CN111122899A - Incidence angle sideslip angle estimation method for flying in atmospheric disturbance - Google Patents
Incidence angle sideslip angle estimation method for flying in atmospheric disturbance Download PDFInfo
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- G01—MEASURING; TESTING
- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P13/00—Indicating or recording presence, absence, or direction, of movement
- G01P13/02—Indicating direction only, e.g. by weather vane
- G01P13/025—Indicating direction only, e.g. by weather vane indicating air data, i.e. flight variables of an aircraft, e.g. angle of attack, side slip, shear, yaw
Abstract
The invention discloses an incidence angle sideslip angle estimation method for flying in atmospheric disturbance, and belongs to the technical field of calculation, calculation or counting. The invention aims to accurately estimate the attack angle and the sideslip angle of the airplane flying in atmospheric disturbance, and the method comprises the following steps: obtaining estimation of low-frequency disturbance wind by an optimization method according to the aircraft modeling data and the flight data; and establishing a flight state equation and a measurement equation, and combining a von Karman atmospheric turbulence model and adopting a self-adaptive filtering method according with a maximum likelihood criterion to accurately estimate the attack angle and the sideslip angle of the airplane.
Description
Technical Field
The invention discloses an attack angle sideslip angle estimation method for flight in atmospheric disturbance, relates to the fields of civil aviation safety technology and flight data application, in particular to a method for estimating an attack angle and a sideslip angle of flight in atmospheric disturbance by using an optimization algorithm and an optimal estimation theory, and belongs to the technical field of calculation, calculation or counting.
Background
The flight quality, riding quality and flight safety of civil aircrafts are seriously influenced by atmospheric disturbance phenomena such as wind shear, atmospheric turbulence and the like. The flight data recorder acquires recording parameters from various systems such as an atmospheric data system, an inertial navigation system and the like from the onboard bus, so that the flight state, the engine state, the flight management, the working state of a flight control system and the like can be recorded in real time. In atmospheric disturbance flight, due to the influence of disturbance wind, an aircraft atmospheric data system is difficult to accurately acquire atmospheric data such as airspeed, angle of attack, sideslip angle and the like, so that the recording of the parameters is inaccurate. As is well known, the angle of attack and the angle of sideslip directly determine the aerodynamic characteristics of the aircraft and are also important input parameters of a flight safety envelope protection system.
The aircraft inertial navigation system is an autonomous navigation device and is not influenced by the environment. Therefore, the ground speed, attitude angle and acceleration data from the inertial navigation system in the flight record data have high precision. The method is a feasible technical approach for estimating airspeed, angle of attack, sideslip angle and the like according to inertial navigation data in flight data by combining the characteristics of atmospheric disturbance.
Currently, there are two main methods for estimating the aircraft angle of attack and sideslip. One is a geometric approach to low frequency disturbing winds, such as wind shear. Under the premise of disturbance wind calibration constant hypothesis and airplane particle hypothesis, firstly, a track angle is obtained through calculation, and then an attack angle and a sideslip angle are obtained through coordinate transformation and geometric analysis according to vector relations of wind speed, airspeed and ground speed. This method does not meet the basic premise that disturbing winds first influence the air flow angle and then influence the track angle.
And the other method is oriented to high-frequency atmospheric turbulence, disturbance wind is regarded as a random process, a flight dynamics model is combined, inertial navigation recorded data are used as observed quantities, and a Kalman filtering algorithm is constructed. The method integrates the dynamic model solving and the state estimating process, and has high requirement on modeling precision. In addition, the influence of model errors and data recording noise uncertainty on the state estimation process is not considered, and the initial value of the filter is given without basis, and uncertain random noise easily causes filter divergence. Actual high-frequency atmospheric turbulence is often accompanied by low-frequency wind shear, and in order to realize accurate estimation of an attack angle sideslip angle, the problems of initial value setting of disturbance wind, suppression of uncertain noise and the like need to be solved.
Disclosure of Invention
The invention aims to overcome the defects of the background technology, provides an incidence angle sideslip angle estimation method for flying in atmospheric disturbance, realizes accurate estimation of an incidence angle sideslip angle under turbulent disturbance wind, and solves the technical problems that the existing aircraft incidence angle sideslip angle estimation does not conform to the basic premise that disturbance wind firstly influences an airflow angle and then influences a flight path angle, and the initial value of Kalman filtering lacks basis and filtering stability is difficult to guarantee.
