CN112527007B - 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 PDFInfo
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Abstract
The invention provides a direct self-adaptive fuzzy logic control method for inhibiting wing rock caused by a large attack angle, relates to the technical field of aircraft wing rock control, solves the problems that in the existing control method for the wing rock caused by the large attack angle, the design of a controller is complex, the parameter of the controller is difficult to solve and the control precision is limited, firstly establishes a wing rock motion model, then converts the wing rock motion model into a nonlinear system model, and establishes a direct self-adaptive fuzzy logic system.
Description
Technical Field
The invention relates to the technical field of aircraft 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
Fighter aircraft is a military aircraft for eliminating enemy aircraft in the air, is a main model of military air combat, and occupies an irreplaceable position in both ground and air fighter. The task requirement of high performance fighters is to operate normally at high angles of attack, while unstable aerodynamic effects at high angles of attack can produce wing roll phenomena that manifest as extreme periodic oscillations in roll. When a fighter plane enters a large attack angle area, aerodynamic and flight characteristics are greatly changed, such as nonlinearity, asymmetry, cross coupling and the like of aerodynamic force, so that the stability and operability of the plane are rapidly changed, a plurality of special flight phenomena such as wing shaking, pitching, nose lateral deviation, overspeed rotation, deep stall, tail rotation and the like occur, and the flight state is dangerous and uncontrollable, if the fighter plane cannot be separated as soon as possible, unexpected serious consequences can be caused to pilots and fighter planes.
The traditional flight control method is that a self-adaptive PID controller based on a self-circulation wavelet neural network identifier is mostly based on linear model design, a local linear or global linear fitting method is adopted, the design of the controller is complex, the parameter solving of the controller is difficult, or a fighter plane large attack angle dynamic inverse control method based on a slide film interference observer is disclosed in China patent (publication number: CN 111610794A), aiming at a large attack angle flight state, a 'time scale separation' method is adopted, an aircraft state variable is decomposed into two groups of subsystems based on different time scales, the control law is solved by a dynamic inverse method respectively, uncertainty of the dynamic inverse design method is compensated by combining a supercoiled slide mode interference observer, and a fighter plane disturbed attitude system stable controller is designed. By reasonably selecting the parameters of the controller, the error can be stabilized and bounded, so that good tracking performance and stability of a flight control system under a large attack angle of a fighter plane are ensured, dangerous states such as deep stall and tail spin are timely changed, good reference significance is provided for practical engineering application, but the control accuracy of a control method based on an interference observer is limited, and the convergence and stability of a closed-loop control system cannot be ensured by an exact theory.
Disclosure of Invention
In order to solve the problems that the design of a controller is complex, the parameter solving of the controller is difficult, and the control precision is limited in the existing control method for the wing rock caused by the large attack angle, the invention provides a direct self-adaptive fuzzy logic control method for inhibiting the wing rock caused by the large attack angle, which reduces the design complexity and the calculation amount of the controller, improves the reliability of a control system, and ensures the stability of the wing in any given error.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
a direct self-adaptive fuzzy logic control method for inhibiting wing rock caused by a large attack angle at least comprises the following steps:
s1, building a wing rock movement 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 restraining a direct self-adaptive fuzzy logic control target of wing rock caused by a large attack angle;
s4, constructing each order error variable 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 each-order error variable of the direct self-adaptive fuzzy logic system and a weight error of an unknown parameter generated in the self-adaptive process, and enabling the Lyapunov function to be semi-positive;
s6, establishing an adaptive law of adaptive parameters in an online updating adaptive process by enabling the time derivative of the Lyapunov function to be semi-negative, 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:
wherein phi (t) represents the roll angle, u (t) represents the control signal, alpha represents the aircraft angle of attack in steady state,
representing uncertain aerodynamic disturbances, K i I=0, 1,..4 represents a known constant, satisfying:
wherein q represents free flow pressure, S represents wing area, b represents wing span, I xx Represents the roll moment of inertia, V represents the flying speed,
the moment coefficient is represented as a dimensionless coefficient, i=0, 1..,4。
Preferably, the standard form of defining the nonlinear system model in step S2 is:
y(t)＝x 1 (t)
wherein ,
is a set of measurable state variables for a nonlinear system; f (f) i :R i →R,i＝1,2,…,n；g n :R n R represents unknown in-system dynamics and unknown control gain, and y (t) E R represents the output of the nonlinear system model; u (t) represents a control signal.
