CN112212860A - Distributed filtering micro-nano satellite attitude determination method with fault tolerance - Google Patents
Distributed filtering micro-nano satellite attitude determination method with fault tolerance Download PDFInfo
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Abstract
According to the distributed filtering micro-nano satellite attitude determination method with fault tolerance, a fault detection and processing link is added based on the traditional Federal Kalman filtering algorithm, and the integral attitude determination method is enabled to have fault tolerance performance by adding fault detection factors and deducing given fault thresholds, so that the sensor fault type is effectively discriminated, and the attitude determination precision of the system is improved. Measurement residual using attitude sensorSetting a fault detection factorFault detection factorThe following formula is satisfied:wherein trace (·) represents the trace operation of matrix calculation; l is the dimension of the measuring vector of the sensor in the local filter;an observed noise variance matrix representing a local filter; (.)TRepresents a transpose of a matrix; fault threshold gamma0Take a value of
Description
Technical Field
The invention relates to a distributed filtering micro-nano satellite attitude determination method with fault tolerance, and belongs to the technical field of satellite attitude determination and control.
Background
With the rapid development of the domestic microelectronic technology and chip process design technology, the satellite attitude determination and control level is increasingly improved. The attitude determination and control technology is a basic guarantee for normal in-orbit work of a satellite platform, and is used for dynamically adjusting and controlling the in-orbit attitude of a satellite according to coordinate information of the satellite relative to a certain reference coordinate system (such as an orbit coordinate system or an inertia coordinate system) so as to improve the accuracy and efficiency of data transmission. The attitude determination and control system generally comprises an on-board brain (spaceborne computer), an attitude sensor and an attitude determination algorithm, wherein the accuracy of the result of the attitude determination algorithm directly influences the accuracy of satellite attitude control and adjustment, so that the improvement of the accuracy of the satellite attitude determination algorithm is always the core subject of the attitude determination and control field.
Due to the limitations of mass, volume and power consumption, micro-nano satellites commonly adopt miniaturized attitude sensors, such as MEMS gyroscopes, magnetometers, micro sun sensors and the like. Although the miniaturized attitude sensor is light and small and has low power consumption, the single sensor has the problems of incapability of determining the attitude, low accuracy of acquiring attitude information and the like. The conventional general improvement mode is to combine a micro attitude sensor with a multi-source information fusion algorithm to expect to acquire attitude information with higher precision. However, the multi-source information fusion algorithm such as the federal kalman filter only provides a distributed information fusion structure, and does not consider the detection and processing link of sensor faults, so that a corresponding fault-tolerant link is lacked. On the premise that the sensor attitude determination and attitude information acquisition precision is not high, targeted fault detection and isolation cannot be realized, sufficient compensation for satellite attitude adjustment cannot be provided through fault analysis, and the conventional attitude determination and control system needs to be further improved.
In view of this, the present patent application is specifically proposed.
Disclosure of Invention
The method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance aims to solve the problems in the prior art, and a fault detection and processing link is added based on the traditional Federal Kalman filtering algorithm, namely a fault detection factor and a fault threshold given by derivation are added, so that the integral attitude determination method has fault tolerance, the sensor fault type is effectively discriminated, and the attitude determination precision of the system is improved.
In order to achieve the above design objective, the present application proposes the following solutions:
a distributed filtering micro-nano satellite attitude determination method with fault tolerance is based on a distributed Federal Kalman filtering algorithm and utilizes a measurement value residual error of an attitude sensorSetting a fault detection factorFailure detection factorThe following formula is satisfied:
wherein: trace (·) represents the trace operation of the matrix; l is the dimension of the measuring vector of the sensor in the local filter;an observed noise variance matrix representing a local filter; (.)TRepresents a transpose of a matrix;
Further, the method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance comprises the following steps:
1) acquiring the angular velocity omega of the micro-nano satellite body coordinate system b relative to the inertial coordinate system i by using the measurement value of the MEMS gyroscopemFor estimation of angular velocity;
2) the mounting shaft of the magnetometer is consistent with the coordinate system of the micro-nano satellite body, and the geomagnetic field vector measurement value B under the coordinate system of the satellite body is obtainedb;
3) The aiming axis of the sun sensor is consistent with the coordinate system-Y axis of the micro-nano satellite body to obtain the sun vector measurement value S under the coordinate system of the satellite bodyb;
4) Selecting an error quaternion Δ qboThe vector component Δ q and the gyro angular rate drift estimation error Δ b of (1) are state quantitiesGiving a state equation of a satellite attitude determination system;
5) utilizing the geomagnetic field vector under the satellite body coordinate system obtained in the step 2)Measured value BbAnd the sun vector measurement value S under the satellite body coordinate system obtained in the step 3)bCombined with the reference value B of the earth magnetic field vector in the orbital coordinate systemoAnd the sun vector reference value S under the orbital coordinate systemoProviding observation information of two local filters in a distributed Federal Kalman filtering algorithm;
6) and calculating to obtain the attitude of the micro-nano satellite by utilizing a distributed Federal Kalman filtering algorithm, and realizing satellite attitude calculation based on a plurality of low-cost, small-volume and low-power consumption micro sensors.