The invention adopts the following technical scheme for realizing the aim of the invention:
an incidence angle sideslip angle estimation method for flying in atmospheric disturbance is used for realizing the estimation of an incidence angle sideslip angle in two stages. The method comprises the following steps that firstly, estimation of space low-frequency disturbance wind is obtained through an optimization method by combining a flight dynamics model and flight record data; and in the second stage, the noise characteristics of flight data are considered, a Kalman filtering system framework is established by combining a von Karman atmospheric turbulence model, and the accurate estimation of the angle of attack and sideslip angle under the influence of high-frequency disturbance wind is realized by adopting a maximum likelihood estimation adaptive filtering algorithm.
The method comprises the following steps: respectively establishing a quality model, a pneumatic model and an engine performance model according to modeling data of a certain machine type, obtaining flight data of a flight of the certain machine type, and obtaining noise characteristics of related data according to flight record specifications;
step two: establishing a dynamic equation set containing acceleration and angular acceleration, and obtaining airspeed, attack angle and sideslip angle pre-estimated values according to triaxial acceleration and angular acceleration data recorded by flight data by adopting an immune clone optimization algorithm;
step three: obtaining a space three-dimensional low-frequency disturbance wind vector based on the estimated airspeed, attack angle and sideslip angle by adopting a nonlinear Gauss-Newton optimization algorithm according to the vector relation of the ground speed, airspeed and disturbance wind of the airplane;
step four: establishing a state equation and a measurement equation for estimating an angle of attack sideslip angle according to a von Karman atmospheric turbulence model and a flight dynamics equation;
step five: and (3) obtaining accurate estimation of the angle of attack sideslip angle by adopting a maximum likelihood estimation self-adaptive filtering method, and carrying out real-time estimation and adjustment on a system and a measured noise variance matrix by utilizing an innovation sequence in the filtering process, wherein the low-frequency disturbance wind vector obtained in the step three is used as an initial value of a filter.
By adopting the technical scheme, the invention has the following beneficial effects: aiming at the condition that the records of the wind speed and the wind direction in the flight data are not accurate enough and only horizontal wind is recorded, the method can estimate a spatial three-dimensional disturbance wind vector serving as a filter initial value by combining a flight dynamics model and the flight record data, and establishes a Kalman filtering system framework by combining a von Karman atmospheric turbulence model to realize the accurate estimation of the attack angle and the sideslip angle under the influence of high-frequency disturbance wind, thereby effectively improving the estimation precision of the attack angle and the sideslip angle, improving the defect that only the attack angle but not the sideslip angle exists in the original data record, and having positive significance for flight quality monitoring and flight accidents and safety analysis induced by atmospheric disturbance.
Drawings
FIG. 1 is a general block diagram of a method for estimating angle of attack and sideslip angle for flight in atmospheric disturbances.
FIG. 2 is a schematic diagram of a low frequency disturbance wind optimization solution process.
Detailed Description
The technical scheme of the invention is explained in detail in the following with reference to the attached drawings.
As shown in FIG. 1, the method for estimating the angle of attack and sideslip angle for flight in atmospheric disturbance disclosed by the invention sequentially comprises the following five steps.
The method comprises the following steps: and respectively establishing a quality model, a pneumatic model and an engine performance model according to modeling data of a certain model. Flight data of a flight of the airplane type is obtained, and noise characteristics of related data are obtained according to flight record specifications.
The aircraft mass model should provide the mass m, inertia matrix of the aircraft
The engine performance model is to establish the thrust T and the throttle opening delta of the enginetMach number M and altitude h.
The aerodynamic model of the airplane is to respectively establish longitudinal aerodynamic coefficients cxTransverse aerodynamic coefficient cyNormal aerodynamic coefficient czCoefficient of rolling moment clCoefficient of pitching moment cmAnd yaw moment coefficient cnAnd the nonlinear function relation between the flight state and the deflection angle of the control surface. On the basis, aerodynamic force F is obtained through calculationx、Fy、FzSum moment Mx、My、Mz：
Wherein the content of the first and second substances,
is dynamic pressure, S is wing area, b is wingspan,
is the average aerodynamic chord length, [ T ]x,Ty,Tz]TThe components of engine thrust in three axes.
Taking the flight record data of a certain flight of the machine type as a research example. The acquired data entries include three-axis angular velocities [ p, q, r ]]TThree-axis acceleration
Attitude angle [ phi, theta ]]T(ii) a Airspeed VbGround speed VgAngle of attack α, height h, total temperature t, Mach number M, elevator deflection angle deltaeAileron declination angle deltaaRudder deflection angle deltarThrottle opening deltatAnd the like. And obtaining the value range, sampling frequency, noise (precision) characteristics and the like of the relevant data from the flight data recording specification.
