• 专利附录
  • 专利说明
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    • 专利摘要:
    A method for estimating the three components of the speed vector (TAS, AOA, SSA) of an aircraft relative to the surrounding air, in a reference frame linked to the aircraft, comprising: a first step (1) of estimating an estimated static pressure (PSe) from measurements of geographical altitude; a second step (2) of estimating a first intermediate variation of a linear combination of the three components of the velocity vector of the aircraft relative to the surrounding air explicitly using the fact that the pressure measured by the static probe is distorted; (of a known quantity) under the effect of the three components of this vector speed of the aircraft relative to the surrounding air; a third step (3) of estimating the three components of the speed vector of the aircraft relative to the air by equating it with the speed vector of the aircraft relative to an inertial reference point and by using inertial measurements (accelerometers and gyrometers); and a fourth step (4) and fifth step (5) of merging the estimation of the linear combination of the three components of the speed vector of the aircraft with respect to the air obtained in the second step (2) and the estimation of the three components of the speed vector of the aircraft relative to the surrounding air obtained in the third step (3).
    公开号:FR3023918A1
    申请号:FR1401620
    申请日:2014-07-18
    公开日:2016-01-22
    发明作者:Christian Mehlen；Jacques Coatantiec
    申请人:Thales SA；
    IPC主号:
    专利说明:

    [0001] The present invention relates to a method for estimating the speed of an aircraft with respect to the surrounding air, in a reference frame, a method for estimating the speed of an aircraft relative to the surrounding air, and associated system. linked to the aircraft. The piloting and guidance of an aircraft requires, among other information, knowledge of the speed vector of the aircraft relative to the surrounding air, and that of the barometric altitude. This knowledge of the speed vector of the aircraft relative to the air is obtained from a set of probes that provide local measurements of pressure, air flow direction and air temperature environment , and which constitute input parameters for determining the speed vector of the aircraft relative to the air and the barometric altitude. This determination implements local aerodynamic corrections (SSEC laws as an acronym for "Static Source Error Correction" in English), which translate the matrix coupling between the local measurements and the real values of the speed vector of the aircraft with respect to the surrounding air, and barometric altitude. The velocity vector of an aircraft relative to the surrounding air is usually expressed in spherical coordinates in a trihedron or reference linked to the aircraft, in the form of three components: the aircraft's SAR velocity relative to the surrounding air, the AOA angle of incidence of the aircraft and the SSA skid angle of the aircraft. It can also be expressed in Cartesian coordinates in the reference linked to the aircraft, in the form of three components: VXair, VYair, VZair. The operational safety of the aircraft requires that the knowledge of the aircraft speed vector relative to the air and that of the barometric altitude have a sufficient level of reliability and availability.35 An aircraft is usually equipped with several probe sets that offer physical redundancy. A failure monitoring device is implemented to best deal with this redundancy.
    [0002] Failure detection based on hardware redundancy of sensors of the same design does not detect common mode failures, i.e. a phenomenon capable of simultaneously affecting the smooth operation of multiple sensors. If a failure mode can affect at least half of the sensors, then the isolation of failed sensors is no longer possible. The use of several sensors with dissimilar operating principles makes it possible to reduce the risk of common mode, at the cost of increased complexity. An alternative to physical redundancy is analytical redundancy, which consists of making an estimate of the value of the parameter measured by a sensor, which is not impacted (or as little as possible) by the failure of the actual sensor. The estimation of the value of the parameter implements either the expression of a kinematic coupling with other sources of measurements (usually inertial measurements), or the expression of dynamic evolution constraints based on the mechanics of the flight. either on a combination of the two methods (kinematics and dynamics).
    [0003] The use of redundancy between a sensor measurement and an estimate has various disadvantages. An observer or speed estimator of an aircraft relative to the surrounding air, built by kinematic coupling with the inertial measurements, can hardly eliminate the acceleration of the air with respect to the ground. Therefore, such an observer can not discern a sensor failure leading to an error in the speed of the aircraft relative to the surrounding air lower than the magnitude of the speed of the surrounding air relative to the ground that would develop a strong gust of wind.35 An observer or estimator of an aircraft's speed relative to the surrounding air based on flight mechanics requires the knowledge of certain characteristic data of the aircraft (aerodynamic coefficients, mass, moments inertia, thrust of the engines). Access to this information is not easy. It is possible to identify them in flight (by estimation techniques, explicitly or implicitly) but this operation is usually tricky. The basic problem is the stabilization of the estimator so that the estimated measurement provides a faithful replica of the reality, without directly using the measurement of the real sensor, under penalty of being trained in case of sensor failure, that the measurements provided other sensors (deemed reliable) can not stabilize.
    [0004] The velocity estimator of an aircraft relative to the surrounding air, with kinematic coupling, with inertial measurements (cf JC Deckert et al, 1976, "F-8 aircraft sensor failure identification using analytical redundancy", IEEE) operates in open loop and has limited performance by the uncertainty of the acceleration of the air relative to the ground. To avoid an unacceptable rate of false alarms (detection of failure with each gust of wind), the estimator must be set loosely, which prevents it from detecting a sensor failure leading to a speed error of the sensor. aircraft relative to the surrounding air lower than the magnitude of the air velocity relative to the ground that would cause a strong gust of wind. The state of the art therefore tends to favor dynamically coupled observers, which in principle are less sensitive to the movement of air relative to the ground. The problem is then to know with sufficient precision the characteristic data of the aircraft (aerodynamic coefficients, moments of inertia, thrust of the engines, mass) which intervene in the equations of propagation of the movement. It is known various techniques (explicit estimation of Kalman filtering type) of estimating these data during learning flight phases, using sensors deemed reliable and relying on trajectories offering the required observability. These techniques induce heavy operational constraints.
