Civil aircraft airflow angle estimation method considering acceleration correction

Abstract

The invention relates to a civil aircraft airflow angle estimation method considering acceleration correction, and belongs to the technical field of civil aviation. Aiming at the problem that the airflow angle cannot be measured under the condition that a civil aviation passenger plane airflow angle (attack angle and sideslip angle) sensor fails, an airflow angle estimation method based on extended Kalman filtering is given by combining a civil aviation passenger plane nonlinear six-degree-of-freedom model and available navigation information, and adverse effects caused by the fact that an accelerometer deviates from the position of the mass center of the plane are corrected. According to the method, accurate airflow angle estimation can be realized, high-precision airflow angle information is provided under the condition of not adding extra airborne equipment, and the method is of great significance in improving the performance and stability of civil aviation passenger plane core systems such as a flight control system and a flight management system.

Description

Technical Field

[0001] The invention belongs to the field of civil aviation technology, and in particular relates to a method for estimating the airflow angle of a civil aircraft taking acceleration correction into consideration. Background Art

[0002] The airflow angle includes the angle of attack and the angle of sideslip, which is an important flight state parameter. Its accuracy and reliability directly affect the operation state of important airborne systems such as the flight management system, flight control system, and stall warning system. In addition, the airflow angle is often used as a feedback signal for the flight control system. Accurate and reliable airflow angle information is a necessary condition to ensure control effect and flight safety. When the airflow angle sensor fails or the signal voting result is abnormal, if the measurement information of each sensor can be fully utilized and combined with the aircraft kinematics and dynamics relationship to estimate it, the reliability of the flight control system can be improved and the sensitivity of the aircraft to sensor failure can be reduced.

[0003] The present invention is proposed in this context, and provides a solution to the problem that the airflow angle cannot be measured when the airflow angle (angle of attack and sideslip angle) sensor of a civil aircraft fails. This is of great significance for improving the performance and stability of core systems of civil aircraft such as flight control systems and flight management systems, and ensuring flight safety. Summary of the invention

[0004] The technical problems to be solved by the present invention are:

[0005] In order to solve the problem that the airflow angle (angle of attack and sideslip angle) of civil aircraft cannot be measured when the airflow angle sensor fails, the present invention proposes a civil aircraft airflow angle estimation method considering acceleration correction, combining the nonlinear six-degree-of-freedom model of the civil aircraft and the available navigation information, and estimating the airflow angle based on the extended Kalman filter.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

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

[0008] Acquire aerodynamic model information, engine model information, navigation parameters, weight, accelerometer installation position and aircraft center of mass position of the aircraft object; the aerodynamic model information includes aerodynamic lift, aerodynamic drag and aerodynamic side force; the engine model information includes engine thrust; the navigation parameter information includes roll angle, pitch angle, heading, roll angle rate, pitch angle rate, yaw angle rate, true airspeed;

[0009] Determine the state vector with the airflow angle, aerodynamic model information, engine model information, and weight, and construct the state equation with the state vector, process noise vector, determined input, and running time;

[0010] The measurement equation is constructed with the state vector, the measurement noise vector and the running time, and the measurement state vector is constructed with the three-axis acceleration at the center of mass of the aircraft and the navigation parameters;

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

[0012] Construct the extended Kalman filter equation 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] Among them, g is the acceleration due to gravity; c is the abbreviation of cos, c α stands for cosα; s is the abbreviation of sin, s α represents sinα; α is the angle of attack, β is the sideslip angle, F T is the engine thrust, F L is the aerodynamic lift, F D is the aerodynamic drag, F Y is the aerodynamic side force, m is the weight, V is the true airspeed, 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 is as follows: the state equation is expressed as:

[0018]

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

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

[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 three-axis acceleration at the center of mass of the aircraft:

[0024]

[0025] Among them, a x,acc 、a y,acc and a z,accis the three-axis acceleration measured by the accelerometer, (x acc ,y acc ,z acc ) is the installation position of the accelerometer, (x cg ,y cg ,z cg ) is the position of the aircraft's center of mass, p is the roll angular rate, and q is the pitch angular rate.