Further, the fault detection and processing and state fusion process is implemented according to the following steps:
(1) fault detection and handling
Setting a fault detection factorFault threshold gamma of0Namely:when the sensor is normal;when the sensor fails, the sensor fails;
based on the fault detection factor and the fault threshold, when the sensor is detected to be in fault, if the local filter S1 is failed, the local filter S1 is failedIs incorrect and thus not input to the main filter, at which point the overall estimate of the system error state may be modified toSimilarly, if the local filter S2 fails, the overall estimate of the system error state is
When the sensor is not detected to have faults, directly executing the following step (2);
(2) state fusion
State estimation for local filters S1 and S2Sum estimation error covariance matrixPerforming data fusion to obtain final state estimationSum estimation error covariance matrix Pg,k;
In conclusion, the method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance has the following advantages:
1. through the fault tolerance link provided by the application, the accuracy of the sensor for acquiring the attitude information of the micro-nano satellite can be improved, and the integral fault tolerance performance of the attitude determination system can be improved by detecting and processing the fault of the sensor.
2. A fault detection factor is innovatively provided, a value reference and a strict mathematical analysis process are provided for a fault threshold, and a large amount of data debugging work in engineering application is remarkably reduced, so that the efficient design of the fault-tolerant attitude determination method is realized.
3. When the satellite is at the end of the service life, if the attitude sensor fails (such as failure of a magnetometer), the existing attitude determination system can still normally acquire and adjust attitude information based on the application, and accordingly the service life of the satellite is prolonged.
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The following drawings are illustrative of specific embodiments of the present application.
FIG. 1 is a schematic diagram of a prior art multi-source information distributed fusion pose determination method;
FIG. 2 is a schematic diagram of a distributed Federal Kalman filtering algorithm based on fault tolerance described in the present application;
FIG. 3 is a flow chart of a fault detection and handling process described herein;
FIG. 4 is a waveform of satellite attitude information determined based on the fault tolerant distributed filtering algorithm described herein when the local filter sensor is fault-free;
FIG. 5 is a waveform of satellite angular velocity information determined based on the fault tolerant distributed filtering algorithm described herein when the local filter sensor is fault-free;
FIG. 6 is a waveform diagram of satellite roll angle information determined based on the fault tolerant distributed filtering algorithm described herein when a local filter magnetometer or sun sensor fails;
FIG. 7 is a waveform diagram of satellite pitch angle information determined based on the fault-tolerant distributed filtering algorithm described in the present application when a local filter magnetometer or sun sensor fails;
FIG. 8 is a waveform diagram of satellite yaw angle information determined based on the fault tolerant distributed filtering algorithm described herein when a local filter magnetometer or sun sensor fails.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings.
As shown in fig. 1 and 2, the existing and improved distributed fusion micro-nano satellite attitude determination methods are based on the existing multi-source information distributed fusion structure, an MEMS gyroscope, a magnetometer and a sun sensor are used as attitude sensors, and the distributed federal kalman filtering algorithm is composed of two local filters and a main filter.
The local filter S1 and the local filter S2 respectively complete the measurement updating of the estimated error covariance and the state of the magnetometer and the sun sensor according to the feedback attitude and the error covariance of the adaptive distribution; the state variables of the two local filters are the same, and the two local filters perform parallel operation.
The main filter realizes the prediction of the attitude quaternion, the one-step prediction of the state and the covariance and the information distribution, detects the sensor fault according to the fault detection factor or performs state fusion according to the output information of the local filter, and corrects and finally outputs the attitude and the gyro drift.