Preferably, the process of converting the wing rock motion model into a nonlinear system model satisfies:
order the
At the moment, the representation of the unified 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:
wherein ,Ki I=0, 1..4 represents a known constant; alpha represents the aircraft attack angle in steady state; u (t) represents a control signal.
Preferably, the direct adaptive fuzzy logic system described in step S3 is:
P(χ(t))＝λ *T ψ(χ(t))
wherein χ (t) = [ χ ] 1 (t),χ 2 (t),...,χ M (t)] T ∈R M Representing a fuzzy input vector;
fuzzy weight vector representing unknown parameters, ψ (χ (t))= [ ψ ] 1 (χ(t)),ψ 2 (χ(t)),…,ψ N (χ(t))] T ∈R N Representing a known basis function vector, the expression of which is:
wherein ,
taking a Gaussian function as a fuzzy membership function, namely:
l＝1,2,…,i＝1,2,…,M.
Preferably, the control target in step S3 is:
wherein y (t) =φ (t) is defined and φ (t) represents the roll angle of the aircraft, y m (t) represents a planned roll angle; delta 1 Representing errors, i.e. the roll angle phi (t) of the aircraft and the planned roll angle y when the time goes to infinity m The error value of (t) is within the allowable error range [ -delta ] 1 ,δ 1 ]In, tooNamely, the roll angle phi (t) of the airplane can be gradually converged into any given error range, and the control precision of the roll angle is improved.
Preferably, the respective order error variables for constructing the direct-adaptive fuzzy logic system described in step S4 are expressed as:
z i (t),i＝1,2,…,n；
z 1 (t)＝y(t)-y m (t)
wherein ,ym (t) represents a given output, α i (t), i=1,..n represents a virtual controller, taking n=2, then z 1 (t) is the aircraft roll angle y (t) and the planned roll angle y m Error value of (t), z 2 (t) is the virtual controller alpha 1 (t) and state variable x 2 (t), planned roll angle acceleration
Error values between the two.
Preferably, the expression for generating the weight error of the unknown parameter in the adaptive process in step S4 is:
wherein ,
representing the weight error, θ, of an unknown parameter generated during adaptation * Represents an unknown parameter generated in the adaptive process, is a constant, θ (t) represents the adaptive parameter in the adaptive process,
wherein ,
depending on the fuzzy logic weight vector; d, d i (ξ i (t)), i=1, 2 depends on the approximation error of the direct adaptive fuzzy logic system to the wing rock motion model.
Preferably, the lyapunov function constructed in step S5 is:
wherein V (t) represents a Lyapunov function, which is semi-positive, and an adaptive law is constructed in the next step to ensure the derivative thereof
Half negative definite, and: />
Thereby enabling the aircraft roll angle output to track the planned roll angle output.
Preferably, step S6 is performed by ensuring the time derivative of the Lyapunov function
The self-adaptive law expression of the self-adaptive parameters in the self-adaptive process of the on-line updating is established as follows:
namely, carrying out real-time iterative updating through a derivative expression of the self-adaptive parameter theta (t); the expression of the control signal and the virtual controller for designing the wing rock motion model meets the following conditions:
u(t)＝α 2 (t)
wherein ：
wherein ,
representing a known basis function vector, the same definition as ψ (χ (t)); />
And psi is equal to l (χ (t)) is the same; n (N) i Representing the number of fuzzy logic rules formulated by the user.
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 the 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 occurrence of wing rock 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 present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for better illustration of the present embodiment, some parts of the drawings may be omitted, enlarged or reduced, and do not represent actual dimensions;
it will be appreciated by those skilled in the art that some well known descriptions in the figures may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
Example 1
The positional relationship depicted in the drawings is for illustrative purposes only and is not to be construed as limiting the present patent;
the flow diagram of the direct adaptive fuzzy logic control method for inhibiting the wing rock caused by the large attack angle shown in fig. 1 comprises the following steps:
s1, building a wing rock movement model; the expression of the wing rock motion model is as follows:
wherein phi (t) represents the roll angle, u (t) represents the control signal, alpha represents the aircraft angle of attack in steady state,
representing uncertain aerodynamic disturbances, K i I=0, 1,..4 represents a known constant, satisfying:
wherein q represents free flow pressure, S represents wing area, b represents wing span, I xx Represents the roll moment of inertia, V represents the flying speed,
the moment coefficient is represented as a dimensionless coefficient, i=0, 1,..4.
