Robust atmosphere data fusion method based on fault adaptive detection removal strategy

Abstract

The invention belongs to the technical field of aerospace, and particularly relates to a robust atmospheric data fusion method based on a fault adaptive detection removal strategy. According to a traditional scheme, data processing modes such as a voting system, an averaging system and a measuring (main sensor)-monitoring (auxiliary sensor) system are adopted for redundant sensors, and the situation that the sensors in an ADS system fail cannot be well processed. Therefore, by introducing a mechanism capable of automatically realizing sensor fault detection and removal under the condition of non-manual intervention, an atmosphere data estimation algorithm with robustness to the sensor fault is designed. A simulation result verifies the feasibility of the provided robust atmospheric data estimation scheme in a real flight environment.

Description

Technical Field

[0001] The invention belongs to the field of aerospace technology, and is specifically applicable to a redundant atmospheric data system of an aircraft, and involves a robust atmospheric data fusion method based on a fault adaptive detection-removal strategy. Background Art

[0002] Since the advent of aircraft, flight safety has always been the most basic requirement. Whether it is automatic or manned, atmospheric data such as angle of attack, sideslip angle and airspeed affect the flight control and guidance of the aircraft, and are crucial to the safe flight of the aircraft. Atmospheric data is usually measured and obtained by the atmospheric data sensor (ADS) system equipped on the aircraft. Since the ADS system is directly exposed to the flight environment, it is easy to fail due to freezing, water accumulation, etc. in some harsh meteorological environments, thereby providing unreasonable atmospheric data, which poses a huge challenge to the flight safety of the aircraft. How to provide continuous and reliable atmospheric data in a complex environment to ensure flight safety is the unremitting goal of the aviation industry. In order to reduce the hidden dangers caused by the failure of the ADS system to the flight safety of the aircraft, engineers equip the ADS system of the aircraft with multiple sets of the same type of sensors based on the concept of redundant design. However, the common data processing methods for redundant sensors, such as "voting system", averaging, measurement (main sensor)-monitoring (secondary sensor) system, etc., cannot handle the situation where the sensor in the ADS system fails well. Summary of the invention

[0003] In view of the above problems, the present invention adds an atmospheric data sensor fault adaptive detection-removal module to the traditional ADS system, realizes self-detection and self-diagnosis of redundant atmospheric data sensors, and information fusion of redundant atmospheric data sensor measurement values, greatly improves the reliability of the ADS system and the accuracy of output atmospheric data, which is of vital importance to the guarantee of safe flight.

[0004] The purpose of the present invention is to design a robust atmospheric data estimation algorithm suitable for redundant ADS systems when there are faulty sensors, so as to achieve accurate atmospheric data estimation with low computational complexity. Specifically, with the help of the solution ideas and algorithms for classification problems in the field of artificial intelligence, a novel, simple and direct atmospheric data estimation idea is proposed for redundant ADS systems of aircraft, which realizes the fault self-detection of atmospheric sensors to meet the requirements of aircraft for reliable and continuous estimation of atmospheric data.

[0005] The technical solution of the present invention is:

[0006] An atmospheric data fusion method based on a fault adaptive detection and removal strategy for a redundant atmospheric data system comprises:

[0007] The fault-resistant atmospheric data estimation model is established as:

[0008] y t =Hx t +w t

[0009] in, Contains all measurements of the aircraft dynamic system, It is made by H 1 and H 2 The measurement matrix composed of t is the noise vector composed of all sensor noises, y 1t It is obtained from the measurement value of the aircraft inertial navigation system:

[0010] y 1t =H 1 x t +w 1t

[0011]

[0012] where x t is the aircraft system status, including atmospheric data and Euler angles, w 1t is the inertial navigation system sensor noise; y 2t It is obtained by measuring the value of the aircraft's atmospheric data sensor. The atmospheric data sensor is defined as K 1 airspeed sensor, K 2 Angle of attack sensor, K 3 There are two sideslip angle sensors, and the measurement equation is:

[0013]

[0014] in, For the kth 1 The airspeed sensor measurement value, H as is the airspeed sensor measurement matrix, kth 1 The airspeed sensor measures noise, For the kth 1 Angle of attack sensor measurement value, H aoa is the angle of attack sensor measurement matrix, k 1 The angle of attack sensor measures the noise, For the kth 1 The measured value of the sideslip angle sensor, H ssa is the sideslip angle sensor measurement matrix, For the kth 1 The sideslip angle sensor measures noise, defined as We can get:

[0015] y 2t =H 2 x t +w 2t

[0016] Among them, y 2t ∈R K Contains all sensor measurements in the redundant ADS system, K = K 1 +K 2 +K 3 , H 2 It is a measurement matrix formed according to the sources of different components;

[0017] Based on the established estimation model, the estimated value of the atmospheric data is obtained through the fault adaptive detection and removal strategy, which is as follows:

[0018] The sensor observation value y t The i-th component introduces a binary indicator variable z it To characterize its properties, if y it is a valid observation value, then z it =1, otherwise y it is an outlier, then z it =0, that is:

[0019]

[0020] Among them, h i is the i-th row of the measurement matrix H, when y it is the effective observation value, i.e. z it =1, y it The likelihood function is σ ii,t is the diagonal matrix R t The i-th main diagonal element of it = 0, y it The likelihood function value of is a constant, so y t The likelihood function is unified as:

[0021]

[0022] Where m is the index number of the sensor.

