A real-time monitoring system for high-precision dynamic measurement of multiple control surfaces of aircraft

Abstract

The invention discloses a high-precision dynamic measurement and real-time monitoring system for multiple control surfaces of an aircraft, the system comprising: a test unit and a host computer; the test unit comprises a sensor assembly, a power module, an AD digital-to-analog conversion module, an RS485 communication module, a wireless communication module, a single-chip microcomputer (MCU) and a power supply. An inertial sensor is mounted on each control surface to be tested of the aircraft, and the communication between the sensor and the main control interface is realized by wireless or wired means. No external equipment is used, and the control surface to be tested is simply rotated to three arbitrary static angles, and then error calculation is performed. The invention is simple to operate, and the installation error can be obtained by deflecting any angle, which can be displayed on the interface, and has high measurement accuracy and good repeatability.

Description

Technical Field

[0001] The invention belongs to the technical field of aircraft multi-control surface measurement, and in particular relates to a high-precision dynamic measurement and real-time monitoring system for aircraft multi-control surfaces. Background Art

[0002] The aircraft rudder uses deflection to generate balancing force and control force to manipulate the aerodynamic wing surface of the flight, and is an important control system for the flight of the aircraft. Before taking off, aircraft (military, civilian, drone, weapon system) need a careful and complete pre-flight inspection process to ensure absolute safety during flight, especially for civil aircraft, which is more complicated, and the rudder detection is undoubtedly one of the most important detection items among all test items. The rudder detection includes static detection and dynamic detection. In order to ensure the accuracy of the detection, it is necessary to use detection methods other than the control mechanism for comparative testing. The commonly used optical test system is inefficient, has occlusions, and is limited in integrity, volume, and space. Therefore, there is an urgent need for a complete rudder detection system to efficiently complete the verification work, so as to improve the efficiency of verification in aircraft design, field test flight, and verification and maintenance, and provide strong guarantees for safe flight. Traditional optical detection systems have problems such as low efficiency and occlusion. Summary of the invention

[0003] In view of this, the present invention proposes a high-precision dynamic measurement and real-time monitoring system for multiple control surfaces of an aircraft, which has the characteristics of simple operation, being able to obtain installation errors by deflecting any angle, being able to display on an interface, having high measurement accuracy, and good repeatability.

[0004] Specifically, the present invention discloses a high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft, comprising a sensor test unit and a host computer;

[0005] The sensor test unit includes a sensor component, a power module, an AD digital-to-analog conversion module, a communication module and an MCU single-chip computer; the sensor component includes a test accelerometer, a gyroscope, a magnetometer

[0006] The test accelerometer measures the gravitational acceleration in three directions of the carrier;

[0007] The power module completes the conversion of the power supply voltage, converting the external power supply into the power required by the inertial navigation board and the corresponding circuit;

[0008] The MCU completes the data acquisition, signal integration, compensation and data output functions of the test accelerometer, gyroscope and magnetometer, and transmits the data to the host computer through the AD digital-to-analog conversion module;

[0009] The host computer has visualization function, which can complete the system parameter setting, installation error correction, and reception and release of attitude angle data.

[0010] Furthermore, the software of the host computer adopts C# or Labview to realize the interactive interface design, receives and displays the attitude angle data sent by the MCU in real time; has the function of installation error correction to correct the situation that the sensor axis system is not parallel to the model axis system; converts the Euler angle to the angle of attack and the roll angle; has the functions of local collection and storage of the deflection angle of the rudder surface and the attitude angle, UDP broadcast in the local area network, and accepting the test command of the field test management software;

[0011] The visualization functions of the host computer include: visualization of the 3D aircraft model corresponding to the 3D test angle, relative zero point setting, observation of the original test accelerometer and gyroscope, and switching between Chinese and English.

[0012] Furthermore, the sensor assembly shell is a square aluminum alloy shell with a three-dimensional orthogonal design inside. It is mounted on each tested control surface of the aircraft in a lossless mounting manner, and the communication between the sensor and the main control interface is achieved through wireless or wired means.

[0013] Furthermore, the communication module includes an RS485 communication module and a wireless communication module.