Step two: and establishing a flight dynamics equation system containing the acceleration and the angular acceleration. Recording angular velocities [ p, q, r ] from flight data using an immune clone optimization algorithm]TAcceleration of the vehicle
Obtaining airspeed VTAngle of attack α, and sideslip angle β according to principles of modeling of flight dynamics, including acceleration
And angular acceleration
The nonlinear differential equation of (a) is expressed as follows:
wherein the content of the first and second substances,
g is the acceleration of gravity. Can be represented by the following formula
And [ V, α]TComplex nonlinear relationship of (a):
recording in real time from flight data
(V) is obtained by solving the above formulaTα) but the conventional algorithm is difficult to solve the equation (3), so the solution problem is converted into the optimization problem shown in (4), and the immune clone optimization algorithm is adopted to obtain (V)Tα) the optimization problem may be as follows:
wherein
Due to (V)Tα) are coupled to each other and are limited to a certain range, and r (x) is a limiting condition derived from the aircraft safety envelope liAnd uiRespectively representing the upper limit and the lower limit of the value of each parameter.
The immune clone algorithm is used as an evolutionary algorithm and has better convergence performance than a genetic algorithm. Furthermore, complex pneumatic models may cause the genetic optimization process to fall into local minima. This "premature" phenomenon can be avoided by using an immune cloning algorithm. The immunoconclone protocol for solving the optimization problem of formula (4) comprises the following steps A to E
Step A: given the maximum number of iterations gmaxPopulation size N, beneficial solution size N0And the cloning ratio R. Randomly generating an initial antibody population A (i)0)＝{a1(i0),a2(i0),…,aN(i0) Get N out of it0Individual antibody as memory unit
And B: for the current antibody population A (i)k) And (5) carrying out recombination operation. First, the center of any three individuals is calculated. And then carrying out random scaling to search solutions in different directions. To A (i)k) Performing the above operation to obtain a temporary population L (i) after recombinationk). Mixing L (i)k) And A (i)k) Combining to obtain population B (i)k). Subsequently, B (i) is combinedk) And M (i)k) And cloning the combined population to obtain Z (i)k)。
And C: to Z (i)k) Performing mutation operation to obtain new population V (i)k). When the fitness of a single antibody is high, the degree of variation is small; conversely, when the fitness of an individual is low, the degree of variation is large. It is also necessary to examine V (i)k) Is out of limits. For V (i)k) A certain entity, if rT(x) r (x) > 0, this indicates that the individual is out of limits. r isT(x) r (x) represents the extent to which the individual is out of the limit. Then V (i) is determined by whether the limit condition is exceededk) Is divided into feasible solutions Xf(ik) And impossible solution
Step D: will solve for Xf(ik) Further divided into preferential solutions Ps(ik) And non-preferential solutions
Selecting N individuals with high fitness as P through population updating operations(ik). At the same time, the infeasible solution can be solved
Is divided into beneficial solutions Qb(ik) And non-beneficial solutions
According to rT(x) r (x) numerical value, converting M (i)k) For individual Qb(ik) Until M (i) is replacedk) All individuals in the system are not inferior to Q in violation of the constraintb(ik) Of (a).
Step E: if the iteration end condition is reached, outputting Ps(ik) As the optimal solution. If the iteration end condition is not reached, P is addeds(ik) As A (i)k+1) And M (i)k) Updated to M (i)k+1). And continuing to execute the loop from the second step.
The pair [ V, α ] is completed by the immune clone algorithm]TIs initially estimated, i.e. the initial estimate is
By adopting the method, low-frequency disturbance wind can be further obtained.
Step three: and obtaining a low-frequency disturbance wind vector based on the estimated airspeed, attack angle and sideslip angle by adopting a nonlinear Gauss-Newton optimization algorithm according to the vector relation of the ground speed, airspeed and disturbance wind of the airplane.