    [0005] It is also known other techniques (implicit estimation, PCA type for acronym for "Principal Component Analysis" in English, SMI for acronym for "Subspace Model Identification" in English, or OKID for "Observer Kalman Identifier" in English language) consisting of estimating a representation of these data (and not the data directly), online over a time horizon more or less long compared to the current time. In this case the formal validation of the observer's performance is difficult because the analytical link with the physics of the problem, which would have reduced a priori the amount of test cases to pass to demonstrate the rates of missed detections and false alarms is lost. An object of the invention is to overcome these problems. According to one aspect of the invention, there is provided a method for estimating the speed of an aircraft relative to the surrounding air, the angle of incidence of the aircraft, and the angle of skid of the aircraft, in a reference frame linked to the aircraft, comprising: a first step of estimating a static pressure estimated from measurements of the geographical vertical speed of the aircraft, measurements of the air temperature surrounding, re-initializations of the estimated static pressure, and a feedback of said estimated static pressure, by integration on a vertical path of a hydrostatic equation; a second step of estimating a linear combination of a first intermediate variation of the speed of the aircraft relative to the surrounding air, a first intermediate variation of the angle of incidence of the aircraft and of a first intermediate variation of the skid angle of the aircraft from: a difference between a variation of an estimated static pressure and a variation of a measured static pressure; and aerodynamic local corrections dependent on the aircraft; and also comprising estimating a first quality indicator of said linear combination estimated by the second step; a third step of estimating a second intermediate variation of the speed of the aircraft relative to the surrounding air, a second intermediate variation of the angle of incidence of the aircraft and a second intermediate variation of the angle of skidding of the aircraft, based on inertial measurements, a feedback of the estimated speed of the aircraft relative to the surrounding air output, a feedback of the estimated incidence angle of the aircraft at the output, and a feedback of the estimated wander angle of the aircraft at the output, by assimilation of the speed vector of the aircraft relative to the surrounding air to a vector speed of the aircraft relative to an inertial marker obtained by integrating the components of a measured acceleration vector (inertial measurements), corrected for the kinematic effect of the reference linked to the aircraft and increased by the projected gravity in the reference linked to the aircraft; and estimating a second quality indicator of said second intermediate variations; a fourth step of merging said second intermediate variation of the speed of the aircraft with respect to the surrounding air, second intermediate variation of the angle of incidence of the aircraft, and second intermediate variation of the skid angle of the aircraft respectively with said first intermediate variation of the speed of the aircraft relative to the surrounding air, the first intermediate variation of the angle of incidence of the aircraft, and the first intermediate variation of the skid angle of the aircraft, by less square type filtering weighted by said first and second quality indicators or by Kalman type filtering; and merging said first and second quality indicators into a merged quality indicator from the error variance estimated by said filtering; and a fifth step of temporal integration of said fusion performed in the fourth step, using re-initializations of the estimated speed of the aircraft relative to the surrounding air, the estimated angle of incidence of the aircraft , and the estimated aircraft slip angle to output an estimated aircraft speed relative to the surrounding air, an estimated aircraft angle of incidence, and an estimated slip angle of the aircraft. Such a method makes it possible to obtain an estimate of the speed of an aircraft relative to the surrounding air without using the sensors measuring this speed. It explicitly uses the fact that the pressure measured by the static probe is distorted (of a known quantity) under the effect of the speed of the aircraft relative to the surrounding air.
    [0006] This method therefore provides an element of analytical redundancy that can be advantageously used for the operational safety of the aircraft. The presence of a quality indicator notably allows the user to dynamically manage the anomaly detection threshold when the speed previously estimated is used in a monitoring device of a sensor measuring the speed of the aircraft. The merging step makes it possible to combine two estimates of the components of the speed vector of the aircraft, each with independent errors: one is affected by the movement of the isobar, the other is affected by the turbulence of the wind. The result is a reduction of the error of the speed vector of the aircraft after fusion.
    [0007] According to one embodiment, said fourth melting step uses: a first substep of estimating said linear combination from local aerodynamic corrections, a feedback of the estimated speed of the aircraft with respect to the surrounding air, a feedback of the estimated incidence angle of the aircraft, and a feedback of the aircraft slip angle; and a second Kalman filter correction sub-step using as input said linear combination estimates provided by the second step and the first substep. Performing the fourth melting step by Kalman filtering provides more efficient temporal filtering than the least squared square In one embodiment, said resetting of the estimated static pressure of the first stage uses pressure measurements. static. These re-initializations of the estimated static pressure allow a long-term stabilization of the estimated static pressure is necessary to temper the natural divergence related to the integration effect, the errors of measurement of geographical velocity, and the effect of the movement of the isobaric.
    [0008] According to one embodiment, said re-initializations of the estimated speed of the aircraft relative to the surrounding air use measurements of the speed of the aircraft relative to the surrounding air. These re-initializations of the estimated speed of the aircraft relative to the surrounding air allow a long-term stabilization of the estimated TAS is necessary to temper the natural divergence related to the integration effect, and to the effect imperfection of the coefficients of the linear combination.