[0014] Furthermore, the sensor assembly is mounted at a designated position of the rudder to be tested, the host computer system is initialized, and three arbitrary positions are randomly rotated to obtain the installation error, and the installation error of the sensor is corrected. The correction method specifically includes:

[0015] The sensor assembly installation error includes three error angles with the surface of the measured object, so the installation error angle solution is to solve the three installation error angles and the corresponding error calibration rotation matrix;

[0016] The pitch error angle is e θ , the rolling error angle is e γ , the heading error angle is e ψ , the corresponding error calibration rotation matrices are as follows:

[0017]

[0018]

[0019]

[0020] If the error calibration rotation matrix is ​​Me, then Me = Me ψ ×M_e θ ×M_e γ ,

[0021]

[0022] The transformation formula from the object coordinate system n to the sensor coordinate system b is obtained by the error calibration rotation matrix:

[0023]

[0024] In the first test, the object under test is stable at the initial moment, the standard initial zero position, that is, the rotation angle θ0 = 0 and γ0 = 0, the corresponding coordinates of the object under test Sensor corresponding coordinates Then we get the following equation:

[0025]

[0026]

[0027]

[0028] Among them, the unknown quantity is e θ 、e γ and e ψ , Since the three equations are coupled, the first two valid formulas are taken;

[0029] In the second test, the object under test rotates three times around the X-axis at angles of θ1, θ2 and θ3, and the corresponding coordinates of the object under test are and The test accelerometer readings are The following equation is obtained:

[0030]

[0031]

[0032]

[0033] Where i = 1, 2, 3, and the unknown quantity is e θ 、e γ 、e ψ , θ1, θ2 and θ3, six valid formulas are obtained from the above formula, as follows:

[0034]

[0035]

[0036]

[0037] y b2 =cos(e θ )cos(e ψ )sin(θ1)-sin(e θ )cos(θ1)

[0038] x b3 =(cos(e ψ )sin(e θ )sin(e γ )-cos(e γ )sin(e ψ ))sin(θ3)+cos(e θ )sin(e γ )cos(θ3)

[0039] y b3 =cos(e θ )cos(e ψ )sin(θ3)-sin(e θ )cos(θ3)

[0040] In the third test, the object under test rotates three times around the Y axis at angles of γ1, γ2 and γ3, and the corresponding coordinates of the object under test are The test accelerometer readings are The following equation is obtained:

[0041]

[0042]

[0043]

[0044] Where i = 4, 5, 6, and the unknown quantity is e θ 、e γ 、e ψ , γ1, γ2 and γ3, six valid formulas are obtained from the above formula;

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051] Through the above three tests, 14 valid formulas are obtained. The Jacobi iteration method and the Newton iteration method are used to solve 9 unknowns to obtain the pitch error angle, roll error angle and heading error angle, and then the error calibration matrix is ​​obtained. The coordinate system corresponding to the sensor component is transformed to the coordinate system of the object under test through the error calibration matrix to measure the angle of the object under test.

[0052] Furthermore, an online calibration method of adaptive Kalman filtering is used to achieve accurate monitoring of the tail angle, including:

[0053] An autoregressive moving average ARMA model is established for the random drift of the gyroscope. The three modes of converting the ARMA model into the state space model are analyzed and the model conversion method is given:

[0054] The structural formula of the AMRA model is

[0055]

[0056] Among them, p and q represent the order of the p-order autoregressive and q-order moving average model respectively. Q j are the model parameters of these two parts respectively, It means: when k is fixed, is a normal distribution; when k changes, each a k Independent of each other;

[0057] The random drift ARMA model is converted into a state space model. When the ARMA is an ARMA (3, 2) model, the expression of ARMA is

[0058]

[0059] Mode 1:

[0060] Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =[a k a k-1 a k-2 ] T , satisfying the following system state space expression:

[0061] X k =AX k-1 +BW K

[0062] Z k =H k

[0063] in, H = [1 0 0];

[0064] Mode 2:

[0065] Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =a k , satisfying the following system state space expression:

[0066] X k =AX k-1 +BW K

[0067] Z k =H k

[0068] in, H=[1 0 0], G0~G2 are Green functions of ARMA(3,2), that is: G0=1,

[0069] Mode 3:

[0070] Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =a k , satisfying the following system state space expression:

[0071] X k =AX k-1 +BW K

[0072] Z k =H k

[0073] in, H = [1 0 0];

[0074] The probability density model is used to model the carrier angular rate to adapt to the uncertainty of the carrier's maneuvering mode;

[0075] Considering the time-varying characteristics of gyro random drift, the virtual noise method is used to offset the calibration error caused by fixed ARMA model parameters;

[0076] Finally, combined with the known calibration errors of the three-axis accelerometers, the precise angle of the tail rudder can be calculated simultaneously.