[0023] The indicator variable z it Modeled as a random variable following a Beta-Bernoulli distribution:

[0024]

[0025] Among them, Beta(·,·) represents the beta function, e it and f itis the parameter of the Beta-Bernoulli distribution;

[0026] definition and π t ={π it}, the unknown quantity to be determined is obtained through the following maximum a posteriori probability estimation problem:

[0027]

[0028] The variational Bayesian inference method is used to approximate the solution:

[0029] p(x t ,z t ,π t |y 1:t )≈q(x t ,z t ,π t )=q(x t )q(z t )q(π t )

[0030] According to the VBI theory, the distribution of different factors is solved through the following sequential iterative process:

[0031]

[0032] Where k is the iteration index of the VBI inference process, is the total probability density function, q k (x t ) is solved by the solution of Kalman smoothing theory, q k (z t ) and q k (π t ) Because of the relationship between the conjugate prior distribution, there is a closed solution; after a certain iterative process, the VBI process converges, and at this time q k (x t ) is approximately Gaussian distribution, and its expectation is used as the estimated value of the state, that is,

[0033]

[0034] According to the definition of system state, x t Contains atmospheric data and Euler angles to obtain an estimate of the atmospheric data.

[0035] The beneficial effect of the present invention is that the atmospheric data estimation method proposed in the present invention can estimate redundant atmospheric data with relatively low computational complexity, thus providing guarantee for the safe flight of the aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1The measured and estimated values ​​of the angle of attack in situation 1 (S1 angle of attack sensor is stuck);

[0037] Figure 2 are the measured and estimated values ​​of the angle of attack in situation 2 (the stuck time of the S1 and S2 angle of attack sensors do not overlap);

[0038] Figure 3 These are the measured and estimated values ​​of the angle of attack in situation three (the stuck time of the S1 and S2 angle of attack sensors overlaps). DETAILED DESCRIPTION

[0039] The present invention is described in detail below with reference to the accompanying drawings and simulation examples to demonstrate the practicability of the present invention.

[0040] The aircraft system kinematic model corresponding to the aircraft VADS can be described by the following differential equations:

[0041]

[0042] Among them, the system state x t =(V t ,α t ,β t ,φ t ,θ t ,ψ t ) T It can be mainly divided into two categories, namely, airspeed V t , angle of attack α t , sideslip angle β t Atmospheric data including the roll angle φ t , pitch angle θ t and the yaw angle ψ t The system input is the Euler angle Including three-axis acceleration Roll angular rate p t , pitch angle rate q t and the yaw rate r t , f(·)=(f 1 ,f 2 ,f 3 ,f 4 ,f 5 ,f 6 ) T It is a nonlinear function as follows

[0043]

[0044] f 4 =p t +q t sinφ t tanθ t +rt cosφ t tanθ t

[0045] f 5 =q t cosφ t -r t sinφ t

[0046]

[0047] The system input can be directly measured by the IMU sensor equipped in the aircraft, that is,

[0048] u tm =u t +v t

[0049] Among them, u tm is the reading of the IMU sensor, v t is the measurement noise of the IMU sensor. Substituting the IMU reading into the system input, the aircraft dynamics equation based on the IMU data can be obtained:

[0050]

[0051] Among them, the scaling matrix G(x t ) is defined as

[0052]

[0053] Considering that the measurement values ​​of sensors in the ADS system are discrete, it is necessary to discretize the aircraft dynamics model described by stochastic differential equations. Common discretization methods include the Euler method, that is,

[0054] x t+1 =x t +f(x t ,u tm )△t+G(x t )v t △t

[0055] Among them, △t is the discrete time interval, which is usually equal to the sampling time of the sensor in the ADS system.