[0026] A further technical solution of the present invention: the measurement state vector expression 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] Among them, Φ k,k-1 =I+F(t k-1 )·T s is the state transfer matrix, I is the unit matrix with the same dimension as F(t), T s is the discretized time; H k =H(t k ) is the measurement matrix, is the one-step prediction of the state, K k is the filter gain, P k / k-1 is the one-step prediction mean square error, is the state estimate 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 value P0, R and Q, based on the measurement Z at time k k The state estimate at time k can be obtained by recursive calculation

[0038] A computer system, characterized in that it comprises: 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 implement the above-mentioned method.

[0039] The beneficial effects of the present invention are:

[0040] The present invention aims at the problem that the airflow angle cannot be measured when the airflow angle (angle of attack and sideslip angle) sensor of civil aircraft fails, and proposes an airflow angle estimation method taking acceleration correction into account. On the one hand, the present invention can provide an additional set of airflow angle signals for comparison and monitoring for the airborne warning system, thereby improving the redundancy of the airflow angle signal without increasing the cost; on the other hand, the present invention can provide high-precision airflow angle information without adding additional airborne equipment when the airflow angle sensor fails, thereby providing a reliable backup signal for the flight control system and the flight management system, thereby ensuring flight safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0042] Figure 1 Schematic diagram of airflow angle estimation considering acceleration correction.

[0043] Figure 2 Schematic diagram of acceleration correction.

[0044] Figure 3 Angle of attack estimation simulation results.

[0045] Figure 4 Sideslip angle estimation simulation results.

[0046] Figure 5 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0049] In order to enable those skilled in the art to better understand the present invention, the present invention is described in detail below in conjunction with specific embodiments.

[0050] Problem description: The initial flight altitude of the aircraft is 5000m, the flight speed is 150m / s, the initial heading is 0°, the position of the center of mass of the aircraft is (16.063,0,1.333)m, the installation position of the accelerometer is (2,0,2)m, the weight of the aircraft is 54000kg, the initial pitch angle is 1.2220°, and the initial angle of attack is 1.2220°; in addition, in order to verify the effectiveness of the present invention in the dynamic process, the aircraft maintains level flight in the first 20 seconds, climbs according to the pitch angle instruction of 11.2220° at the 20th second, and descends according to the pitch angle instruction of -4° at the 50th second.

[0051] The specific solutions are:

[0052] Step 1: Load the aircraft object's aerodynamic model, engine model, navigation parameters, weight (m), accelerometer installation position (x acc ,y acc ,z acc ) and the aircraft center of mass position (x cg ,y cg ,z cg ) and other information, aerodynamic model and engine model information include: engine thrust (F T ), aerodynamic lift (F L ), aerodynamic drag (F D ) and lateral force (F Y ); Navigation parameter information includes: roll angle (φ), pitch angle (θ), heading (ψ), roll angle rate (p), pitch angle rate (q), yaw angle rate (r), true airspeed (V);

[0053] Step 2: Construct the equation of state X is the state vector, w is the process noise vector, u=[pqr] Tis the deterministic input, t is the running time;

[0054]

[0055] Among them, g is the acceleration due to gravity; c is the abbreviation of cos, such as c α stands for cosα; s is the abbreviation of sin, such as s α It stands for sinα; α is the angle of attack and β is the sideslip angle.

[0056] Step 3: Calculate the three-axis acceleration (a x,cg ,a y,cg ,a z,cg ), the calculation method is:

[0057]

[0058] Among them, a x,acc 、a y,acc and a z,acc It is the three-axis acceleration measured by the 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 the measurement state vector Z is:

[0060]

[0061] Step 5: Calculate the Jacobian matrix F(t) of the state equation using the following method:

[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 using the following method:

[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 is the state transfer matrix, I is the unit matrix with the same dimension as F(t), T s is the discretized time; H k =H(t k ) is the measurement matrix, is the one-step prediction of the state, K k is the filter gain, P k / k-1 is the one-step prediction mean square error, is the state estimate 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. Then only the initial value needs to be given P0, R and Q, based on the measurement Z at time k k The state estimate at time k can be obtained by recursive calculation

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

[0081]

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

[0083] The effects of this application are described in detail below in conjunction with simulation.