[0077] Furthermore, the virtual noise method adopts the Allan variance curve fitting program, in which five basic errors, namely angle quantization noise, angle random walk, zero bias instability, angular rate random walk, and rate ramp, are preset, and two Markov basic processes are set. Through the curve fitting manual adjustment process, the simulated and independent angle random walk, angular rate random walk, rate ramp and two Markov processes are obtained, a total of five basic error time series, which are added again as the simulation reproduction of the virtual gyroscope drift.

[0078] The beneficial effects of the present invention are as follows:

[0079] Without using any external equipment, the measured rudder surface is simply rotated to three arbitrary static angles, and then the error calculation is performed. The invention is simple to operate, and the installation error can be obtained by deflecting any angle, which can be displayed on the interface, with high measurement accuracy and good repeatability. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0081] Figure 1 The detection system framework diagram of the present invention;

[0082] Figure 2 A flowchart of a system for real-time monitoring of multiple control surfaces of an aircraft with high precision dynamic measurement is provided in one embodiment of the present invention;

[0083] Figure 3 Schematic diagram of the rudder angle measurement tooling based on multiple transmission channels;

[0084] Figure 4 Schematic diagram of the host computer interface. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0086] like Figure 1The research block diagram of the attitude fusion algorithm architecture shown in the figure shows that the invention provides a high-precision dynamic measurement real-time detection system for multiple control surfaces of an aircraft, which includes the following parts: the system includes: a test unit and a host computer; the sensor test unit includes a sensor component, a power module, an AD digital-to-analog conversion module, an RS485 communication module, a wireless communication module, a single-chip microcomputer (MCU) and a power supply. The sensor component includes a test accelerometer, a gyroscope, and a magnetometer. The invention designs and implements an inertial navigation system architecture based on multiple redundant accelerometers and multiple redundant gyroscopes, and performs error compensation on the accelerometers and zero bias correction on the gyroscopes. The redundant information fusion algorithm is used to obtain angular acceleration and acceleration information with high accuracy and good reliability, and abnormal data is automatically eliminated. The designed adaptive Kalman algorithm fuses the data of the accelerometer and the gyroscope, and uses the magnetometer as an auxiliary to obtain the attitude information. The designed extended Kalman algorithm fuses the data of the accelerometer and the gyroscope, and uses the attitude as an auxiliary to obtain the position information; the obtained attitude quantity and position quantity are complementary to each other to calibrate each other, and the attitude information and position information with high accuracy and high stability are obtained.

[0087] Above Figure 1 A high-precision dynamic measurement and real-time detection system for multiple control surfaces of an aircraft can be used to perform the installation error correction and dynamic high-precision flight attitude real-time detection of the present invention, and accordingly achieve the technical effect of high-precision dynamic real-time measurement achieved by the above-mentioned present invention, which will not be repeated here.

[0088] refer to Figure 2 , the technical solution of the present invention is:

[0089] A high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft, comprising a sensor test unit and a host computer;

[0090] The sensor test unit includes test accelerometer, gyroscope, magnetometer, power module, AD digital-to-analog conversion module, RS485 communication module and single-chip microcomputer (MCU). The accelerometer measures the gravity acceleration in three directions of the carrier. The power supply part completes the conversion of the power supply voltage, converting the external power supply into the power supply required by the internal and corresponding circuits of the inertial navigation board. The power conversion part completes the conversion of the required current between the various circuits. The MCU part completes the data acquisition, signal integration, compensation and data output of the accelerometer, gyroscope and magnetometer. Finally, the MCU transmits the data to the host computer through the AD digital-to-analog conversion module; the host computer has a visualization function to complete the system parameter setting, installation error correction, and reception and release of attitude angle data.

[0091] In some embodiments, the host computer software uses C# or Labview to complete the interactive interface design, and receives and displays the attitude angle data sent by the MCU in real time; it has the function of installation error correction (referring to the situation where the sensor axis system is not parallel to the model axis system), and the error correction parameter input interface is designed in the interface; it has the function of converting the Euler angle to the angle of attack and the roll angle, and the conversion option is designed in the interface; it has the functions of local collection and storage of the rudder deflection angle and the attitude angle, UDP broadcast in the local area network, and accepting the test commands of the field test management software. In addition, the host computer has added visualization functions, such as: adding the three-dimensional test angle corresponding to the three-dimensional aircraft model; adding the relative zero point setting function; adding the observation window of the original accelerometer and gyroscope; it can switch between Chinese and English, etc., with the advantages of convenient operation and good interface interaction design.