[0056] The measurements of the aircraft system can be divided into two categories, namely those provided by the inertial navigation system (φ tm ,θ tm ,ψ tm ) and (V tm ,α tm ,β tmThe Euler angle measurement value provided by the inertial navigation system is the result of integrating information from various sensors such as gyroscopes, accelerometers and magnetic sensors. In the present invention, it is assumed that the inertial navigation system that provides such measurement values ​​has no faults. Then there is

[0057] y 1t =H 1 x t +w 1t

[0058] Among them, H 1 for

[0059]

[0060] According to the concept of redundant design, for each type of atmospheric data, the redundant ADS system is provided by several sensors of the same type. Therefore, assuming that the number of airspeed sensors, angle of attack sensors, and sideslip angle sensors are K and 1 , K 2 and K 3 , then the corresponding measurement equation can be written as

[0061]

[0062] For convenience, define The above formula can be written as

[0063] y 2t =H 2 x t +w 2t

[0064] Among them, y 2t ∈R K (K=K 1 +K 2 +K 3 ) contains all sensor measurements in the redundant ADS system, H 2 According to y 2t The measurement matrix formed by the sources of different components.

[0065] The unified measurement equation of the aircraft is obtained, namely

[0066] y t =Hx t +w t

[0067] in, Contains all measurements of the aircraft dynamic system, It is made by H 1 and H 2 The measurement matrix composed of tis the noise vector consisting of all sensor noises, usually assumed to be Gaussian distributed

[0068] In order to establish the mathematical model of the above sensor fault adaptive detection-removal mechanism, the present invention intends to introduce a binary indicator variable to the sensor to mark its attribute (i.e., whether a fault occurs), and use the corresponding probability distribution model to model the binary indicator variable to realize the adaptive mechanism. Specifically, the sensor observation value y t The i-th component introduces a binary indicator variable z it to characterize its properties. it is a valid observation value, then z it =1, otherwise, if y it is an outlier, then z it =0, that is

[0069]

[0070] Among them, h i is the i-th row of the measurement matrix H. it is the effective observation value, i.e. z it =1, y it The likelihood function can be written as σ ii,t is the diagonal matrix R t The i-th main diagonal element of ; and when y it is an outlier value, i.e. z it = 0, y it The likelihood function value of should be a constant. Combining the above two points, y t The likelihood function can be unified as

[0071]

[0072] It should be noted that the measured value y t It also includes the Euler angle provided by the navigation system. In the present invention, it is assumed that the navigation system has no faults, so for the measured value y t The Euler angle measurement value in , let the corresponding z it =1.

[0073] In the unified form of the likelihood probability density function given by it Different values ​​are taken to implement the sensor fault detection-removal strategy. However, the indicator variable z in the model it It still needs to be set manually. In order to realize the adaptive and autonomous execution of the sensor fault detection-removal strategy, the indicator variable z is further it Modeled as a random variable following a Beta-Bernoulli distribution:

[0074]

[0075] Among them, Beta(·,·) represents the beta function, e it and f it is the parameter of the Beta-Bernoulli distribution. By adjusting e it and f it The correct adjustment can be achieved.

[0076] Combining the above sensor fault adaptive detection-removal model with the SSM model for estimating atmospheric data, we can obtain an atmospheric data estimation model based on sensor fault adaptive detection-removal. and π t ={π it}, theoretically the unknown quantity can be obtained through the following maximum a posteriori probability estimation problem:

[0077]

[0078] However, the maximum a posteriori probability density is almost impossible to solve, so the variational Bayesian inference method (VBI) is used to approximate the solution. Specifically, the following mean field method is used to approximate the maximum a posteriori probability density, that is,

[0079] p(x t ,z t ,π t |y 1:t )≈q(x t ,z t ,π t )=q(x t )q(z t )q(π t )

[0080] According to the VBI theory, the distribution of different factors can be solved through the following sequential iterative process:

[0081]

[0082] Where k is the iteration index of the VBI inference process, is the total probability density function. k (x t ) can be solved with the help of Kalman smoothing theory solutions such as UKS and CKS, q k (z t ) and q k (π t) Because of the relationship between the conjugate prior distribution, there is a closed solution. After a certain number of iterations, the VBI process will converge. At this time, q k (x t ) is usually approximated to a Gaussian distribution, so its expectation can be used as an estimate of the state, that is,

[0083]

[0084] According to the definition of system state, x t Contains atmospheric data and Euler angles, and obtains the estimated value of atmospheric data.

[0085] Taking the longitudinal short-period linear system of a certain aircraft as an example, the feasibility of the proposed scheme of the present invention to provide an estimated angle of attack value when a redundant angle of attack sensor fails is verified. The discrete dynamic equation of the longitudinal short-period of a certain aircraft is:

[0086]

[0087] Among them, x t =[α t ,q t ] is the state of the system, including the angle of attack α t and the pitch rate q t ;u t =δ t is the input of the elevator. Considering the measurements provided by one pitch rate sensor and two angle of attack sensors of the aircraft, the measurement equation is

[0088]

[0089] Assume that the pitch rate sensor works normally, but the angle of attack sensor may fail. To this end, introduce binary random variables z into the two angle of attack sensors respectively. 1t and z 2t As an indicator of whether a fault has occurred, the estimated value of the angle of attack is calculated according to the scheme proposed by the present invention.