    [0009] The proposed method is based on a kinematically coupled estimation, but offering a much better tolerance to the movement of air relative to the ground by the introduction of the static pressure measurement. The following advantages are thus combined: advantage of analytical redundancy (with respect to physical redundancy): possibility of handling common modes of failure, and less complexity; advantage of the kinematic coupling estimation (with respect to the dynamic coupling): no need to know the characteristic data of the aircraft; - advantage of dynamic coupling estimation (compared to kinematic coupling): less sensitivity to wind movement relative to the ground. It is also proposed, according to another aspect of the invention, a system for estimating the speed of an aircraft relative to the surrounding air, the angle of incidence of the aircraft, and the the angle of wiggle of the aircraft, in a reference linked to the aircraft, adapted to implement the method as described above. According to another aspect of the invention, there is also provided an aircraft comprising a system as previously described. The invention will be better understood by studying a few embodiments described by way of non-limiting examples and illustrated by FIG. the accompanying drawings in which: - Figures 1 and 2 schematically illustrate a method according to one aspect of the invention. In all the figures, the elements having identical references are similar. FIG. 1 schematically illustrates a method for estimating the speed TASe of an aircraft relative to the surrounding air, in a reference frame linked to the aircraft according to one aspect of the invention. The method of estimating the speed of an aircraft relative to the surrounding air, the angle of incidence of the aircraft, and the angle of wander of the aircraft, in a reference frame linked to the aircraft, comprising: a first step 1 of estimating an estimated static pressure PSe from measurements of the aircraft's vertical vertical velocity Vzgeo, measurements of the surrounding air temperature, re-initializations of the aircraft; estimated static pressure PSe, and a feedback of said estimated static pressure PSe, by integration on a vertical path of a hydrostatic equation; a second step 2 consisting in estimating a linear combination of a first intermediate variation 6TASa of the speed of the aircraft relative to the surrounding air, a first intermediate variation 6AOAa of the angle of incidence of the aircraft and a first intermediate variation δSSAa of the aircraft skid angle from: said estimated static pressure PSe and a measurement of the static pressure PSm; a difference between a variation ÔPSe of the estimated static pressure and a variation ÔPSm of the measured static pressure; and aerodynamic local corrections SSEC dependent on the aircraft; and also comprising estimating a first quality indicator IndQa of said linear combination estimated by the second step 2; a third step 3 of estimating a second intermediate variation ÔTASb of the speed of the aircraft relative to the surrounding air, a second intermediate variation 6AOAb of the angle of incidence of the aircraft and a second intermediate variation ÔSSAb of the angle of wander of the aircraft, from inertial measurements, a feedback of the estimated speed TASe of the aircraft relative to the surrounding air output, a feedback of the angle of incidence estimated AOAe of the aircraft at the exit, and a feedback of the estimated skid angle SSAe of the aircraft at the output, by assimilation of the velocity vector of the aircraft relative to the surrounding air to a velocity vector inertial with respect to an inertial reference obtained by integrating the components of a measured acceleration vector Inertial measurements, corrected for the kinematic effect of the reference linked to the aircraft and increased the projected gravity in the bound reference frame to the aircraft; and estimating a second quality indicator IndQb of said second intermediate variations ÔTASb, ÔAOAb, ÔSSAb; a fourth step 4 of melting said second intermediate variation ÔTASb of the speed of the aircraft relative to the surrounding air, second intermediate variation ÔAOAb of the angle of incidence of the aircraft, and second intermediate variation ÔSSAb of the angle of wander of the aircraft respectively with said first intermediate variation ÔTASa of the speed of the aircraft with respect to the surrounding air, first intermediate variation ÔAOAb of the angle of incidence of the aircraft, and first intermediate variation 6SSAb of the aircraft skid angle, by barycentre type filtering weighted by said first and second IndQa, IndQb quality indicators or by Kalman type filtering; and merging said first and second IndQa, IndQb quality indicators into an IndQ merged quality indicator from the error variance estimated by said filtering; and a fifth step of temporal integration of said fusion performed in said step 4, using re-initializations of the estimated speed of the aircraft relative to the surrounding air, the estimated angle of incidence of the the aircraft, and the estimated SSA aircraft skid angle for outputting an estimated aircraft TASe velocity relative to the surrounding air, an estimated aircraft AOAe incidence angle, and a estimated skid angle SSAe of the aircraft. The first step of estimating an estimated static pressure PSe can be performed as follows. The static pressure at a given point is obtained by integrating (along the vertical path) the classical hydrostatic equation: dPSe = -pgdzgeo = RPTSe .g.dz geo dPSe = -PS dz e R- Ge ttair with R the constant of the air (R = 287 m2 / K.s2), and zgeo the geographical altitude. The integration of the differential equation requires an initial value of the pressure and the knowledge of the temperature along the vertical path. The baro-standard altitude (ISA for "International Standard Atmosphere" in English) is thus based on a "mean" model of temperature (15 ° C at sea level, then linear decay at 6.5 ° C / km). The fact that the actual sea level temperature is different from 15 ° C and that the vertical temperature gradient below the current point is not strictly constant = 6.5 ° C / km will lead to an ISA static pressure different from the real static pressure. The equation above is only the first term of the general evolution equation of PSe: dPS = 813Se .dz + 11) Se .de + PSe.dt e az at The second term of the sum translates the PSe variation following the horizontal (without changing altitude), the third reflects the temporal variation of PSe (remaining in the same place). Keeping the first term we thus obtain the evolution equation of PSe: PSe = RT .g.Vzgeo.PS, + P (eq 1). The term P corresponds to the disturbance of the atmosphere which can not be easily modeled (terms in and dt). Vzgeo magnitude is the geographical speed measured by an inertial system or a GPS receiver. The air temperature is the air temperature measured by a temperature sensor.
