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CN-116756686-B - Method and system for estimating strong disturbance rejection altitude state of aircraft

CN116756686BCN 116756686 BCN116756686 BCN 116756686BCN-116756686-B

Abstract

The invention provides a strong disturbance rejection altitude state estimation method and system of an aircraft, the method comprises the following steps of S1, obtaining data of a sensor, estimating time delay of the sensor, aligning the time delay of the sensor to obtain an observed quantity of the sensor, S2, estimating error variance of the sensor according to the observed quantity of the sensor, S3, calculating second differentiation of the observed quantity of the sensor, calculating correlation degree of the second differentiation and acceleration of inertial measurement, S4, carrying out data weighted fusion on the observed quantity of the sensor and the correlation degree, calculating relative altitude of the aircraft from the ground, S5, carrying out filtering processing on the relative altitude and the absolute altitude by a third-order Kalman filtering algorithm, switching the observed quantity of the altitude, calculating the altitude of the aircraft by adopting a altitude observed quantity switching mechanism, and finally estimating the altitude of the aircraft from the ground by a first-order Kalman filtering algorithm. The invention has accurate observation of the altitude state in the aircraft and high flight safety and reliability.

Inventors

  • TAO JIE
  • XU XIURONG

Assignees

  • 广东工业大学

Dates

Publication Date
20260505
Application Date
20230619

Claims (7)