[0090] Three failure scenarios of the angle of attack sensors are considered. The first scenario is that one of the two angle of attack sensors fails while the other works normally. The measured value is Figure 1 (a) is shown; the second situation is that the two angle of attack sensors fail alternately, but at the same time one sensor can work normally, and the measured value is as follows Figure 2 (a) is shown; the third situation is that the two angle of attack sensors fail alternately, and both sensors fail at the same time for a period of time. The measured values ​​are as follows Figure 3(a) shows. Three schemes are used to obtain the estimated value of the angle of attack. First, the measurement value of a single sensor is directly used; second, the measurement values ​​of the two angle of attack sensors are processed without introducing the adaptive fault detection-removal process; third, the sensor fault adaptive detection-removal process is introduced to process the measurement values ​​of the two angle of attack sensors. In three different situations, the estimated results are shown in Figure 1 (b) Figure 2 (b) and Figure 3 (b) As shown in these results, it can be seen that the estimated value of the angle of attack can be close to the true value only when the sensor is not faulty or the sensor fault adaptive detection-removal process is adopted, which preliminarily proves the effectiveness and feasibility of the redundant ADS system robust atmospheric data estimation algorithm based on sensor fault adaptive detection-removal proposed by the present invention.

[0091] In summary, the present invention develops a robust atmospheric data estimation method that is resistant to sensor failures and is suitable for aircraft redundant ADS systems. By introducing a mechanism that can automatically detect and remove sensor failures without human intervention, an atmospheric data estimation algorithm that is robust to sensor failures is designed. Simulation results verify the feasibility of the proposed robust atmospheric data estimation scheme in a real flight environment.

Claims

1. An atmospheric data fusion method based on fault adaptive detection and removal strategy, characterized in that: include: The fault-resistant atmospheric data estimation model is established as: y t =Hx t +w t in, Contains all measurements of the aircraft dynamic system, is the measurement matrix composed of H1 and H2, w t is the noise vector composed of all sensor noises, y 1t It is obtained from the measurement value of the aircraft inertial navigation system: y 1t =H1x t +w 1t where x t is the aircraft system status, including atmospheric data and Euler angles, w 1t is the inertial navigation system sensor noise; y 2t It is obtained by the measurement value of the aircraft's atmospheric data sensor. The atmospheric data sensor is defined to include K1 airspeed sensor, K2 angle of attack sensor, and K3 sideslip angle sensor. The measurement equation is: in, is the k1th airspeed sensor measurement value, H as is the airspeed sensor measurement matrix, The k1th airspeed sensor measurement noise, is the measured value of the k1th angle of attack sensor, H aoa is the angle of attack sensor measurement matrix, is the k1 angle of attack sensor measurement noise, is the measured value of the k1th sideslip angle sensor, H ssa is the sideslip angle sensor measurement matrix, For the k1th sideslip angle sensor measurement noise, define We can get: y 2t =H2x t +w 2t Among them, y 2t ∈R K Contains all sensor measurements in the redundant ADS system, K = K1 + K2 + K3, H2 is the measurement matrix formed according to the sources of different components; Based on the established estimation model, the estimated value of the atmospheric data is obtained through the fault adaptive detection and removal strategy, which is as follows: The sensor observation value y t The i-th component introduces a binary indicator variable z it To characterize its properties, if y it is a valid observation value, then z it =1, otherwise y it is an outlier, then z it =0, that is: Among them, h i is the i-th row of the measurement matrix H, when y it is the effective observation value, i.e. z it =1, y it The likelihood function is σ ii,t is the diagonal matrix R t The i-th main diagonal element of it = 0, y it The likelihood function value of is a constant, so y t The likelihood function is unified as: Where m is the index number of the sensor; The indicator variable z it Modeled as a random variable following a Beta-Bernoulli distribution: Among them, Beta(·,·) represents the beta function, e it and f it is the parameter of the Beta-Bernoulli distribution; definition and π t ={π it }, the unknown quantity to be determined is obtained through the following maximum a posteriori probability estimation problem: The variational Bayesian inference method is used to approximate the solution: p(x t ,z t ,π t |y 1:t )≈q(x t ,z t ,π t )=q(x t )q(z t )q(π t ) According to the VBI theory, the distribution of different factors is solved through the following sequential iterative process: Where k is the iteration index of the VBI inference process, is the total probability density function, q k (x t ) is solved by the solution of Kalman smoothing theory, q k (z t ) and q k (π t ) Because of the relationship between conjugate prior distributions, there are closed solutions; After a certain number of iterations, the VBI process converges, and q k (x t ) is approximately Gaussian distribution, and its expectation is used as the estimated value of the state, that is, According to the definition of system state, x t Contains atmospheric data and Euler angles to obtain an estimate of the atmospheric data.

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