Figure BDA0002838389260000098
wherein ,
Figure BDA0002838389260000099
represents a known basis function vector, as defined by ψ (χ (t));
Figure BDA00028383892600000910
and psil(χ (t)) is defined identically; n is a radical ofiRepresenting user-defined fuzzy logic rulesThe number of strips.
The positional relationships depicted in the drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A direct adaptive fuzzy logic control method for inhibiting wing rock caused by a large attack angle is characterized by at least comprising the following steps:
s1, establishing a wing rock motion model;
s2, defining a standard form of a nonlinear system model, and converting the wing rock motion model into the nonlinear system model according to the standard form of the nonlinear system model;
s3, establishing a direct self-adaptive fuzzy logic system, and definitely inhibiting a direct self-adaptive fuzzy logic control target of the wing rock caused by a large attack angle;
s4, constructing error variables of each order of the direct self-adaptive fuzzy logic system and generating a weight error of an unknown parameter in the self-adaptive process;
s5, constructing a Lyapunov function by utilizing error variables of each order of a direct self-adaptive fuzzy logic system and weight errors of unknown parameters generated in a self-adaptive process, and making the Lyapunov function semi-positive;
s6, establishing a self-adaptive law of self-adaptive parameters in an online updating self-adaptive process by semi-negatively determining the time derivative of the Lyapunov function, and designing a control signal and a virtual controller of a wing rock motion model to ensure the stability of the system.
2. The direct adaptive fuzzy logic control method for suppressing the wing rock induced by the large attack angle according to claim 1, wherein the expression of the wing rock motion model in step S1 is:
Figure FDA0002838389250000011
where φ (t) represents the roll angle, u (t) represents the control signal, α represents the steady state aircraft angle of attack,
Figure FDA0002838389250000012
representing an uncertain aerodynamic disturbance, KiI ═ 0, 1., 4 denotes a known constant, satisfying:
Figure FDA0002838389250000013
Figure FDA0002838389250000014
wherein q represents the free flow pressure, S represents the airfoil area, b represents the airfoil span, IxxShowing the rolling inertia moment, V showing the flying speed,
Figure FDA0002838389250000015
the moment coefficient is represented as a dimensionless coefficient, i is 0, 1.
3. The direct adaptive fuzzy logic control method for suppressing the wing rock induced by the large attack angle according to claim 2, wherein the standard form of the nonlinear system model defined in the step S2 is:
Figure FDA0002838389250000021
Figure FDA0002838389250000022
y(t)＝x1(t)
wherein ,
Figure FDA0002838389250000023
is a measurable state variable of a set of nonlinear systems; f. ofi:Ri→R,i＝1,2,…,n；gn:Rn→ R represents the unknown in-system dynamics and unknown control gain, and y (t) epsilon R represents the output of the nonlinear system model; u (t) denotes a control signal.
4. The direct adaptive fuzzy logic control method for inhibiting the wing rock caused by the large attack angle according to claim 3, wherein the process of converting the wing rock motion model into the nonlinear system model satisfies the following steps:
let x1＝φ,
Figure FDA0002838389250000024
At the moment, the representation of a uniform variable t is omitted, and a mathematical model of the wing rock motion is converted into a nonlinear system model, wherein the expression is as follows:
Figure FDA0002838389250000025
Figure FDA0002838389250000026
wherein ,KiI ═ 0, 1., 4 denotes a known constant; α represents the steady state aircraft angle of attack; u (t) denotes a control signal.
5. The direct adaptive fuzzy logic control method for suppressing the wing rock induced by the large attack angle according to claim 4, wherein the direct adaptive fuzzy logic system of the step S3 is:
P(χ(t))＝λ*Tψ(χ(t))
wherein, χ (t) ═ χ1(t),χ2(t),...,χM(t)]T∈RMRepresenting a blurred input vector;
Figure FDA0002838389250000027
a fuzzy weight vector representing an unknown parameter, # t (χ (t)) [ ψ [ #1(χ(t)),ψ2(χ(t)),…,ψN(χ(t))]T∈RNRepresents a known basis function vector, whose expression is:
Figure FDA0002838389250000028
wherein ,
Figure FDA0002838389250000029
taking a Gaussian function as a fuzzy membership function, namely:
Figure FDA00028383892500000210
l＝1,2,...,i＝1,2,...,M.
Figure FDA00028383892500000211
all represent constants.
6. The direct adaptive fuzzy logic control method for suppressing the wing rock induced by the large attack angle according to claim 5, wherein the control targets in the step S3 are:
Figure FDA0002838389250000031
where y (t) is defined as phi (t), where phi (t) denotes the roll angle of the aircraft, and ym(t) represents a planned roll angle; delta1Indicating an error.
7. The direct adaptive fuzzy logic control method for suppressing the wing rock induced by the large attack angle according to claim 6, wherein the error variables of each order for constructing the direct adaptive fuzzy logic system in step S4 are expressed as:
zi(t),i＝1,2,...,n；
z1(t)＝y(t)-ym(t)
Figure FDA0002838389250000032
wherein ,ym(t) denotes a given output, αi(t), i equals 1, …, n denotes a virtual controller, and when n equals 2, z equals1(t) aircraft roll angle y (t) and planned roll angle ym(t) error value, z2(t) is the virtual controller and state variable x2(t), planned roll angular acceleration
Figure FDA0002838389250000033
To an error value therebetween.
8. The direct adaptive fuzzy logic control method for suppressing the wing rock induced by the large attack angle according to claim 7, wherein the expression of the weight error of the unknown parameter generated in the adaptive process in step S4 is as follows:
Figure FDA0002838389250000034
wherein ,
Figure FDA0002838389250000035
representing the weight error, theta, of the unknown parameter produced during the adaptation process*Represents an unknown parameter generated in the adaptation process, is a constant, theta (t) represents an adaptation parameter in the adaptation process,
Figure FDA0002838389250000036
Figure FDA0002838389250000037
wherein ,
Figure FDA0002838389250000038
dependent on the fuzzy logic weight vector; di(ξi(t)), i is 1 and 2 depends on the approximation error of the wing rock motion model by the direct adaptive fuzzy logic system.
9. The direct adaptive fuzzy logic control method for suppressing high incidence induced wing rock rolling according to claim 8, wherein the lyapunov function constructed in step S5 is:
Figure FDA0002838389250000039
wherein V (t) represents Lyapunov function, which is half positive, and adaptive law is constructed in the next step to ensure its derivative
Figure FDA0002838389250000041
Semi-negative determination; and:
Figure FDA0002838389250000042
10. the direct adaptive fuzzy logic control method for suppressing high incidence induced wing rock rolling according to claim 9, wherein step S6By ensuring the time derivative of the Lyapunov function
Figure FDA0002838389250000043
Semi-negatively determining, and establishing an adaptive law expression of adaptive parameters in an online updating adaptive process as follows:
Figure FDA0002838389250000044
namely, carrying out real-time iterative updating through the derivative expression of the adaptive parameter theta (t); control signal u (t) and virtual controller alpha for designing wing rock motion modeliThe expression of (t) satisfies:
u(t)＝α2(t)
Figure FDA0002838389250000045
Figure FDA0002838389250000046
wherein ：
Figure FDA0002838389250000047
Figure FDA0002838389250000048
Figure FDA0002838389250000049
Figure FDA00028383892500000410
wherein ,
Figure FDA00028383892500000411
represents a known basis function vector, as defined by ψ (χ (t));
Figure FDA00028383892500000412
and psil(χ (t)) is defined identically; n is a radical ofiRepresenting the number of pieces of user-defined fuzzy logic rules.
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