  1. 1. A method of estimating a strong immunity altitude state of an aircraft, the method comprising the steps of: S1, acquiring data of a sensor, estimating the time delay of the sensor, and aligning the time delay of the sensor to obtain the observed quantity of the sensor; S2, estimating the error variance of the sensor according to the observed quantity of the sensor; S3, calculating a secondary differential of the observed quantity of the sensor, and calculating the correlation degree between the secondary differential and the inertial measurement acceleration; S4, carrying out data weighted fusion on the observed quantity of the sensor and the correlation degree, and calculating the relative height of the aircraft from the ground; S5, acquiring the current absolute height, respectively carrying out filtering treatment on the relative height and the absolute height by using a third-order Kalman filtering algorithm, switching the altitude observed quantity by using an altitude observed quantity switching mechanism, calculating the altitude of the aircraft, and finally estimating the ground clearance of the aircraft by using a first-order Kalman filtering algorithm; the step S3 specifically comprises the following substeps: According to the data at the current moment being z (0), the historical data being z (k), k=1, 2..n-1, and calculating a differential of the observed quantity of the sensor as follows: ...(8); Re-recording the sequence value of the first derivative of the observed quantity of the sensor (K) K=1, 2,..n-1, and calculating the second derivative of the sensor observations: ...(9); Recording a secondary differential sequence value of the observed quantity of the sensor, and calculating and recording an order value of the upward acceleration under an inertial coordinate system: (k),k=1,2,...n-1 ...(10); , ...(11); ...(12); Wherein, the Is the value of the three axes of the accelerometer, And finally, calculating a correlation function g (X) of the observed quantity secondary differential and the unmanned aerial vehicle antenna acceleration: The correlation function ...(13); In the step S4, the method for calculating the relative height specifically includes the following steps: Presetting an aircraft to carry n ranging sensors, wherein the observed noise variance of each ranging sensor is as follows The correlation coefficient of the quadratic differential term of each ranging sensor and the tangential acceleration is The observed quantity of each ranging sensor is Wherein i=1, 2,..n, n is a positive integer; Weighting coefficients of each of the ranging sensors I=1, 2,..n, calculated as follows: Order the ... (14); Then ...(15); And carrying out weighted fusion on the observed quantity, and calculating the relative height r_H of the aircraft from the ground: ...(16)。
  2. 2. The method for estimating a high noise immunity altitude state of an aircraft according to claim 1, wherein in the step S1, the sensor time delay represents a time difference between the data received by the processor and the actual data, which is required to be preset for processing and transmitting the data after the sensor collects the data.
  3. 3. The method of estimating a high noise immunity altitude state of an aircraft according to claim 2, wherein the data defining the current time of output of a certain one of said sensors is z (k), and the historical data of output of said sensor is z (k+1), z (k+2), Z (k+n), wherein n is an integer, the data after the sensor delay alignment is a sensor observed quantity z_delay, the sensor observed quantity z_delay is shown by expression (1), and the sensor delay Td is shown by expression (2): ...(1); ...(2); Wherein, the For the time delay relative to the largest time delay sensor, Is the sampling period of the sensor.
  4. 4. A method for estimating a strong immunity altitude state of an aircraft according to claim 3, characterized in that said step S2 comprises in particular the following sub-steps: Setting the data at the current moment as z (0) and the historical data as z (k), wherein k=1, 2,..n; Estimating the actual distance measured by the sensor The actual distance is as shown in expression (3): , ...(3); calculating an error of the sensor The error is as shown in expression (4): ...(4); The Variance of the error is calculated and taken into the kalman filter algorithm as shown in expression (5): ...(5); Wherein, the The coefficient a and the coefficient b in the expression are calculated by the following expression (6) and expression (7), respectively: ...(6); ...(7)。
  5. 5. The method for estimating the high noise immunity altitude state of an aircraft according to claim 4, wherein said step S5 comprises the following sub-steps: firstly, defining a Kalman filtering state equation: ...(17); Wherein h is the altitude state of the aircraft, v is the sky speed state of the aircraft, For acceleration in the upward direction Is used to determine the offset error of (a), X represents the system state at the current moment, U is the control quantity input of the system at the current moment, A is the system state transition matrix, and B is the control matrix of the system; Defining a Kalman filtering observation equation: ...(18); Obtaining a Kalman filtering algorithm by using a Kalman filtering state equation and a Kalman filtering observation equation; then, a first-order Kalman filtering algorithm is used for fusing the observed quantity of the ranging sensor and the tangential acceleration data, and estimating the ground distance of the unmanned aerial vehicle; The state equation and the observation equation of the first-order Kalman filtering are as follows: ...(19); Where r_H is the relative altitude of the aircraft, Is the unmanned aerial vehicle's forward speed estimated by the third-order Kalman filtering algorithm.
  6. 6. The method for estimating a strong immunity altitude state of an aircraft according to claim 5, wherein the altitude observed quantity switching mechanism is specifically: Step S51, presetting a height switching threshold; S52, judging whether the relative height is smaller than the height switching threshold, if yes, marking the position 1 of the relative height, and if not, marking the position 0 of the relative height, wherein the position 1 and the position 0 are assigned values; And step S53, judging whether the relative height mark position jumps or not, if so, recording the current relative height, absolute height and the height of the aircraft, and if not, returning to the step S52.
  7. 7. A system for estimating a strong immunity altitude state of an aircraft, the system comprising: the acquisition module is used for acquiring data of the sensor, estimating the time delay of the sensor, and aligning the time delay of the sensor to obtain the observed quantity of the sensor; The estimation module is used for estimating the error variance of the sensor according to the observed quantity of the sensor; the differential calculation module is used for calculating the secondary differential of the observed quantity of the sensor and calculating the correlation degree between the secondary differential and the inertial measurement acceleration; the height calculation module is used for carrying out data weighted fusion on the observed quantity of the sensor and the correlation degree and calculating the relative height of the aircraft from the ground; the processing module is used for acquiring the current absolute height, respectively carrying out filtering processing on the relative height and the absolute height by using a third-order Kalman filtering algorithm, then adopting a height observed quantity switching mechanism to switch the height observed quantity to calculate the height of the aircraft, and finally estimating the ground clearance of the aircraft by using a first-order Kalman filtering algorithm; the differential calculation module is further configured to: According to the data at the current moment being z (0), the historical data being z (k), k=1, 2..n-1, and calculating a differential of the observed quantity of the sensor as follows: ...(8); Re-recording the sequence value of the first derivative of the observed quantity of the sensor (K) K=1, 2,..n-1, and calculating the second derivative of the sensor observations: ...(9); Recording a secondary differential sequence value of the observed quantity of the sensor, and calculating and recording an order value of the upward acceleration under an inertial coordinate system: (k),k=1,2,...n-1 ...(10); , ...(11); ...(12); Wherein, the Is the value of the three axes of the accelerometer, And finally, calculating a correlation function g (X) of the observed quantity secondary differential and the unmanned aerial vehicle antenna acceleration: The correlation function ...(13); The calculation method of the relative height comprises the following steps: Presetting an aircraft to carry n ranging sensors, wherein the observed noise variance of each ranging sensor is as follows The correlation coefficient of the quadratic differential term of each ranging sensor and the tangential acceleration is The observed quantity of each ranging sensor is Wherein i=1, 2,..n, n is a positive integer; Weighting coefficients of each of the ranging sensors I=1, 2,..n, calculated as follows: Order the ... (14); Then ...(15); And carrying out weighted fusion on the observed quantity, and calculating the relative height r_H of the aircraft from the ground: ...(16)。