    [0010] This gives the formulation of the observer of Ps in open loop: R Vzgeot'_ PSe (tn) - PSe (tn_1). [1- (tn-tn_1). R. 1) 1 (eq 2) Tair ((tn_1) In practice, the synthetic measurement provided by this open-loop observer will tend to move away from actual values because of 1) errors in both geographic velocity and temperature measurement. air, and 2) disturbances of the atmosphere (variation of the isobar relative to the geographical altitude). The first step 1 also uses re-initializations of the estimated static pressure PS e for example by periodic re-initializations by means of the static pressure from the static pressure measurement chain (gross pressure measured and corrected by the SSEC laws ) or by setting up an outer loop that uses this same measured and corrected static pressure to build a slow correction. The second step 2 consists in estimating a linear combination of a first intermediate variation δTASa of the speed of the aircraft relative to the surrounding air, a first intermediate variation 6AOAa of the angle of incidence of the aircraft and a first intermediate variation δSSAa of the skid angle of the aircraft can be performed as follows.
    [0011] The static pressure is the sum of the pressure PSm measured by the static probe and the SSEC correction of the effect of the speed (impact of the mach, the angle of incidence of the aircraft AOA, and the angle skid of the SSA aircraft).
    [0012] It is written that the speed of variation of the static pressure is the sum of the rate of variation of the pressure PSm and the speed of the correction SSEC: dPSe = -g.Vzgeo.PSe dPS ', dCor dt RT dt + dt dP ', + aCor dM aCor dAOA aCor dSSA dt aM - dt + 5A0A-dt + assAdt With: Cor representing the SSEC correction to be applied to the measured static pressure to obtain the true static pressure, in Pascal; M represents the Mach, adimensional One thus obtains a synthetic measurement of the variation of a linear combination of M, AOA, SSA: K m .811 / 1 + KA.M0A + K s.8SSA = g-Vzgeo-PS e-6t The coefficients KM, KA, Ks, which constitute the coefficients of the linear combination, are calculated from the SSEC correction laws of the aircraft, at the current point of the flight domain. In addition, since the speed of an aircraft relative to the surrounding air TAS is Mach-related by TAS = y .R.Ta, r .M (noting y ratio of mass heats of air to volume and constant pressure, R is the perfect gas constant, and Tais the air temperature) Equation 3 can be reformulated by introducing TAS rather than Mach M: -GYzeo-PS e-St KT .8TAS + KA .8A0A + K s .8SSA = 8PS ', = 8PS, - SPS,' (eq 4) RT with KT = Km I ily.R.Tair by noting 25 This gives an estimate of the variation of a linear combination of the speed of an aircraft relative to the surrounding air, the angle of incidence of the aircraft, and the angle of wander of the aircraft, which are three magnitudes completely defining the speed vector of the aircraft compared to the surrounding air. The performance gain depends on the amplitude of the coefficients KT, KA, and K.
    [0013] These coefficients KT, KA, Ks are calculated from the SSEC correction laws, which themselves depend on the mounting topology of the probes on the aircraft and the current point of the flight envelope. When SSEC corrections are known as polynomials, the calculation of the coefficients consists of a simple derivation. When corrections are known as tabs, they should be reformulated in polynomial form by applying an adjustment method. The quality of the estimate is all the more effective when the coefficients are large, ie the mounting topology is such that the velocity vector Vair of the aircraft relative to the surrounding air significantly distorts the measurement PSm produced by the static probe. The second step 2 also estimates the first indQa quality indicator of said linear combination (KT.5TAS + KA.8A0A + Ks.ÔSSA) estimated by the second step 2, depending on said coefficients of said linear combination KT, KA, KS, of the accuracy of the measurements involved in the calculation of variations of the speed of the aircraft relative to the surrounding air, (vertical vertical velocity Vzgeo, air temperature Tair, and the standard deviation of the speed of variation of the isobar itself depends on the horizontal and vertical speeds of the aircraft according to a modeling well known to those skilled in the art The third step 3 consists in estimating a second intermediate variation ÔTASb of the speed of the aircraft relative to the surrounding air, a second intermediate variation δA0Ab of the angle of incidence of the aircraft and a second intermediate variation 6SSAb of the aircraft skid angle, from inertial measurements. es, a feedback of the estimated speed TASe of the aircraft relative to the surrounding air output, a feedback of the estimated angle of incidence AOAe of the aircraft output, and a feedback of the estimated wander angle SSAe of the aircraft at the output, by assimilation of the velocity vector of the aircraft with respect to the surrounding air to an inertial velocity vector with respect to an inertial reference obtained by integration of the components of a measured acceleration vector Inertial measurements, corrected for the kinematic effect of the reference linked to the aircraft and increased by the projected gravity by assimilating the velocity vector of the aircraft relative to the surrounding air to an inertial velocity vector relative to to an inertial reference obtained by integrating the components of a measured acceleration vector Inertial measurements, corrected for the kinematic effect of the reference linked to the aircraft and increased by the projected gravity in the reference linked to the aircraft; and estimating a second quality indicator IndQb of said second intermediate variations ÔTASb, ÔAOAb, ÔSSAb. The fourth step 4 consists in fusing said second intermediate variation