As shown in fig. 2, the method for determining the attitude of the distributed filtering micro/nano satellite with fault tolerance according to the present application can complete satellite attitude determination and detect and process sensor faults at the same time, and includes the following implementation steps:
1) acquiring the angular velocity omega of the micro-nano satellite body coordinate system b relative to the inertial coordinate system i by using the measurement value of the MEMS gyroscopemFor estimation of angular velocity;
2) when the mounting shaft of the magnetometer is consistent with the coordinate system of the micro-nano satellite body, the geomagnetic field vector measurement value B under the coordinate system of the satellite body is obtainedb;
3) The aiming axis of the sun sensor is consistent with the coordinate system-Y axis of the micro-nano satellite body to obtain the sun vector measurement value S under the coordinate system of the satellite bodyb;
4) Selection error quaternion Δ qboThe vector component Δ q and the gyro angular rate drift estimation error Δ b of (1) are state quantitiesThe discrete state equation for the satellite attitude determination system is given as:
Xk=Φk,k-1Xk-1+Γk,k-1Wk-1
wherein,t is the calculation step size,ωbiis the angular velocity vector, I, of the satellite body coordinate system relative to the Earth's center inertial coordinate system3×3The gyro angular velocity random walk noise is a 3 x 3-order unit matrix, upsilon is gyro angular velocity random walk noise, zeta is gyro angular velocity random walk noise, k represents the kth time, and k-1 represents the kth time.
5) Utilizing the geomagnetic field vector measurement value B under the satellite body coordinate system obtained in the step 2)bAnd the sun vector measurement value S under the satellite body coordinate system obtained in the step 3)bCombined with the reference value B of the earth magnetic field vector in the orbital coordinate systemoAnd the sun vector reference value S under the orbital coordinate systemoAnd providing observation information of two local filters in the distributed Federal Kalman filtering algorithm:
the observed information of the local filter S1 is
Z1k=(Bb(k)-Bb(k/k-1))/2
Wherein, Bb(k)The measured value of the geomagnetic field vector under a satellite body coordinate system at the moment k is obtained; b isb(k/k-1)=Tbo(qbo(k/k-1))Bo(k)The earth magnetic field vector under the body coordinate system is calculated according to the attitude estimation value;
Bo(k)a ground magnetic field vector of a satellite orbit coordinate system at the moment k; q. q.sbo(k/k-1)Is the predicted value of the attitude four-element number from the time k-1 to the time k.
The local filter S2 observes the information as
Z2k=(Sb(k)-Sb(k/k-1))/2
Wherein S isb(k)The measured value of the sun vector is the measured value of the satellite body coordinate system at the moment k; sb(k/k-1)=Tbo(qbo(k/k-1))So(k)The sun vector under the body coordinate system calculated according to the attitude estimation value; so(k)The solar vector is the solar vector under the satellite orbit coordinate system at the moment k.
6) And calculating to obtain the attitude of the micro-nano satellite by utilizing a distributed Federal Kalman filtering algorithm, and realizing satellite attitude calculation based on a plurality of low-cost, small-volume and low-power consumption micro sensors.
As shown in fig. 3, the present application provides an improvement of a distributed fusion micro-nano satellite attitude determination method, namely, a fault detection and processing link is added. The method for determining the attitude of the distributed Federal Kalman filtering micro-nano satellite with fault tolerance is implemented according to the following steps:
(1) prediction of attitude quaternion
Predicting attitude quaternion by using RungeKutta equation according to attitude kinematics equation and estimated angular velocity
Quaternion estimation from attitude at time k-1And estimating angular velocityPredicting the angular speed of the body coordinate system relative to the track coordinate system at the moment k as follows:
according to the kinematic equation of satellite attitude, adopting RungeKutta equation to predict attitude quaternion at k momentIs composed of
Wherein k is1、k2、k3And k4Intermediate variables for the longge-kuta method.
(2) One step prediction of state and covariance
According to the discrete state information, the state estimation value is estimated from the k-1 timeOne step state prediction for calculating time kSum estimation error covariance matrix Pg,k/k-1;
Wherein, Pg,k-1An estimation error covariance matrix at the time of k-1; q is the process noise variance matrix of the main filter.
(3) Information distribution
Information distribution factor alpha of main filteriAnd resets the estimation error covariance matrix to the local filter S1 and the local filter S2
Calculating an information distribution factor alphaiThe following were used:
wherein, is the error covariance matrix of the local filter i,is a matrixThe trace of (c). Resetting the estimation error covariance matrix to the local filter S1 and the local filter S2Therefore, the temperature of the molten steel is controlled,
(4) and updating the measurement
Each local filter carries out measurement updating according to the observation information of the magnetometer and the sun sensor respectively, and the local filter Kalman gain at the moment k is calculatedLocal filter state estimationAnd error covariance matrix
Wherein,an observation matrix representing the local filter at time k;and ZikRespectively representing an observation noise variance matrix and observation information of a local filter;
(5) fault detection and handling and state fusion
And detecting the sensor fault according to the fault detection factor or performing state fusion according to the output information of the local filter.