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

[0085] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be included in the protection scope of the present invention.

Claims

1. A method for estimating the airflow angle of a civil aircraft considering acceleration correction, characterized in that: include: Acquire aerodynamic model information, engine model information, navigation parameters, weight, accelerometer installation position and aircraft center of mass position of the aircraft object; the aerodynamic model information includes aerodynamic lift, aerodynamic drag and aerodynamic side force; the engine model information includes engine thrust; the navigation parameter information includes roll angle, pitch angle, heading, roll angle rate, pitch angle rate, yaw angle rate, true airspeed; Determine the state vector with the airflow angle, aerodynamic model information, engine model information, and weight, and construct the state equation with the state vector, process noise vector, determined input, and running time; The measurement equation is constructed with the state vector, the measurement noise vector and the running time, and the measurement state vector is constructed with the three-axis acceleration at the center of mass of the aircraft and the navigation parameters; Calculate the Jacobian matrix of the state equation and the Jacobian matrix of the measurement equation respectively; Construct the extended Kalman filter equation 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. A method for estimating the airflow angle of a civil aircraft taking acceleration correction into account according to claim 1, characterized in that: The expression of the state vector is: Among them, g is the acceleration due to gravity; c is the abbreviation of cos, c α stands for cosα; s is the abbreviation of sin, s α represents sinα; α is the angle of attack, β is the sideslip angle, F T is the engine thrust, F L is the aerodynamic lift, F D is the aerodynamic drag, F Y is the aerodynamic side force, m is the weight, V is the true airspeed, φ is the roll angle, θ is the pitch angle, and r is the yaw rate.

3. A method for estimating the airflow angle of a civil aircraft taking acceleration correction into account according to claim 2, characterized in that: The state equation expression is: Where X is the state vector, w is the process noise vector, and u = [pqr] T is the deterministic input and t is the running time.

4. A method for estimating airflow angle of a civil aircraft taking acceleration correction into account according to claim 3, characterized in that: The measurement equation is expressed as: y=h(X,v,t) Where v is the measurement noise vector.

5. A method for estimating airflow angle of a civil aircraft taking acceleration correction into account according to claim 4, characterized in that: The calculation method of the three-axis acceleration at the center of mass of the aircraft is: Among them, a x,acc 、a y,acc and a z,acc is the three-axis acceleration measured by the accelerometer, (x acc ,y acc ,z acc ) is the installation position of the accelerometer, (x cg ,y cg ,z cg ) is the position of the center of mass of the aircraft, p is the roll angular rate, and q is the pitch angular rate.

6. A method for estimating airflow angle of a civil aircraft taking acceleration correction into account according to claim 5, characterized in that: The measurement state vector expression is:

7. A method for estimating the airflow angle of a civil aircraft taking acceleration correction into account according to claim 6, characterized in that: The calculation method of the Jacobian matrix of the state equation is: Where F(t) is the Jacobian matrix of the state equation.

8. A method for estimating airflow angle of a civil aircraft taking acceleration correction into account according to claim 7, characterized in that: The calculation method of the Jacobian matrix of the measurement equation is: Where H(t) is the Jacobian matrix of the measurement equation.

9. A method for estimating airflow angle of a civil aircraft taking acceleration correction into account according to claim 8, characterized in that: The expression of the extended Kalman filter equation is as follows: Among them, Φ k,k-1 =I+F(t k-1 )·T s is the state transfer matrix, I is the unit matrix with the same dimension as F(t), T s is the discretized time; H k =H(t k ) is the measurement matrix, is the one-step prediction of the state, K k is the filter gain, P k / k-1 is the one-step prediction mean square error, is the state estimate 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; Based on initial value P0, R and Q, based on the measurement Z at time k k The state estimate at time k can be obtained by recursive calculation 10. A computer system, characterized in that include: 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 are enabled to implement the method of claim 1.

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