ÔTASb of the speed of the aircraft relative to the surrounding air with said first intermediate variation ÔTASa of the speed of the aircraft relative to the surrounding air by filtering of the type. center weighted by said first and second IndQa, IndQb or Kalman-type quality indicators, and merging said first and second IndQa, IndQb quality indicators into an IndQ merged quality indicator from the estimated error variance by the filtering operation. Then, the fifth step 5 consists in achieving a temporal integration of the fusion performed in the fourth step 4, by using resets of the estimated speed of the aircraft relative to the surrounding air, the angle of estimated aircraft impact, and estimated aircraft slip angle to output an estimated aircraft TASe velocity relative to the surrounding air, an estimated aircraft AOAe incidence angle , and an estimated SSAe aircraft slip angle.35 The estimated aircraft TASe velocity relative to the surrounding air, the estimated aircraft AOAe incidence angle, and the estimated skid angle SSAe of the aircraft, can be used again in the second step 2 to intervene in the calculation of the coefficients KT, KA, and KS since they depend on the current point of the flight domain. The third step 3 also uses re-initializations of the estimated speed TASe of the aircraft relative to the surrounding air, the estimated angle of incidence AOAe of the aircraft, and the estimated slip angle SSAe. of the aircraft, for example by periodic re-initializations by means of real measurements or by setting up an outer loop which uses real measurements to construct a slow correction (slow compared to the rapid correction resulting from the estimator static pressure). The actual measurements mentioned are those resulting from the chain of real measurements of the speed of the aircraft relative to the surrounding air, ie the chains of measurement of the speed TAS of an aircraft relative to the surrounding air, of the angle of incidence AOA of the aircraft, and the angle of wander SSA of the aircraft The third step 3 of estimating a second intermediate variation ÔTASb of the speed of the aircraft relative to the surrounding air, a second intermediate variation ÔAOAb of the angle of incidence of the aircraft, and a second intermediate variation ÔSSAb of the skid angle of the aircraft can be performed as follows.
    [0014] The vector Vair velocity of the aircraft relative to the surrounding air, can be expressed either in the form of three Cartesian coordinates Vxair, Vyair, and Vzair in the reference linked to the aircraft, either in the form of three spherical coordinates TAS, AOA, SSA in the reference linked to the aircraft.
    [0015] The relationship between these two forms of representation is: Vx air TAS .cos AOA. cos SSA Vair = VYair TAS. sin SSA (eq5) Vzair TAS. sin A0A.cosSSA We now consider the equation of propagation of the inertial velocity vector of the aircraft expressed in the reference linked to the aircraft. The derivative of this vector with respect to time is equal to the acceleration measured by the accelerometers, corrected for the kinematic effect of the reference linked to the aircraft and increased by the gravity projected in the reference linked to the aircraft: + AccIR + Clpg (eq 6) in which AccIR represents the inertial acceleration vector, Ç2IR represents the matrix of the pqr the pqr being the three components of the inertial rotation speed vector), and CIR represents the projection coefficients of the vertical in the bound coordinate system to the aircraft: 0 -r - sin 0 - 1-2 IR-0 cos O. sin ço cos O. cos q, noting 0 the roll and cp the -qp 0 pitch. By neglecting the acceleration of the wind, this same equation is used for the speed of the aircraft with respect to the air: -S2 .V ACCIR CIR .g (eq 7) 25 with Vair = Vxair and Vair = Vxair VYair VYair Vzair _Vzair _ In equation 7 we replace the three coordinates of Vair by their expression according to TAS, AOA, and SSA, and we replace the three coordinates of the derivative of Vair by their expression according to the derivatives of TAS, AOA, and SSA.
    [0016] We thus obtain three linear equations in AS; TOA, A.SA, whose coefficients depend on the quantities TAS, A0A, SSA, and whose term on the right is a function of the inertial measurements and quantities TAS, AOA, SSA The term on the right is tainted with an error, which is homogeneous with an error of acceleration, and which represents the unknown acceleration of the wind and the error of acceleration induced by the imprecision of the inertial measurements. The third step 3 also estimates the second quality indicator IndQb of said second intermediate variation ÔTASb of the speed of the aircraft relative to the surrounding air, the second intermediate variation ÔAOAb of the angle of incidence of the aircraft , and the second intermediate variation ÔSSAb of the skid angle of the aircraft. This second quality indicator IndQb depends on the precision of the measurements involved in the calculation of the second intermediate variations ÔTASb, bA0Ab, and ÔSSAb (inertial measurements) and the standard deviation of the wind acceleration provided by a well-known modeling of the skilled person. The fourth melting step 4 can be performed by combining these independent sources of estimation of a linear combination (KT.BTASa + KA.82104 + K s .8SSAa) of the first intermediate variations (ÔTASa, 6A0Aa, ÔSSAa) and second intermediate variations (6TASb, ÔAOAb, ÔSSAb) in a variation of the speed of the aircraft relative to the merged ambient air ÔTAS, a variation of the angle of incidence of the merged aircraft ÔAOA, and a variation of the the skid angle of the fused aircraft OSSA, for example by a weighted least squares applied to the 4 equations linking the 3 unknowns ÔTAS, ÔAOA, ÔSSA, the weighting coefficients then being deduced from the standard deviations of error IndQa and IndQb.