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 defining the nonlinear system model in step S2 is:
y(t)＝x 1 (t)
wherein ,
is a set of measurable state variables for a nonlinear system; f (f) i :R i →R,i＝1,2,...,n；g n :R n R represents unknown in-system dynamics and unknown control gain, y (t) ε R represents a nonlinear system modelOutputting; u (t) represents a control signal.
The process of converting the wing rock motion model into the nonlinear system model satisfies the following conditions:
order the
At the moment, the representation of the unified 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:
wherein ,Ki I=0, 1, …,4 represents a known constant; alpha represents the aircraft attack angle in steady state; u (t) represents a control signal; specifically, a schematic view 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 restraining a direct self-adaptive fuzzy logic control target of wing rock caused by a large attack angle; the direct adaptive fuzzy logic system described in step S3 is:
P(χ(t))＝λ *T ψ(χ(t))
wherein χ (t) = [ χ ] 1 (t),χ 2 (t),...,χ M (t)] T ∈R M Representing a fuzzy input vector;
fuzzy weight vector representing unknown parameters, ψ (χ (t))= [ ψ ] 1 (χ(t)),ψ 2 (χ(t)),...,ψ N (χ(t))] T ∈R N Representing a known basis function vector, the expression of which is:
wherein ,
taking a Gaussian function as a fuzzy membership function, namely:
l＝1,2,...,i＝1,2,...,M.
The direct self-adaptive fuzzy logic control targets for inhibiting the wing rock caused by the large attack angle are as follows:
wherein y (t) =φ (t) is defined and φ (t) represents the roll angle of the aircraft, y m (t) represents a planned roll angle; delta 1 Representing errors, i.e. the roll angle phi (t) of the aircraft and the planned roll angle y when the time goes to infinity m The error value of (t) is within the allowable error range [ -delta ] 1 ,δ 1 ]The roll angle phi (t) of the airplane can be gradually converged into any given error range, so that the control precision of the roll angle is improved;
s4, constructing each order error variable 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 of the construction of the direct self-adaptive fuzzy logic system are expressed as follows:
z i (t),i＝1,2,…,n；
z 1 (t)＝y(t)-y m (t)
wherein ,ym (t) represents a given output, α i (t), i=1,..n represents a virtual controller, taking n=2, then z 1 (t) is the aircraft roll angle y (t) and the planned roll angle y m Error value of (t), z 2 (t) is the virtual controller alpha 1 (t) and state variable x 2 (t), planned roll angle acceleration
Error values between the two.
The expression of the weight error of the unknown parameters generated in the self-adaption process is as follows:
wherein ,
representing the weight error, θ, of an unknown parameter generated during adaptation * Represents an unknown parameter generated in the adaptive process, is a constant, θ (t) represents the adaptive parameter in the adaptive process,
wherein ,
depending on the fuzzy logic weight vector; d, d i (ξ i (t)), i=1, 2 depends on the approximation error of the direct adaptive fuzzy logic system to the wing rock motion model.
S5, constructing a Lyapunov function by utilizing each-order error variable of the direct self-adaptive fuzzy logic system and a weight error of an unknown parameter generated in the self-adaptive process, and enabling the Lyapunov function to be semi-positive;
the constructed lyapunov function is:
wherein V (t) represents the Lyapunov function, the Lyapunov function is semi-positive, and the adaptive law is built in the next step to ensure the derivative thereof
Half negative definite, and: />
Thereby enabling the aircraft roll angle output to track the planned roll angle output.
S6, establishing an adaptive law of adaptive parameters in an online updating adaptive process by enabling the time derivative of the Lyapunov function to be semi-negative, 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
The self-adaptive law expression of the self-adaptive parameters in the self-adaptive process of the on-line updating is established as follows:
namely, carrying out real-time iterative updating through a derivative expression of the self-adaptive parameter theta (t); the expression of the control signal and the virtual controller for designing the wing rock motion model meets the following conditions:
u(t)＝α 2 (t)
wherein ：
wherein ,
representing a known basis function vector, the same definition as ψ (χ (t)); />
And psi is equal to l (χ (t)) is the same; n (N) i Representing the number of fuzzy logic rules formulated by the user.
