A method for estimating the airflow angle of civil aircraft considering acceleration correction

Abstract

This invention relates to a method for estimating the airflow angle of civil aircraft that considers acceleration correction, belonging to the field of civil aviation technology. Addressing the problem of unmeasurable airflow angles when airflow angle (angle of attack and sideslip angle) sensors malfunction in civil airliners, this invention presents an airflow angle estimation method based on extended Kalman filtering, combining a nonlinear six-degree-of-freedom model of the civil airliner and available navigation information, and corrects for the adverse effects caused by the accelerometer deviating from the aircraft's center of gravity. This invention can achieve accurate airflow angle estimation, providing high-precision airflow angle information without adding additional onboard equipment, which is of great significance for improving the performance and stability of core systems of civil airliners such as flight control systems and flight management systems.

Description

Technical Field

[0001] This invention belongs to the field of civil aviation technology, specifically relating to a method for estimating the airflow angle of civil aircraft that takes acceleration correction into account. Background Technology

[0002] Airflow angle, including angle of attack and sideslip angle, is a crucial flight status parameter. Its accuracy and reliability directly affect the operational status of critical airborne systems such as the flight management system, flight control system, and stall warning system. Furthermore, airflow angle is frequently used as a feedback signal for the flight control system; accurate and reliable airflow angle information is essential for ensuring control effectiveness and flight safety. When airflow angle sensors fail or their signal results are abnormal, if the measurement information from various sensors can be fully utilized in conjunction with the aircraft's kinematics and dynamics to estimate the airflow angle, the reliability of the flight control system can be improved, and the aircraft's sensitivity to sensor failures can be reduced.

[0003] This invention was proposed against this background, providing a solution to the problem of unmeasurable airflow angle when the airflow angle (angle of attack and sideslip angle) sensor of civil aircraft malfunctions. It is of great significance for improving the performance and stability of core systems of civil aircraft such as flight control system and flight management system, and ensuring flight safety. Summary of the Invention

[0004] The technical problem to be solved by this invention is:

[0005] To address the problem of unmeasurable airflow angles when airflow angle (angle of attack and sideslip angle) sensors malfunction in civil aircraft, this invention proposes an airflow angle estimation method for civil aircraft that considers acceleration correction. Combining a nonlinear six-degree-of-freedom model of a civil aircraft with available navigation information, the airflow angle is estimated based on extended Kalman filtering.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A method for estimating the airflow angle of a civil aircraft considering acceleration correction, characterized by comprising:

[0008] The system acquires aerodynamic model information, engine model information, navigation parameters, weight, accelerometer installation location, and center of gravity location of the aircraft object. The aerodynamic model information includes aerodynamic lift, aerodynamic drag, and aerodynamic side forces. The engine model information includes engine thrust. The navigation parameter information includes roll angle, pitch angle, heading, roll rate, pitch rate, yaw rate, and vacuum speed.

[0009] The state vector is determined by the airflow angle, aerodynamic model information, engine model information, and weight. The state equation is constructed by the state vector, process noise vector, input determination, and running time.

[0010] The measurement equation is constructed using the state vector, measurement noise vector, and running time; the measurement state vector is constructed using the triaxial acceleration at the aircraft's center of mass and navigation parameters.

[0011] Calculate the Jacobian matrix of the state equation and the Jacobian matrix of the measurement equation, respectively.

[0012] The extended Kalman filter equation is constructed based on the Jacobian matrix of the state equation, the Jacobian matrix of the measurement equation, and the measurement state vector.

[0013] The airflow angle is estimated iteratively based on the extended Kalman filter equation.

[0014] A further technical solution of the present invention: the expression of the state vector is:

[0015]

[0016] Where g is the acceleration due to gravity; c is the abbreviation for cosine. α Represents cosα; s is an abbreviation for sin. α Represents sinα; α is the angle of attack, β is the sideslip angle, and F T For engine thrust, F L For aerodynamic lift, F D For aerodynamic drag, F Y φ is the aerodynamic side force, m is the weight, V is the vacuum velocity, φ is the roll angle, θ is the pitch angle, and r is the yaw rate.

[0017] A further technical solution of the present invention: the expression of the state equation is:

[0018]

[0019] Where X is the state vector, w is the process noise vector, and u = [pqr] T The input is deterministic, and t is the running time.

[0020] A further technical solution of the present invention: the expression of the measurement equation is as follows:

[0021] y = h(X,v,t)

[0022] Where v is the measurement noise vector.

[0023] A further technical solution of the present invention: a method for calculating the triaxial acceleration at the aircraft's center of gravity:

[0024]

[0025] Among them, a x,acc a y,acc and a z,accThe triaxial acceleration measured by the accelerometer, (x acc ,y acc ,z acc (x) represents the accelerometer installation location. cg ,y cg ,z cg ) represents the position of the aircraft's center of gravity, p represents the roll rate, and q represents the pitch rate.

[0026] A further technical solution of the present invention: the expression for the measurement state vector is:

[0027]

[0028] A further technical solution of the present invention: a method for calculating the Jacobian matrix of the state equation:

[0029]

[0030] Where F(t) is the Jacobian matrix of the state equation.