(5.1) Fault detection and handling
Based on distributed Federal Kalman filtering algorithm, utilizing measurement value residual error of attitude sensorSetting a fault detection factorTo detect attitude to determine if the system has a sudden fault or a slowly accumulating fault.
wherein: trace (·) represents the trace operation of the matrix; l is the dimension of the measuring vector of the sensor in the local filter;an observed noise variance matrix representing a local filter; (.)TRepresenting the transpose of the matrix.
In conjunction with FIG. 2, thenAndnamely the fault detection factors of the local filter S1, the local filter S2.
When the attitude sensor has no fault, the corresponding measurement value residual errorShould be oneAnd the noise is very small, and theoretically meets the characteristics of zero mean Gaussian white noise. When the attitude sensor has sudden change fault or slowly changing fault accumulated to a certain degree, the residual error of the measured valueWill become significantly larger, applying the above described fault detection factorThe calculation result can directly judge whether the sensor in the attitude determination system has a fault.
Therefore, the following failure detection factor needs to be setFault threshold gamma of0Namely:the sensor is normal;when the sensor fails, the sensor fails.
For fault threshold gamma0When γ is analyzed0When a smaller value is set, fault detection is easier to be carried out on the attitude determination system, but error judgment is also easier to be caused; when gamma is0With a larger value set, it is relatively difficult to detect a fault.
The present application proposes the following fault threshold γ0And the calculation process comprises the following steps:
assuming that the dimension of the attitude sensor measurement vector of a certain local filter Si is l,
wherein,Las vectorsThe elements of (1); sigma(i)The standard deviation of the measurement noise of the sensor in the local filter Si.
Due to the order of l matrixThe sum of each element on the main diagonal is the trace of the matrix, then there is
Wherein,measuring residual error delta in one dimension when the sensor is normal(i)The information of (a); d is a single-dimensional measurement residual error delta when the sensor fails(i)The information of (1).
Further, from the above plurality of publicationsDerived from the formula, the fault detection factorStandard deviation sigma of sensor measuring noise in local filter Si(i)Are closely related.
As described above, when the attitude sensor is not faulty, the measurement residualSimilar to the noise measured by the sensor, the sensor meets the characteristics of zero-mean Gaussian white noise. Typically, the upper bound on the measurement noise of the attitude sensor is taken to be 3 σ(i)Therefore, the residual error of one-dimensional measurement value when the attitude sensor in the local filter Si is not in faultThe upper limit of (3 a) may be taken to be 3 a(i)I.e. byWhen the attitude sensor has no fault, | d | -, 0,so that the fault threshold gamma0Is taken as
Based on the fault detection factor and the fault threshold, when the sensor is detected to be in fault, if the local filter S1 is failed, the local filter S1 is failedIs incorrect and thus not input to the main filter, at which point the overall estimate of the system error state may be modified toSimilarly, if the local filter S2 fails, the overall estimate of the system error state is
When the sensor is not detected to be in fault, the following step (5.2) is directly executed.
(5.2) State fusion
State estimation for local filters S1 and S2Sum estimation error covariance matrixPerforming data fusion to obtain final state estimationSum estimation error covariance matrix Pg,k;
(6) Attitude and gyro drift correction
Using state estimationModifying estimated attitude quaternionAnd random drift estimateAnd combined with the measured value omega of the MEMS gyroscopemObtaining the final estimation attitude quaternionAnd estimating angular velocity
The following simulation experiment results are given in fig. 4 to 8 to verify the accuracy and validity of the fault detection and state fusion.
Taking a micro-nano satellite as an example, the micro-nano satellite runs on a sun synchronous orbit with the orbit height of 520km, and the rotational inertia of the satellite is diag [ 0.088450.14220.07518 ]]kg·m2The descending intersection point is 7:30am, and the track inclination angle is 97.62 degrees. When the satellite is stable on the three axes of the earth, the expected attitude is [0,0 ]]Angle random walk sigma of MEMS gyroυ=0.03°/s12Angular velocity random walk σζ=0.0001°/s32The measuring noise of the magnetometer is 100nT, and the measuring noise of the sun sensor is 0.1 degree. The distributed filtering micro-nano satellite attitude determination method for fault tolerance is utilized to carry out simulation experiments. When the local filter sensor has no fault, the attitude information of the micro/nano satellite determined based on the improved distributed filtering algorithm is shown in fig. 4; when the local filter sensor has no fault, the angular velocity information of the micro-nano satellite determined based on the improved distributed filtering algorithm provided by the application is shown in fig. 5. When a local filter magnetometer fails or a sun sensor fails, the fault tolerant sub-system based on the present applicationThe roll angle, pitch angle and yaw angle information of the micro-nano satellite determined by the distributed filter algorithm are respectively shown in fig. 6, fig. 7 and fig. 8.