The positional relationship depicted in the drawings is for illustrative purposes only and is not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and are not intended to limit the scope of the invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (8)
1. A direct self-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, building a wing rock movement 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 restraining a direct self-adaptive fuzzy logic control target of wing rock caused by a large attack angle;
the direct adaptive fuzzy logic system described in step S3 is:
P(χ(t))＝λ *T ψ(χ(t))
wherein χ (t) = [ χ ] 1 (t),χ 2 (t),...,χ M (t)] T ∈R M Representing a fuzzy input vector;
fuzzy weight vector representing unknown parameters, ψ (χ (t))= [ ψ ] 1 (χ(t)),ψ 2 (χ(t)),...,ψ N (χ(t))] T ∈R N Representing a known basis function vector, the expression of which is:
wherein ,
taking a Gaussian function as a fuzzy membership function, namely:
the control targets described in step S3 are:
wherein y (t) =φ (t) is defined and φ (t) represents the roll angle of the aircraft, y m (t) represents a planned roll angle; delta 1 Representing the error;
s4, constructing each order error variable 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 each-order error variable of the direct self-adaptive fuzzy logic system and a weight error of an unknown parameter generated in the self-adaptive process, and enabling the Lyapunov function to be semi-positive;
s6, establishing an adaptive law of adaptive parameters in an online updating adaptive process by enabling the time derivative of the Lyapunov function to be semi-negative, 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 a large angle of attack induced wing rock of claim 1, wherein the expression of the wing rock motion model of step S1 is:
wherein phi (t) represents the roll angle, u (t) represents the control signal, alpha represents the aircraft angle of attack in steady state,
representing uncertain aerodynamic disturbances, K i I=0, 1,..4 represents a known constant, satisfying: />
Wherein q represents free flow pressure, S represents wing area, b represents wing span, I xx Represents the roll moment of inertia, V represents the flying speed,
the moment coefficient is represented as a dimensionless coefficient, i=0, 1,..4.
3. The direct adaptive fuzzy logic control method of claim 2, wherein the standard form of the nonlinear system model defined in step S2 is:
y(t)＝x 1 (t)
wherein ,
is a set of measurable state variables for a nonlinear system; f (f) i :R i R, i=1, 2,.. n :R n R represents unknown in-system dynamics and unknown control gain, and y (t) E R represents the output of a nonlinear system model; u (t) represents a control signal.
4. The method for direct adaptive fuzzy logic control of a large angle of attack induced wing rock of claim 3, wherein the process of converting the wing rock motion model into a nonlinear system model is as follows:
let x 1 ＝φ,
At the moment, the representation of the unified 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:
wherein ,Ki I=0, 1..4 represents a known constant; alpha represents the aircraft attack angle in steady state; u (t) represents a control signal.
5. The method for controlling a direct adaptive fuzzy logic system for suppressing a roll induced by a large angle of attack as set forth in claim 4, wherein the step S4 is characterized in that the error variables of each order of constructing the direct adaptive fuzzy logic system are expressed as:
z i (t),i＝1,2,...,n；
z 1 (t)＝y(t)-y m (t)
wherein ,αi (t), i=1,..n represents a virtual controller, taking n=2, then z 1 (t) is the aircraft roll angle y (t) and the planned roll angle y m Error value of (t), z 2 (t) is virtual controlController and state variable x 2 (t), planned roll angle acceleration
Error values between the two.
6. The direct adaptive fuzzy logic control method of suppressing a large angle of attack induced wing rock of claim 5, wherein the expression of the weight error of the unknown parameter generated in the adaptive process of step S4 is:
wherein ,
representing the weight error, θ, of an unknown parameter generated during adaptation * Representing the unknown parameters generated during the adaptation process as constants, θ (t) representing the adaptation parameters during the adaptation process,/->
wherein ,
depending on the fuzzy logic weight vector; d, d i (ξ i (t)), i=1, 2 depends on the approximation error of the direct adaptive fuzzy logic system to the wing rock motion model.
7. The method for directly adaptive fuzzy logic control of suppressing a large angle of attack induced wing rock of claim 6, wherein the constructed lyapunov function of step S5 is:
wherein V (t) represents a Lyapunov function, which is semi-positive, and an adaptive law is constructed in the next step to ensure the derivative thereof
Semi-negative setting; and: />
8. The method of claim 7, wherein step S6 is performed by ensuring the time derivative of Lyapunov function
The self-adaptive law expression of the self-adaptive parameters in the self-adaptive process of the on-line updating is established as follows:
namely, carrying out real-time iterative updating through a derivative expression of the self-adaptive parameter theta (t); control signal u (t) and virtual controller alpha for designing wing rock motion model i The expression of (t) satisfies:
u(t)＝α 2 (t)
wherein ：
ξ 1 (t)＝[x 1 (t),y m (t)] T
wherein ,
representing a known basis function vector, the same definition as ψ (χ (t)); />
And psi is equal to l (χ (t)) is the same; n (N) i Representing the number of fuzzy logic rules formulated by the user. />
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