[0031] A further technical solution of the present invention: a method for calculating the Jacobian matrix of the measurement equation:

[0032]

[0033] Where H(t) is the Jacobian matrix of the measurement equation.

[0034] A further technical solution of the present invention: The expression of the extended Kalman filter equation is as follows:

[0035]

[0036] Where, Φ k,k-1 =I+F(t) k-1 )·T s Let I be the state transition matrix, and T be the identity matrix with the same dimension as F(t). s For discretization time; H k =H(t) k ) is the measurement matrix, For a one-step prediction of the state, K k For the filter gain, P k / k-1 The mean squared error for one-step prediction. For the state estimation at time k, P k To estimate the mean square error, R is the measurement noise variance matrix, and Q is the system noise variance matrix;

[0037] Based on initial values P0, R, and Q, based on the measurement Z at time k. k The state estimate at time k can then be calculated recursively.

[0038] A computer system is characterized by comprising: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method described above.

[0039] The beneficial effects of this invention are as follows:

[0040] This invention addresses the problem of unmeasurable airflow angles in civil aviation aircraft when airflow angle (angle of attack and sideslip angle) sensors malfunction. It proposes an airflow angle estimation method that considers acceleration correction. On one hand, this invention provides an additional set of airflow angle signals for comparison and monitoring by the airborne warning system, improving the redundancy of the airflow angle signals without increasing costs. On the other hand, this invention can provide high-precision airflow angle information without adding additional airborne equipment in the event of airflow angle sensor failure, providing a reliable backup signal for the flight control and flight management systems, thus ensuring flight safety. Attached Figure Description

[0041] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0042] Figure 1 A schematic diagram of the principle of airflow angle estimation considering acceleration correction.

[0043] Figure 2 Schematic diagram of acceleration correction principle.

[0044] Figure 3 Simulation results for angle of attack estimation.

[0045] Figure 4 Simulation results for sideslip angle estimation.

[0046] Figure 5 Flowchart of the method of this invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0049] To enable those skilled in the art to better understand the present invention, the present invention will be described in detail below with reference to specific embodiments.

[0050] Problem Description: The aircraft's initial flight altitude is 5000m, flight speed is 150m / s, initial heading is 0°, aircraft center of gravity position is (16.063,0,1.333)m, accelerometer installation position is (2,0,2)m, aircraft weight is 54000kg, initial pitch angle is 1.2220°, initial angle of attack is 1.2220°; In addition, to verify the effectiveness of the invention in dynamic processes, the aircraft maintains level flight for the first 20 seconds, climbs at the 20th second with a pitch angle command of 11.2220°, and descends at the 50th second with a pitch angle command of -4°.

[0051] The specific solution is as follows:

[0052] Step 1: Load the aerodynamic model, engine model, navigation parameters, weight (m), and accelerometer mounting position (x) of the aircraft object. acc ,y acc ,z acc ) and the position of the aircraft's center of gravity (x) cg ,y cg ,z cg Information such as aerodynamic model and engine model information includes: engine thrust (F...) T ), aerodynamic lift (F L ), aerodynamic drag (F D ) and lateral force (F Y Navigation parameters include: roll angle (φ), pitch angle (θ), heading (ψ), roll rate (p), pitch rate (q), yaw rate (r), and free speed (V).

[0053] Step 2: Constructing the state equations X is the state vector, w is the process noise vector, and u = [pqr]. TThe input is deterministic, and t is the running time.

[0054]

[0055] Where g is the acceleration due to gravity; c is an abbreviation for cosine, such as c α Represents cosα; s is an abbreviation for sin, such as s α β represents sinα; α is the angle of attack, and β is the sideslip angle.

[0056] Step 3: Calculate the triaxial acceleration (a) at the aircraft's center of mass. x,cg ,a y,cg ,a z,cg The calculation method is as follows:

[0057]

[0058] Among them, a x,acc a y,acc and a z,acc The triaxial acceleration is measured by an accelerometer.

[0059] Step 4: Construct the measurement equation y = h(X, v, t) and the measurement state vector Z, where v is the measurement noise vector and Z is:

[0060]

[0061] Step 5: Calculate the Jacobian matrix F(t) of the state equation. The calculation method is as follows:

[0062]

[0063] The components are:

[0064]

[0065] f 14 =g·c φ ·s β ·c θ -g·c β ·s α ·c θ ·s φ

[0066] f 15 =-g·s β ·s φ ·s θ -g·c α ·c β ·c θ -g·c β ·c φ ·s α ·sθ

[0067]

[0068]

[0069] Step 6: Calculate the Jacobian matrix H(t) of the measurement equation. The calculation method is as follows:

[0070]

[0071] The components are:

[0072] h 52 =F L ·c α +F D ·c β ·s α

[0073] h 53 =F D ·c α ·s β

[0074] h 63 =-F D ·c β

[0075] h 72 =F L ·s α -F D ·c α ·c β

[0076] h 73 =F D ·s α ·s β

[0077] Step 7: Construct the extended Kalman filter equation as follows:

[0078]

[0079] Where Φ k,k-1 =I+F(t) k-1 )·T s Let I be the state transition matrix, and T be the identity matrix with the same dimension as F(t). s For discretization time; H k =H(t) k ) is the measurement matrix, For a one-step prediction of the state, K k For the filter gain, P k / k-1 The mean squared error for one-step prediction. For the state estimation at time k, P k To estimate the mean square error, let R be the measurement noise variance matrix and Q be the system noise variance matrix. Then only initial values ​​need to be given. P0, R, and Q, based on the measurement Z at time k. k The state estimate at time k can then be calculated recursively.