As can be seen from fig. 4 to 5, when the local filter sensor has no fault, the attitude angle and the angular velocity of the micro/nano satellite estimated based on the distributed filtering algorithm of the present application have higher estimation accuracy. As can be seen from fig. 6 to 8, when the sun sensor of the local filter fails, the attitude angle of the micro/nano satellite determined based on the fault-tolerant distributed filtering algorithm of the present application is within a range of ± 0.5 °; when the local filter magnetometer has a fault, the rolling angle and the yaw angle of the micro-nano satellite determined based on the distributed filtering algorithm of fault tolerance are in a range of +/-0.4 degrees, and the estimated pitch angle is in a range of +/-1.25 degrees. According to comparison of all simulation results, the distributed filtering micro-nano satellite attitude determination method with fault tolerance has high practicability and good fault tolerance.
In summary, the embodiments presented in connection with the figures are only preferred. Those skilled in the art can derive other alternative structures according to the design concept of the present invention, and the alternative structures also fall within the scope of the present invention.
Claims (3)
1. A distributed filtering micro-nano satellite attitude determination method with fault tolerance is characterized by comprising the following steps: based on distributed Federal Kalman filtering algorithm, utilizing measurement value residual error of attitude sensorSetting a fault detection factorFault detection factorThe following formula is satisfied:
wherein: trace (·) represents the trace operation of the matrix; l is the dimension of the measuring vector of the sensor in the local filter;an observed noise variance matrix representing a local filter; (.)TRepresents a transpose of a matrix;
2. The method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance according to claim 1, wherein the method comprises the following steps: comprises the following implementation steps of the following steps of,
1) acquiring the angular velocity omega of the micro-nano satellite body coordinate system b relative to the inertial coordinate system i by using the measurement value of the MEMS gyroscopemFor estimation of angular velocity;
2) the mounting shaft of the magnetometer is consistent with the coordinate system of the micro-nano satellite body, and the geomagnetic field vector measurement value B under the coordinate system of the satellite body is obtainedb;
3) The aiming axis of the sun sensor is consistent with the coordinate system-Y axis of the micro-nano satellite body to obtain the sun vector measurement value S under the satellite body coordinate systemb;
4) Selecting an error quaternion Δ qboThe vector component Δ q and the gyro angular rate drift estimation error Δ b of (1) are state quantitiesGiving a state equation of a satellite attitude determination system;
5) utilizing the geomagnetic field vector measurement value B under the satellite body coordinate system obtained in the step 2)bAnd the sun vector measurement value S under the satellite body coordinate system obtained in the step 3)bCombined with the earth-magnetic field vector in the orbital coordinate systemQuantity reference value BoAnd the sun vector reference value S under the orbital coordinate systemoProviding observation information of two local filters in a distributed Federal Kalman filtering algorithm;
6) and calculating to obtain the attitude of the micro-nano satellite by utilizing a distributed Federal Kalman filtering algorithm, and realizing satellite attitude calculation based on a plurality of low-cost, small-volume and low-power consumption micro sensors.
3. The method for determining the attitude of the distributed filtering micro-nano satellite with fault tolerance according to claim 2, wherein the method comprises the following steps: the fault detection and processing and state fusion process is implemented as follows,
(1) fault detection and handling
Setting a fault detection factorFault threshold gamma of0Namely:when the sensor is normal;when the sensor fails, the sensor fails;
based on the fault detection factor and the fault threshold, when the sensor is detected to be in fault, if the local filter S1 is failed, the local filter S1 is failedIs incorrect and thus not input to the main filter, at which point the overall estimate of the system error state may be modified toSimilarly, if the local filter S2 fails, the overall estimate of the system error state is
When the sensor is not detected to have faults, directly executing the following step (2);
(2) state fusion
State estimation for local filters S1 and S2Sum estimation error covariance matrixPerforming data fusion to obtain final state estimationSum estimation error covariance matrix Pg,k;
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