    [0017] It is in this case, the variation of the speed of the aircraft relative to the surrounding air merged ÔTAS, the variation of the angle of incidence of the fused aircraft ÔAOA, and the variation of the angle of skid of the fused aircraft ÔSSA, which are temporally integrated by the fifth step 5. Of course, alternatively, it is possible to perform the fifth step of time integration before the fourth melting step 4, in which case the merger is carried out on the estimation of the speed of the aircraft relative to the surrounding air and not its variations. Similarly, other fusion techniques can be used to couple the two estimates, such as the Kalman filter, as shown in FIG. 2. In this case, the fourth step can be decomposed into a first sub-step 4a of calculation of said linear combination from the 3 components of the speed vector of the aircraft with respect to the air resulting from the fifth step, and coefficients calculated from the local aerodynamic correction laws and a second substep 4b consisting of calculating an observation equal to the difference between the linear combination resulting from the second step and that resulting from the first substep of the fourth step, then using this observation to correct by Kalman filtering the components of the aircraft speed vector relative to the air. More precisely, we consider the state vector X consisting of the three components Vx, Vy, Vz of the air / air speed vector and their three derivatives ex, G, G. The propagation of the six components of this state vector is deduced from Equation 7, and is written: Vair (t ,,) = -SIIR.V ''. (t'_1) + ACCIR + CIR.g Vair (ta = Vair (t ,, 1) + eair (t'_1) .ATp noting ATP the duration of the propagation step. The error committed on this propagation has a known covariance matrix Q, partly from the characteristics of the sensors used, and partly from the known statistic of the wind turbulence profiles.The scalar measurement ZZ = gYzGEo.PSe 8t 8PS is considered, noting ATR the duration of the step of RT This measurement is obtained by integrating over the duration ATR the quantity under the integral sign and by subtracting ÔPSm which is the variation 10 of the measurement PSm over the same duration ATR.The measurement Z has a variance of error R, partly based on the characteristics of the sensors used, and partly on the basis of the known statistics of the isobaric variation profiles, According to equation 4 this measurement is also equal to KT. ATAS + KA .AA0A + KB .ASSA noting ATAS, AAOA, ASSA the variation of TAS, AOA, SSA over time ATR 20 It is therefore possible to calculate the observation matrix H which quantifies how a small variation of the state X modifies the quantity KT .ATAS + KA.AA0A + KB .ASSA At each propagation period one calculates propagation matrix A and propagates the state vector X and its covariance matrix P. At each resetting time, an observation Y is calculated equal to the difference between the measurement calculated from the filter state X and Z measurement. The observation matrix H is calculated, and the X state and the P covariance are recalculated using the well known formulation of the extended Kalman filter.
    [0018] ATR registration.
    [0019] This gives the three components Vx, Vy, Vz of the air / air speed vector and the associated covariance matrix. It is easily known to re-express these quantities in the form of the three components TAS, AOA and SSA of the air / air speed vector and the associated covariance matrix. The fusion of data from inertia and data from static pressure significantly improves the accuracy of the estimation of the speed vector of the aircraft relative to the surrounding air, but does not make it possible to stabilize this estimate well. on the long term. Indeed, the use of the PSm static pressure measurement directly improves the estimation of the acceleration of the aircraft relative to the air. The improvement in speed estimation is only a consequence of improving the estimation of the acceleration. The temporal integration of the acceleration to obtain the speed can thus diverge in the long term. Stabilization over the long term can be achieved by periodic resets or the establishment of an outer loop as explained above in the detail of the fifth step by implementing well-known long-term stabilization techniques. . With periodic re-initialization, there is a blind zone at the time of re-initialization: if the failure of the actual measurement chain (producing a measured speed of the aircraft relative to the surrounding air 25 TASm) just intervenes before the re-initialization moment, then the speed of the aircraft relative to the surrounding air TAS estimated TASe is distorted and does not detect the failure. To counter this blind zone, two separate estimates can be used, whose re-initialization times are temporally offset (by half of the re-initialization period). The outer loop is based on a correction calculated from the difference between the estimated speed of the aircraft relative to the surrounding air TASe and the measured speed of the aircraft relative to the surrounding air TASm produced by the aircraft. the actual chain of measurement of the speed of the aircraft relative to the air. This correction is then applied in the production line of the estimated speed of the aircraft relative to the surrounding air TASe to follow in the long run the measured speed of the aircraft relative to the surrounding air TASm. The correction is designed to stabilize the estimate of the estimated aircraft speed relative to the surrounding air TASe over the long term while ensuring a delay in the absorption of a measured speed failure of the aircraft. aircraft relative to the surrounding air TASm. Thus, by comparing the estimated speed TASe and the measured speed TASm it is possible to detect a failure of the measured speed TASm provided that this failure develops over a sufficiently short time. The periodic re-initialization technique can thus be seen as a particular embodiment of the outer loop technique: the correction is reduced to the sampled identity function (the correction is equal to the estimate). In both cases, the estimate, coupled with the long-term stabilization, is characterized by its ability to detect a failure of the actual measurement chain of the speed of the aircraft relative to the surrounding air, this failure being characterized by an AMIN minimum amplitude and a TMAX maximum settling time. A fault whose amplitude is greater than AMIN and whose set-up time is less than TMAX is detected almost for sure. Failure of smaller amplitude and / or longer setup time will likely be undetected. The use of "inertia / static pressure" fusion reduces AMIN and increases TMAX, which improves the ability to detect a failure in the actual measurement chain of the aircraft speed relative to Surrounding air The use of estimates for sensor failure detection and isolation purposes is well known in the state of the art. It usually uses two groups of sensors A and B, both groups being assumed to be independent of failures: the occurrence of a failure in group A is independent of the occurrence of a failure in the group. group B (or, at least, the risk of such dependence is low). The measurements provided by the sensors A are deemed reliable and used to calculate Best estimates, which are representative of the measurements produced by the sensors B. This gives an analytical redundancy. By comparing the estimates B and the actual measurements B, it is possible to detect a fault, and to isolate the faulty sensor (s) from the group B while ensuring continuity of operation.