Under the coordinate system of the aircraft body, the relation between the three-axis speed of the aircraft and the airspeed, the attack angle and the sideslip angle is as follows:
and (3) carrying out derivation on the formula, and finishing to obtain:
furthermore, according to the vector relationship between the space ground speed, the airspeed and the disturbance wind, the following formula holds:
wherein the content of the first and second substances,
is airspeed vector under the engine system;
the ground speed vector under the organism system;
is the wind speed vector under the ground system;
is a ground to body transition matrix. The nonlinear relationship of airspeed, angle of attack, sideslip angle, and the spatially low-frequency disturbance wind component can be described by:
solving an optimization problem shown by the following formula by adopting a nonlinear Gauss-Newton optimization algorithm:
wherein the content of the first and second substances,
the estimated value obtained in step two. The iterative formula of the optimization algorithm is as follows:
wherein i is the number of iterations; j. the design is a squareiIs f (x)i) The Jacobian matrix of:
obtaining a spatial low-frequency disturbance wind estimation value through the optimization algorithm
Step four: and establishing a state equation and a measurement equation for estimating an attack angle and a sideslip angle according to the von Karman atmospheric turbulence model and the flight dynamics equation.
The derivation of equation (7) can be found:
substituting equation (6) can obtain:
thereby establishing a state equation with airspeed, angle of attack and sideslip angle as states.
The Von Karman model is an atmospheric turbulence model capable of reflecting the characteristics of high-frequency disturbance wind with high fidelity. The time spectrum of the von karman model is:
in the above formula, ω is the turbulence time frequency, and a is 1.339. Phi1，Φ2，Φ3Representing longitudinal, lateral and vertical turbulence time spectra, respectively. SigmaiAnd LiIs the turbulence intensity and scale as a function of fly height. The real-time turbulent wind field can be generated by exciting a shaping filter by white noise with unit intensity, and the transfer function of the shaping filter can be obtained by the spectral decomposition of the formula (14):
the first order simplification is performed on (15), and model discretization is realized by using a first order backward difference method, so that 3 shaping filters are converted into the following difference form:
wherein i is 1,2, 3. T issIs the sampling period. The equation of state of turbulent disturbing wind is thus expressed as:
thus, the formula [ V, α, W ] is established by the simultaneous expression of the formula (13) and the formula (17)x,Wy,Wz]TIs a parametric filter state equation. Wherein [ wxt,wyt,wzt]TIs gaussian white noise that conforms to a standard normal distribution. In addition, a corresponding measurement equation is established according to the formula (2). After discretizing the two equations, the following equation is shown:
wherein x is [ V ]T,α,β,Wx,Wy,Wz]TFor the state to be estimated, phikBeing a state transition matrix, GkIn order to control the input matrix,
to control the input vector, LkFor system noise driven arrays, wkAs a covariance matrix of QkProcess noise of HkTo measure the matrix, vkAs a covariance matrix of RkThe measurement noise of (2).
Step five: and obtaining accurate estimation of an angle of attack sideslip angle under turbulent disturbance wind by adopting a maximum likelihood estimation self-adaptive filtering method, and estimating and adjusting a system and a measurement noise variance matrix in real time by utilizing an innovation sequence in a filtering process. And taking the low-frequency disturbance wind vector as an initial value of the filter. The iteration process of the basic Kalman filtering is as follows:
the innovation r is defined as the difference between the measured estimate and the actual measured value of the filter at time k:
innovation sequence theory covariance CrComprises the following steps:
the maximum likelihood estimation achieves the actual estimation and adjustment of the noise covariance matrix Q, R from the perspective that the probability of occurrence of the system metric is maximum:
will be provided with
In equation (19), a gain calculation formula based on innovation covariance estimation is obtained:
the adaptive estimation formula of the measurement noise covariance matrix is as follows:
the system noise covariance matrix is adjusted as:
the algorithm can estimate and adjust the system and the measured noise variance in real time by using the actual estimated value of the innovation covariance, better adapts to the change of the recording noise characteristic of flight data, and improves the estimation precision of the angle of attack and sideslip angle.
Claims (5)
1. The method is characterized in that triaxial angular acceleration and triaxial acceleration recorded by flight record data are used as input quantities of a flight dynamics model, nonlinear relations between the triaxial angular acceleration and the triaxial acceleration as well as airspeed, an attack angle and a sideslip angle are established, an immune clone algorithm is adopted to solve the nonlinear relations to obtain pre-estimated values of the airspeed, the attack angle and the sideslip angle, a nonlinear Gauss-Newton optimization algorithm is adopted to solve a vector relation formula of the ground speed, the airspeed and disturbance wind of an airplane to obtain a low-frequency disturbance wind vector based on the pre-estimated values of the airspeed, the attack angle and the sideslip angle, a state equation taking the airspeed, the attack angle and the sideslip angle as states is established according to the vector relation of the ground speed, the airspeed and the disturbance wind, a state equation of the disturbance wind turbulence disturbance wind is established according to a von Karman atmospheric turbulence model, and a state equation for estimating the attack angle is established according to the flight dynamics model, And (3) obtaining a Kalman filter by simultaneously using a measurement equation of the sideslip angle, a state equation taking airspeed, an attack angle and the sideslip angle as states, a measurement equation and a state equation of turbulent disturbance wind, and performing maximum likelihood estimation adaptive filtering by taking a low-frequency disturbance wind vector based on the airspeed, the attack angle and a sideslip angle pre-estimated value as an initial value of the Kalman filter to obtain an accurate estimation value of the attack angle sideslip angle under the turbulent disturbance wind.