[0092] In some embodiments, the sensor housing is a square housing, and the internal design adopts a three-dimensional orthogonal design to make reasonable use of space. The sensor is small in size, the housing is made of aluminum alloy, light in weight, and has the characteristics of rust prevention, corrosion prevention, and easy processing. It is mounted on each of the aircraft's rudder surfaces in a non-destructive mounting manner, and the communication between the sensor and the main control interface is realized by wireless or wired means, which greatly improves the detection efficiency.

[0093] In some embodiments, the transmission mode of the test unit is through RS485 wired transmission and wireless transmission module.

[0094] In some embodiments, a hidden field calibration method is used to mount the sensor at a specified position on the rudder to be tested, initialize the host computer system, rotate three arbitrary positions at will, obtain the installation error, and perform installation error correction on the sensor, specifically including:

[0095] The installation error of the sensor component mainly includes three error angles with the surface of the measured object. The installation error angle solution mainly involves solving the three installation error angles and the corresponding error calibration rotation matrix.

[0096] Let the pitch error angle be e θ , the rolling error angle is e γ , the heading error angle is e ψ , the corresponding error calibration rotation matrices are as follows:

[0097]

[0098]

[0099]

[0100] If the error calibration rotation matrix is ​​Me, then Me = Me ψ ×M_e θ×M_e γ ,

[0101]

[0102] The transformation formula from the measured object coordinate system n to the sensor coordinate system b is obtained through the error calibration matrix:

[0103]

[0104] In the first test, the object under test is stable at the initial moment, the standard initial zero position, that is, the rotation angle θ0 = 0 and γ0 = 0, the corresponding coordinates of the object under test Sensor corresponding coordinates Then we can get the following equation:

[0105]

[0106]

[0107]

[0108] Among them, the unknown quantity is e θ 、e γ and e ψ , since the three equations are coupled, the first two valid formulas are taken.

[0109] In the second test, the object under test rotates three times around the X-axis at angles of θ1, θ2 and θ3, and the corresponding coordinates of the object under test are and The sensor readings are Then we can get the following equation:

[0110]

[0111]

[0112]

[0113] Where i = 1, 2, 3, and the unknown quantity is e θ 、e γ 、e ψ , θ1, θ2 and θ3, six valid formulas can be obtained from the above formula.

[0114]

[0115]

[0116]

[0117] yb2 =cos(e θ )cos(e ψ )sin(θ1)-sin(e θ )cos(θ1)

[0118] x b3 =(cos(e ψ )sin(e θ )sin(e γ )-cos(e γ )sin(e ψ ))sin(θ3)+cos(e θ )sin(e γ )cos(θ3)

[0119] y b3 =cos(e θ )cos(e ψ )sin(θ3)-sin(e θ )cos(θ3)

[0120] In the third test, the object under test rotates three times around the Y axis at angles of γ1, γ2 and γ3, and the corresponding coordinates of the object under test are and The sensor readings are Then we can get the following equation:

[0121]

[0122]

[0123]

[0124] Where i = 4, 5, 6, and the unknown quantity is e θ 、e γ 、e ψ , γ1, γ2 and γ3, six valid formulas can be obtained from the above formula.

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131] Through the above three tests, 14 valid formulas are obtained. The Jacobi iteration method and the Newton iteration method are used to solve 9 unknowns to obtain the pitch error angle, roll error angle and heading error angle, and then the error calibration matrix can be obtained. The coordinate system corresponding to the sensor can be transformed to the coordinate system of the object under test through the error calibration matrix, so the angle of the object under test can be effectively measured.

[0132] In some embodiments, an online calibration method of adaptive Kalman filtering is used to achieve accurate monitoring of the tail angle, specifically including:

[0133] Firstly, on the basis of establishing the autoregressive moving average ARMA model based on the random drift of the gyroscope, the three modes of converting the ARMA model into the state space model are analyzed from both theoretical and experimental aspects, and a model conversion method with strong applicability is given; then, the probability density model is used to model the carrier angular rate to adapt to the uncertainty of the carrier's maneuvering mode; finally, considering the time-varying characteristics of the gyroscope random drift, the virtual noise method is used to offset the calibration error caused by the fixed ARMA model parameters, and combined with the known calibration error of the three-axis accelerometer, the precise angle of the tail rudder is solved jointly.