    [0020] Depending on the operational constraints, the comparison may be permanent (with a risk of increased false alarm) or triggered on an event (for example on the detection of an inconsistency between sensors of group B) with an increased risk of detection missed . The combination of physical redundancy (measurement) and analytical redundancy (estimation) thus offers multiple possibilities in the architecture of fault detection and isolation systems. When estimating the speed of the aircraft relative to the surrounding air by inertia / static pressure melting described in this application, the group of sensors A is the following: - inertial sensor vertical vertical velocity sensor - static pressure sensor air temperature sensor 30 The inertial sensor is typically an IRS (acronym for "Inertial reference system" in English) or an AHRS (acronym for "Attitude and heading reference system" in English) and provides the following measurements: three acceleration components (AccX, AccY, AccZ), three rotational speed components (p, q, r), two roll angles and pitch. We also have the knowledge of gravity g at the current point, via an adequate gravity model.
    [0021] The vertical vertical velocity measurement (vertical speed of the aircraft relative to the Earth) is typically derived from a GPS receiver, or any radio or optical system for measuring the geographical altitude with respect to the Earth. It can also be obtained from inertial measurements, provided that it takes into account the fact that the inertial vertical velocity is divergent. In all cases, this vertical vertical velocity measurement must be independent of the SSEC corrected static pressure. The group of sensors B comprises the anemometric sensors 15 measuring the three components of the velocity vector (Vair (TAS, AOA, SSA)). For some applications, the sensor group B can be reduced to two or one of the three components of the velocity vector.
    [0022] For example: only the sensors measuring the speed of the aircraft relative to the surrounding air TAS and the angle of incidence of the aircraft AOA are in group B because the topology of the mounting of the probes on the aircraft creates a weak coupling between the static pressure and the skid angle of the SSA aircraft. Thus, the measurement probe of the SSA 25 skid angle does not intervene in the estimate, neither in A nor in B. In another example, the sensors measuring the angle of incidence of the aircraft AOA and the SSA aircraft skid angle are assumed to be reliable and are in group A, only the source of the speed of the aircraft 30 relative to the surrounding air TAS is in group B.
    权利要求:
    Claims (6)
    [0001]
    REVENDICATIONS1. A method for estimating the speed of an aircraft relative to the surrounding air, the angle of incidence of the aircraft, and the angle of wander of the aircraft, in a reference frame linked to the aircraft, comprising: a first step (1) of estimating an estimated static pressure (PSe) from measurements of the geographical vertical speed (Vzgeo) of the aircraft, measurements of the temperature (Tair) of the surrounding air , re-initializations of the estimated static pressure (PSe), and a feedback of said estimated static pressure (PSe), by integration on a vertical path of a hydrostatic equation; a second step (2) of estimating a linear combination of a first intermediate variation (6TASa) of the speed of the aircraft relative to the surrounding air, a first intermediate variation ($ 5A0Aa) of the angle of the aircraft and a first intermediate variation (ÔSSAa) of the aircraft skid angle from a difference between a variation (6PSe) of an estimated static pressure (PSe) and a variation (6PSm) of a measured static pressure (PSm), said linear combination using coefficients calculated from the aircraft dependent aerodynamic localization laws (SSEC), the speed (TAS) of the aircraft relative to to the surrounding air, the aircraft angle of attack (AOA), and the aircraft skid angle (SSA), and also to estimate a first quality indicator (IndQa) of said linear combination estimated by the second step (2); a third step (3) of estimating a second intermediate variation (ÔTASb) of the speed of the aircraft relative to the surrounding air, a second intermediate variation (ÔAOAb) of the angle of incidence of the aircraft and a second intermediate variation (EISSAb) of the aircraft skid angle, based on inertial measurements, and a feedback of the estimated speed (TASe) of the aircraft relative to the surrounding air output, a feedback of the estimated angle of incidence (AOAe) of the output aircraft, and an estimate of the estimated slip angle (SSAe) of the output aircraft, by assimilation of the aircraft speed vector. the aircraft relative to the surrounding air at a speed vector of the aircraft with respect to an inertial reference obtained by integrating the components of a measured acceleration vector (inertial measurements), corrected for the kinematic effect of the reference linked to the aircraft and increased the projected gravity in the reference linked to the aircraft; and estimating a second quality indicator (IndQb) of said second intermediate variations ($ 5TASb, 6AOAb, ÔSSAb); a fourth step (4) for melting said second intermediate variation ($ 5TASb) of the speed of the aircraft relative to the surrounding air, second intermediate variation (6AOAb) of the angle of incidence of the aircraft, and second intermediate variation (6SSAb) of the angle of wander of the aircraft respectively with said first intermediate variation (6TASa) of the speed of the aircraft relative to the surrounding air, first intermediate variation (6AOAb) of the angle of incidence of the aircraft, and first intermediate variation (15SSAb) of the aircraft skid angle, by least square filtering weighted by said first and second quality indicators (IndQa, IndQb) or by a filtering Kalman type; and merging said first and second quality indicators (IndQa, IndQb) into a merged quality indicator (IndQ) from the error variance estimated by said filtering; and a fifth step (5) of temporal integration of said fusion performed in the fourth step (4), using re-initializations of the estimated speed of the aircraft relative to the surrounding air, the angle of the estimated aircraft impact, and the estimated aircraft slip angle to output an estimated aircraft airspeed (TASe) relative to the surrounding air, an estimated angle of incidence (AOAe) of the aircraft, and an estimated skid angle (SSAe) of the aircraft.