2. The method of claim 1, wherein the flight dynamics model is:
for three-axis acceleration, [ p, q, r]TIn order to have the three-axis angular velocity,
in order to achieve the three-axis angular acceleration,
is a three-axis ground speed, Ix、Iy、IzIs a triaxial inertia moment, IxzIs the product of the x-axis and z-axis inertia,
3. the method of claim 1, wherein the method of using an immune clonal algorithm to solve the nonlinear relationship to obtain the estimated values of airspeed, angle of attack, and sideslip angle comprises:
according to the non-linear relation
Constructing an optimization problem:
f1(x) Is the nonlinear relation between triaxial angular acceleration and triaxial acceleration and airspeed, angle of attack and sideslip angle, z is the actual flight data record value of triaxial angular acceleration and triaxial acceleration, r (x) is the limiting condition obtained from the safety envelope of the airplane, VTIs space velocity,/V、uVThe upper and lower limits of airspeed, α angle of attack, lα、uαUpper and lower limits of angle of attack, β sideslip angle, |β、uβThe upper and lower limits of the sideslip angle;
the method comprises the steps of mapping airspeed, an angle of attack and a sideslip angle to antibodies, initializing a population, taking a limited number of antibodies to construct a memory unit set, carrying out recombination and mutation operations on the population to obtain an updated population, dividing the updated population into a feasible solution set and an infeasible solution set according to whether individuals exceed a limit condition, further dividing the feasible solution set into a preferential solution and a non-preferential solution, dividing the infeasible solution set into a beneficial solution and a non-beneficial solution, and replacing the memory unit with the beneficial solution until the extent that all individuals in the memory unit set exceed the limit condition is not inferior to all individuals in the beneficial solution set.
4. The method for estimating the angle of attack and sideslip angle of flight in atmospheric disturbance according to claim 1, characterized in that the specific method for establishing the state equation taking the airspeed, the angle of attack and the sideslip angle as the states according to the vector relation of the ground speed, the airspeed and the disturbance wind is as follows: vector relation of ground speed, airspeed and disturbance wind of airplane
Derived to obtain
Then substituting the ground speed and airspeed of the airplane considering disturbance estimation into relational expressions of the triaxial speed, the airspeed, the attack angle and the sideslip angle of the airplane under the body coordinate system
Obtaining a state equation with airspeed, angle of attack and sideslip angle as states,
is the airspeed vector under the machine body system,
is the ground speed vector under the machine system,
for transfer matrices from ground to body systems, WeIs the wind speed vector under the ground system,
is a pair of
Obliquely symmetrical matrix, V, produced after derivationTFor airspeed, α is the angle of attack and β is the sideslip angle.
5. The method for estimating the angle of attack and sideslip angle for flight in atmospheric disturbance according to claim 1, characterized in that a turbulent disturbance wind state equation established according to a von Karman atmospheric turbulence model is as follows:
for three-axis disturbing winds, T1、K1As a parameter of the first shaping filter, T2、K2For the parameter, T, of the second forming filter3、K3Are parameters of the third shaping filter.
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| CN111679580A (en) * | 2020-06-11 | 2020-09-18 | 江苏理工学院 | Self-adaptive aircraft control system fault compensation and disturbance suppression method |
| CN111679580B (en) * | 2020-06-11 | 2022-05-13 | 江苏理工学院 | Self-adaptive aircraft control system fault compensation and disturbance suppression method |
| EP4012420A1 (en) * | 2020-12-08 | 2022-06-15 | Beijing Interstellar Glory Space Technology Co., Ltd. | Wind estimation method and apparatus for carrier rocket, device and storage medium |
| CN114091180A (en) * | 2021-11-19 | 2022-02-25 | 南京航空航天大学 | Disturbance wind customized modeling and atmospheric data estimation method based on flight data |
| CN114636842A (en) * | 2022-05-17 | 2022-06-17 | 成都信息工程大学 | Atmospheric data estimation method and device for hypersonic aircraft |
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