Description

Method and system for estimating strong disturbance rejection altitude state of aircraft Technical Field The invention relates to the technical field of aircrafts, in particular to a method and a system for estimating a strong disturbance rejection altitude state of an aircraft. Background At present, unmanned aerial vehicles with various configurations have been widely applied in fields such as reconnaissance, rescue, plant protection and the like because of rapid development in the field of microelectronics. In the design of the unmanned aerial vehicle flight control system, the unmanned aerial vehicle flight control system mainly comprises two aspects of unmanned aerial vehicle motion control and system state estimation, wherein the system state estimation is to estimate state quantities such as the position, the speed, the attitude angle and the angular speed of the unmanned aerial vehicle by using a sensor, and the calculated precision of the unmanned aerial vehicle motion state directly influences the stability and the safety of the unmanned aerial vehicle flight. In this regard, there have been many aircraft controller developers developing methods for aircraft altitude state estimation, APM flight control using 3-order complementary filtering algorithms to estimate the altitude state of an aircraft. PX4 flight control uses an extended kalman filter algorithm to estimate the altitude state of an aircraft. However, the aircraft altitude state estimation method used by the aircraft only considers the condition that the sensor works normally, and is insufficient in terms of sensor fault detection, when the sensor breaks down and sensor data jump, if no special processing is performed, the altitude and the speed of the aircraft are estimated erroneously, and the aircraft is easy to run away, and secondly, the state estimation algorithm used by PX4 flight control comprises a 24-dimensional state equation, so that the integration level is high, distributed calculation is not facilitated, and the aircraft altitude state estimation effect is poor. Disclosure of Invention Aiming at the defects of the related technology, the invention provides a strong disturbance rejection altitude state estimation method of an aircraft, which is used for solving the problems of easy runaway, poor reliability and poor stability when the existing aircraft flies. In order to solve the technical problems, an embodiment of the present invention provides a method for estimating a strong noise immunity altitude state of an aircraft, the method including the following steps: S1, acquiring data of a sensor, estimating the time delay of the sensor, and aligning the time delay of the sensor to obtain the observed quantity of the sensor; S2, estimating the error variance of the sensor according to the observed quantity of the sensor; S3, calculating a secondary differential of the observed quantity of the sensor, and calculating the correlation degree between the secondary differential and the inertial measurement acceleration; S4, carrying out data weighted fusion on the observed quantity of the sensor and the correlation degree, and calculating the relative height of the aircraft from the ground; And S5, acquiring the current absolute height, respectively carrying out filtering treatment on the relative height and the absolute height by using a third-order Kalman filtering algorithm, switching the altitude observed quantity by using an altitude observed quantity switching mechanism, calculating the altitude of the aircraft, and finally estimating the ground clearance of the aircraft by using a first-order Kalman filtering algorithm. Preferably, in the step S1, the time delay of the sensor indicates that a predetermined time is required for processing and transmitting the data after the sensor collects the data, and a time difference between the data received by the processor and the actual data is obtained. Preferably, the data defining the current time of output of a certain sensor is z (k), and the history data of output of the sensor is z (k+1), z (k+2), z (k+n), where n is an integer, the data aligned by the sensor delay is the sensor observed quantity, the sensor observed quantity is represented by expression (1), and the sensor delay is represented by expression (2): Zdelay=z(k+Td)...(1); Td=Tdelay/ΔT...(2); Wherein T delay is the time delay of the sensor with respect to the time delay maximum, and DeltaT is the sampling period of the sensor. Preferably, the step S2 specifically includes the following substeps: Setting the data at the current moment as z (0) and the historical data as z (k), wherein k=1, 2,..n; Estimating an actual distance measured by the sensor, the actual distance being represented by expression (3): Yest(k)=b+ka,k=0,1,2...n...(3); Calculating an error of the sensor, the error being represented by expression (4): Err(k)=Yest(k)-Z(k),k=0,1,2...n...(4); The variance of the error is calculated and taken into the