[0134] An autoregressive moving average ARMA model is established for the random drift of the gyroscope. The three modes of converting the ARMA model into the state space model are analyzed and the model conversion method is given:

[0135] The structural formula of the AMRA model is

[0136]

[0137] Among them, p and q represent the order of the p-order autoregressive and q-order moving average model respectively. Q j are the model parameters of these two parts respectively, It means: when k is fixed, is a normal distribution; when k changes, each a k Independent of each other;

[0138] The random drift ARMA model is converted into a state space model. When the ARMA is an ARMA(3,2) model, the expression of ARMA is:

[0139]

[0140] Mode 1:

[0141] Let the state variable X k =[x k x k-1 x k-2 ] T, system noise variable W k =[a k a k-1 a k-2 ] T , satisfying the following system state space expression:

[0142] X k =AX k-1 +BW K

[0143] Z k =H k

[0144] in, H = [1 0 0];

[0145] Mode 2:

[0146] Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =a k , satisfying the following system state space expression:

[0147] X k =AX k-1 +BW K

[0148] Z k =H k

[0149] in, H=[1 0 0], G0~G2 are Green functions of ARMA(3,2), that is: G0=1,

[0150] Mode 3:

[0151] Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =a k , satisfying the following system state space expression:

[0152] X k =AX k-1 +BW K

[0153] Z k =Hk

[0154] in, H = [1 0 0];

[0155] The probability density model is used to model the carrier angular rate to adapt to the uncertainty of the carrier's maneuvering mode;

[0156] Considering the time-varying characteristics of gyro random drift, the virtual noise method is used to offset the calibration error caused by fixed ARMA model parameters;

[0157] Finally, combined with the known calibration errors of the three-axis accelerometers, the precise angle of the tail rudder can be calculated simultaneously.

[0158] Figure 3 This is a schematic diagram of the tooling of the rudder angle sensor based on multiple transmission channels. The inertial sensor is mounted on each rudder to be tested on the aircraft, and the communication between the sensor and the main control interface is realized by wireless or wired means, which greatly improves the detection efficiency.

[0159] Figure 4 The figure shows a schematic diagram of the upper computer interface. The upper computer has added visualization functions, such as: adding a three-dimensional aircraft model corresponding to a three-dimensional test angle; adding a relative zero point setting function; adding observation windows for the original accelerometer and gyroscope; and being able to switch between Chinese and English. It has the advantages of convenient operation and good interface interaction design.

[0160] The beneficial effects of the present invention are as follows:

[0161] Without using any external equipment, the measured rudder surface is simply rotated to three arbitrary static angles, and then the error calculation is performed. The invention is simple to operate, and the installation error can be obtained by deflecting any angle, which can be displayed on the interface, with high measurement accuracy and good repeatability.

[0162] The above described device is schematic, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0163] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, or by hardware. The above technical solution can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft, characterized in that: Including sensor test unit and host computer; The sensor test unit includes a sensor component, a power module, an AD digital-to-analog conversion module, an RS485 communication module, a wireless communication module and an MCU single-chip computer; the sensor component includes a test accelerometer, a gyroscope and a magnetometer; The accelerometer measures the gravitational acceleration of the carrier in three directions; The power supply part completes the conversion of the power supply voltage, converting the external power supply into the power supply required by the inertial navigation board and the corresponding circuits; The MCU completes the data collection, signal integration, compensation and data output functions of the test accelerometer, gyroscope and magnetometer; and transmits the data to the host computer through the AD digital-to-analog conversion module; The host computer has visualization function, which can complete the system parameter setting, installation error correction, and reception and release of attitude angle data; The sensor is mounted on the specified position of the rudder surface to be measured, the upper computer system is initialized, and three arbitrary positions are randomly rotated to obtain the installation error. The installation error of the sensor is corrected, which includes: the installation error of the sensor component mainly includes three error angles with the surface where the measured object is located, and the installation error angle solution mainly solves the three installation error angles and the corresponding error calibration rotation matrix; Let the pitch error angle be e θ , the rolling error angle is e γ , the heading error angle is e ψ , the corresponding error calibration rotation matrices are as follows: If the error calibration rotation matrix is ​​Me, then Me = Me ψ ×M_e θ ×M_e γ , The transformation formula from the measured object coordinate system n to the sensor coordinate system b is obtained through the error calibration matrix: In the first test, the object under test is stable at the initial moment, the standard initial zero position, that is, the rotation angle θ0 = 0 and γ0 = 0, the corresponding coordinates of the object under test Sensor corresponding coordinates Then we can get the following equation: Among them, the unknown quantity is e θ 、e γ and e ψ , Since the three equations are coupled, the first two valid formulas are taken; In the second test, the object under test rotates three times around the X-axis at angles of θ1, θ2 and θ3, and the corresponding coordinates of the object under test are and The sensor readings are Then we can get the following equation: Where i = 1, 2, 3, and the unknown quantity is e θ 、e γ 、e ψ , θ1, θ2 and θ3, six valid formulas can be obtained from the above formula, as follows: In the third test, the object under test rotates three times around the Y axis at angles of γ1, γ2 and γ3, and the corresponding coordinates of the object under test are and The sensor readings are Then we can get the following equation Where i = 4, 5, 6, and the unknown quantity is e θ 、e γ 、e ψ , θ1, θ2 and θ3, six valid formulas can be obtained from the above formula, as follows: Through the above three tests, 14 valid formulas are obtained. The Jacobi iteration method and the Newton iteration method are used to solve 9 unknowns to obtain the pitch error angle, roll error angle and heading error angle, and then the error calibration matrix can be obtained. The coordinate system corresponding to the sensor can be transformed to the coordinate system of the object under test through the error calibration matrix, so the angle of the object under test can be effectively measured.