    [0002]
    The method of claim 1, wherein said fourth merging step (4) uses: a first substep (4a) of computing said linear combination using coefficients calculated from the dependent local aerodynamic correction laws (SSEC) of the aircraft, the speed (TAS) of the aircraft relative to the surrounding air, the aircraft angle of attack (AOA), and the aircraft slip angle ( SSA); and a second Kalman filter correction sub-step (4b) using as input said linear combination estimates provided by the second step (2) and said first substep (4a).
    [0003]
    The method of claim 1 or 2, wherein said re-initializations of the estimated static pressure (PSe) of the first step (1), utilize static pressure measurements corrected by the local aerodynamic correction (SSEC) laws. .
    [0004]
    4. Method according to one of claims 1 to 3, wherein said re-initializations of the estimated speed (TASe) of the aircraft relative to the surrounding air, the estimated angle of incidence (AOAe) of the aircraft and the aircraft's estimated wander angle (SSAe) respectively use measurements of the aircraft speed relative to the surrounding air corrected by the local aerodynamic correction laws (SSEC), measurements Aircraft angle of incidence corrected by local aerodynamic correction (CSST) laws, and aircraft angle of deflection measurements corrected by local aerodynamic correction laws (SSEC).
    [0005]
    5. An aircraft's speed estimation system (TASe) with respect to the surrounding air, aircraft angle of attack (AOA), and aircraft skid angle (SSA), in a reference linked to the aircraft, adapted to implement the method according to one of the preceding claims.
    [0006]
    Aircraft comprising a system according to claim 5.
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    同族专利:
    公开号 | 公开日
    BR102015017185A8|2016-04-26|
    BR102015017185A2|2016-01-19|
    CA2897699A1|2016-01-18|
    US20160325845A1|2016-11-10|
    FR3023918B1|2017-12-29|
    US9828111B2|2017-11-28|
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    US20080066540A1|2006-09-14|2008-03-20|The Boeing Company|Methods and systems for calculating atmospheric vehicle air data|
    US20100100260A1|2008-10-21|2010-04-22|Mcintyre Melville Duncan Walter|Alternative method to determine the air mass state of an aircraft and to validate and augment the primary method|
    EP2568295A1|2011-09-09|2013-03-13|Airbus Opérations SAS|Method and apparatus for the automatic estimation of airspeed of an aircraft|
    FR2978829B1|2011-08-04|2014-03-21|Aer|VELOCIMETRE INSENSIBLE TO GIVING CONDITIONS AND TO HEAVY RAIN|
    FR2988480B1|2012-03-21|2014-05-09|Airbus Operations Sas|IMPACT PROBE BLOCK DETECTION SYSTEM FOR AN AIRCRAFT.|US10296013B2|2016-05-16|2019-05-21|Booz Allen Hamilton Inc.|Vehicle guidance system and method that uses air data from surface-mounted pressure sensors for vehicle orientation control|
    CN106871892B|2017-02-17|2020-08-11|张梦|Aircraft combined navigation method and device|
    US11066189B2|2018-12-07|2021-07-20|The Boeing Company|Flight control system for determining estimated dynamic pressure based on lift and drag coefficients|
    US11029706B2|2018-12-07|2021-06-08|The Boeing Company|Flight control system for determining a fault based on error between a measured and an estimated angle of attack|
    US11003196B2|2018-12-07|2021-05-11|The Boeing Company|Flight control system for determining a common mode pneumatic fault|
    法律状态:
    2015-06-29| PLFP| Fee payment|Year of fee payment: 2 |
    2016-01-22| PLSC| Publication of the preliminary search report|Effective date: 20160122 |
    2016-06-28| PLFP| Fee payment|Year of fee payment: 3 |
    2017-06-28| PLFP| Fee payment|Year of fee payment: 4 |
    2018-06-28| PLFP| Fee payment|Year of fee payment: 5 |
    2020-06-25| PLFP| Fee payment|Year of fee payment: 7 |
    2021-06-24| PLFP| Fee payment|Year of fee payment: 8 |
    优先权:
    申请号 | 申请日 | 专利标题
    FR1401620A|FR3023918B1|2014-07-18|2014-07-18|METHOD OF ESTIMATING THE SPEED OF AN AIRCRAFT IN RELATION TO THE SURROUNDING AIR, AND ASSOCIATED SYSTEM|FR1401620A| FR3023918B1|2014-07-18|2014-07-18|METHOD OF ESTIMATING THE SPEED OF AN AIRCRAFT IN RELATION TO THE SURROUNDING AIR, AND ASSOCIATED SYSTEM|
    US14/797,015| US9828111B2|2014-07-18|2015-07-10|Method of estimation of the speed of an aircraft relative to the surrounding air, and associated system|
    CA2897699A| CA2897699A1|2014-07-18|2015-07-16|Method of estimation of the speed of an aircraft relative to the surrounding air, and associated system|
    BR102015017185A| BR102015017185A8|2014-07-18|2015-07-17|method of estimating the speed of an aircraft relative to the surrounding air, and associated system|
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