[0080] In this embodiment, the initial value is given as:

[0081]

[0082] Step 8: Repeat steps 1 to 7 to obtain the estimated airflow angle as follows: Figure 3 and Figure 4 As shown.

[0083] The following is a detailed description of the effects of this application, using simulation results.

[0084] Simulation conclusions: Statistical analysis shows that without considering acceleration correction, the estimation errors for angle of attack are [-11.9638°, 70.7145°] and sideslip angle are [-1.0661°, 2.1206°]; considering acceleration correction, the estimation errors for angle of attack are [-3.1284°, 0.7099°] and sideslip angle are [-0.1442°, 0.1565°]. Simulation results show that the proposed method for estimating the airflow angle of civil aircraft considering acceleration correction can achieve high-precision airflow angle estimation and provide accurate and reliable backup angle of attack and sideslip angle signals for key airborne systems.

[0085] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the scope of the technology disclosed in the present invention, and such modifications or substitutions should all be covered within the scope of protection of the present invention.

Claims

1. A method for estimating the airflow angle of a civil aircraft considering acceleration correction, characterized in that, include: The system acquires aerodynamic model information, engine model information, navigation parameters, weight, accelerometer installation location, and center of gravity location of the aircraft object. The aerodynamic model information includes aerodynamic lift, aerodynamic drag, and aerodynamic side forces. The engine model information includes engine thrust. The navigation parameter information includes roll angle, pitch angle, heading, roll rate, pitch rate, yaw rate, and vacuum speed. The state vector is determined by the airflow angle, aerodynamic model information, engine model information, and weight. The state equation is constructed by the state vector, process noise vector, input determination, and running time. Measurement equations are constructed using the state vector, measurement noise vector, and runtime; the measurement state vector is constructed using the triaxial acceleration at the aircraft's center of gravity and navigation parameters; the method for calculating the triaxial acceleration at the aircraft's center of gravity is as follows: in, , and The triaxial accelerations measured by the accelerometer, ) is the installation location of the accelerometer. () represents the location of the aircraft's center of gravity. For the roll rate, The pitch rate; Calculate the Jacobian matrix of the state equation and the Jacobian matrix of the measurement equation, respectively. The extended Kalman filter equation is constructed based on the Jacobian matrix of the state equation, the Jacobian matrix of the measurement equation, and the measurement state vector. The airflow angle is estimated iteratively based on the extended Kalman filter equation.

2. The method for estimating the airflow angle of a civil aircraft considering acceleration correction according to claim 1, characterized in that, The expression for the state vector is: in, It is the acceleration due to gravity; for abbreviation, represent ; for abbreviation, represent ; For the angle of attack, Sideslip angle, For engine thrust, For aerodynamic lift, For aerodynamic drag, It is an aerodynamic lateral force. For weight, Vacuum speed, For roll angle, The pitch angle, The yaw rate, This is the heading angle.

3. The method for estimating the airflow angle of a civil aircraft considering acceleration correction according to claim 2, characterized in that, The state equation is expressed as follows: in, For state vectors, This is the process noise vector. For deterministic input, This refers to the runtime.

4. The method for estimating the airflow angle of a civil aircraft considering acceleration correction according to claim 3, characterized in that, The measurement equation is expressed as follows: in, This is the measurement noise vector.

5. The method for estimating the airflow angle of a civil aircraft considering acceleration correction according to claim 1, characterized in that, The expression for the measurement state vector is: 。 6. The method for estimating the airflow angle of a civil aircraft considering acceleration correction according to claim 5, characterized in that, The method for calculating the Jacobian matrix of the state equation is as follows: in, Let be the Jacobian matrix of the state equation.

7. The method for estimating the airflow angle of a civil aircraft considering acceleration correction according to claim 6, characterized in that, The method for calculating the Jacobian matrix of the measurement equation is as follows: in, Let be the Jacobian matrix of the measurement equation.

8. The method for estimating the airflow angle of a civil aircraft considering acceleration correction according to claim 7, characterized in that, The expression for the extended Kalman filter equation is as follows: in, Here is the state transition matrix. To and Identity matrices of the same dimension Discretization time; For the measurement matrix, This is a one-step prediction of the state. For filter gain, The mean squared error for one-step prediction. for State estimation at time 10:00 To estimate the mean square error, To measure the noise variance matrix, Let be the noise variance matrix of the system; Based on initial values , , and ,according to Measurement of time It can be calculated recursively. State estimation at time 1 .

9. A computer system, characterized in that... include: One or more processors, a computer-readable storage medium for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method of claim 1.

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