2. The high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft according to claim 1, characterized in that: The host computer software uses C# or Labview to complete the interactive interface design, receives and displays the attitude angle data sent by the MCU in real time; has an installation error correction function to correct the situation where the sensor axis system is not parallel to the model axis system; converts the Euler angle to the angle of attack and the roll angle; It has the functions of local collection and storage of rudder deflection angle and attitude angle, LAN UDP broadcast, and acceptance of test commands from field test management software; The visualization functions of the host computer include: visualization of the 3D aircraft model corresponding to the 3D test angle, relative zero point setting, observation of the original test accelerometer and gyroscope, and switching between Chinese and English.

3. The high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft according to claim 1, characterized in that: The sensor assembly shell is a square aluminum alloy shell with a three-dimensional orthogonal design inside. It is mounted on each tested control surface of the aircraft in a lossless mounting manner, and the communication between the sensor and the main control interface is achieved wirelessly or wired.

4. The high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft according to claim 1, characterized in that: The communication module includes an RS485 communication module and a wireless communication module.

5. A high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft according to any one of claims 1 to 4, characterized in that: The online calibration method of adaptive Kalman filtering is used to achieve accurate monitoring of the tail angle, which includes: establishing an autoregressive moving average ARMA model for the random drift of the gyroscope, converting the ARMA model into three modes of state space model for analysis, and giving the model conversion method: the structure of the AMRA model is: Among them, p and q represent the order of the p-order autoregressive and q-order moving average model respectively. θ j are the model parameters of these two parts respectively, It means: when k is fixed, is a normal distribution; when k changes, each a k are independent of each other; The random drift ARMA model is converted into a state space model. When the ARMA is an ARMA(3,2) model, the expression of ARMA is: Mode 1: Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =[a k a k-1 a k-2 ] T , satisfying the following system state space expression: X k =AX k-1 +BW k in, H = [1 0 0]; Mode 2: Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =a k , satisfying the following system state space expression: X k =AX k-1 +BW k Z k =H k in, H=[1 0 0], G0~G2 are Green functions of ARMA(3,2), that is: G0=1, Mode 3: Let the state variable X k =[x k x k-1 x k-2 ] T , system noise variable W k =a k , satisfying the following system state space expression: X k =AX k-1 +BW k Z k =H k in, H = [1 0 0]; The probability density model is used to model the carrier angular rate to adapt to the uncertainty of the carrier's maneuvering mode; Considering the time-varying characteristics of gyro random drift, the virtual noise method is used to offset the calibration error caused by fixed ARMA model parameters; Finally, combined with the known calibration errors of the three-axis accelerometers, the precise angle of the tail rudder can be calculated simultaneously.

6. The high-precision dynamic real-time monitoring system for multiple control surfaces of an aircraft according to claim 5, characterized in that: The virtual noise method adopts the Allan variance curve fitting program. Five basic errors, namely angle quantization noise, angle random walk, zero bias instability, angular rate random walk, and rate ramp, are preset in the program, and two Markov basic processes are set. Through the curve fitting manual adjustment process, the simulated and independent angle random walk, angular rate random walk, rate ramp and two Markov processes are obtained, a total of five basic error time series, which are added again as the simulation reproduction of the virtual gyroscope drift.

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