CN110440795A - A kind of Angular Acceleration Estimation based on Kalman filtering - Google Patents
A kind of Angular Acceleration Estimation based on Kalman filtering Download PDFInfo
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- CN110440795A CN110440795A CN201910693265.7A CN201910693265A CN110440795A CN 110440795 A CN110440795 A CN 110440795A CN 201910693265 A CN201910693265 A CN 201910693265A CN 110440795 A CN110440795 A CN 110440795A
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- angular acceleration
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
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- General Physics & Mathematics (AREA)
- Feedback Control In General (AREA)
- Navigation (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a kind of Angular Acceleration Estimations based on Kalman filtering, it is resolved for solving increment dynamic inversion control (INDI) using Angular Acceleration Feedback, and the problem of generally flying control hardware and angular speed is measured using Inertial Measurement Unit, angular acceleration is directly measured without angular acceleration transducer.The angular acceleration noise directly got using angular velocity difference is excessive, it is impossible to be used in the resolving of control law, it is therefore desirable to estimate angular acceleration signal using the signal of sensor.Conventional method is that angular velocity signal directly carries out difference low-pass filtering again later, but can bring additional time delay in this way.It is provided by the invention to carry out angular acceleration estimation with Kalman filtering and remove high-frequency noise and guarantee lesser delay.The algorithm is because relating to Practical Project background, and simple and convenient easy to implement, engineering application value with higher.
Description
Technical field
The present invention and flying vehicles control field more particularly to a kind of aircraft Angular Acceleration Estimation.
Background technique
Increment dynamic inversion control (INDI) is a kind of robust nonlinear control method insensitive for model parameter, the party
Method is resolved using Angular Acceleration Feedback, and is generally flown control hardware and measured angular speed using Inertial Measurement Unit, without angle plus
Velocity sensor directly measures angular acceleration.
The angular acceleration noise directly got using angular velocity difference is excessive, it is impossible to be used in the resolving of control law, therefore need
Angular acceleration signal is estimated using the signal of sensor.Conventional method is after angular velocity signal directly carries out difference
Low-pass filtering again, but additional time delay can be brought in this way.
Summary of the invention
The present invention provides a kind of Angular Acceleration Estimations based on Kalman filtering, improve based on increment dynamic inverse
The stability of the control of algorithm.
A kind of Angular Acceleration Estimation based on Kalman filtering, for angular acceleration in increment dynamic inversion control
It resolves, mainly comprises the following steps:
S1: establishing Kalman filter model, and quantity of state isRespectively angular speed, angular acceleration and angle accelerate
The derivative of degree, the state equation and observational equation of aircraft are as follows:
zk+1=[1 0 0] xk+1+ R, H=[1 0 0]
Δ T is the sampling period, and z is observed quantity, and observed quantity is rate of pitch, and source is the angular speed of IMU.Q and R were respectively
Journey noise and observation noise, state error covariance is defined as:
S2: filtering estimation procedure: a step state updates, and the quantity of state of next sampling instant is gone out according to solving kinematic equation
S3: it is further updated, is obtained according to upper sampling instant state error covariance
S4: according toAnd H, solve kalman gain；
S5: attitude angle movement-state and covariance correction are carried out.
Further, the quantity of state of next sampling instant
Further, the state covariance more new calculation method is
Further, the solution kalman gain formula is
Further, the attitude angle movement-state and covariance correction representation method are as follows:
Compared with prior art, the invention has the advantages that: utilize Kalman filtering, pass through and choose motion model appropriate, In
Guarantee that excessive delay will not be brought while removal high-frequency noise, there is higher engineering practical value.
Detailed description of the invention
Attached drawing illustrates the illustrative embodiments of the disclosure, and it is bright together for explaining the principles of this disclosure,
Which includes these attached drawings to provide further understanding of the disclosure, and attached drawing includes in the description and constituting this theory
A part of bright book.
Fig. 1 is according to the Kalman filtering Angular Acceleration Estimation of the disclosure at least one embodiment and direct differential+low
The Angular Acceleration Estimation estimated result comparison diagram of pass filter.
Fig. 2 is the two methods estimated result partial enlarged view according at least one embodiment of the disclosure.
Specific embodiment
The disclosure is described further with reference to the accompanying drawings and detailed description.It is understood that this place is retouched
The specific embodiment stated is only used for explaining related content, rather than the restriction to the disclosure.It also should be noted that in order to
Convenient for description, part relevant to the disclosure is only illustrated in attached drawing.
A kind of Angular Acceleration Estimation based on Kalman filtering, for angular acceleration in increment dynamic inversion control
It resolves, mainly comprises the following steps:
S1: establishing Kalman filter model, and quantity of state isRespectively angular speed, angular acceleration and angle accelerate
The derivative of degree, the state equation and observational equation of aircraft are as follows:
zk+1=[1 0 0] xk+1+ R, H=[1 0 0]
Δ T is the sampling period, and z is observed quantity, and observed quantity is rate of pitch, and source is the angular speed of IMU.Q and R were respectively
Journey noise and observation noise；
S2: filtering estimation procedure: a step state updates, and the quantity of state of next sampling instant is gone out according to solving kinematic equation
S3: it is further updated, is obtained according to upper sampling instant state error covariance
S4: according toAnd H, solve kalman gain；
S5: attitude angle movement-state and covariance correction are carried out.
Further, the quantity of state of next sampling instant
Further, the state covariance more new calculation method is
Further, the solution kalman gain formula is
Further, the attitude angle movement-state and covariance correction representation method are as follows:
Further, to verify improved differential evolution algorithm a possibility that, below by taking the estimation of pitch acceleration as an example,
By estimated result to further illustrate.
Model built is linear model, and quantity of state is
The respectively derivative of rate of pitch, pitching angular acceleration and pitching angular acceleration, state equation and
Observational equation is as follows
zk+1=[1 0 0] xk+1+ R, H=[1 0 0]
Δ T is the sampling period, and z is observed quantity, and observed quantity is rate of pitch, and source is the angular speed of IMU.Q and R were respectively
Journey noise and observation noise.
Filter estimation procedure:
1) a step state updates, and the quantity of state of next sampling instant is gone out according to aircraft pitch angular movement equation solution
2) a step update is carried out according to upper sampling instant state error covariance
3) kalman gain is solved
4) pitch movement quantity of state and covariance correction are carried out
Fig. 1 is to be estimated using Kalman filtering Angular Acceleration Estimation and direct differential+low-pass filtering angular acceleration
Method compares the estimated result of pitching angular acceleration, and Fig. 2 is the estimated result partial enlarged view of two methods, it is seen then that diagonal
Speed signal, which carries out direct differential, to amplify noise, add low-pass filtering that can then bring certain delay using traditional difference,
And high-frequency noise can be removed and guarantee lesser delay by carrying out angular acceleration estimation with Kalman filtering.
Claims (5)
1. a kind of Angular Acceleration Estimation based on Kalman filtering, the solution for angular acceleration in increment dynamic inversion control
It calculates, which is characterized in that comprise the following steps:
S1: establishing Kalman filter model, and quantity of state isRespectively angular speed, angular acceleration and angle accelerate
The derivative of degree, the state equation and observational equation of aircraft are as follows:
zk+1=[1 0 0] xk+1+ R, H=[1 0 0]
Δ T is the sampling period, and z is observed quantity, and observed quantity is rate of pitch, and source is the angular speed of IMU.Q and R were respectively
Journey noise and observation noise；
S2: filtering estimation procedure: a step state updates, and the quantity of state of next sampling instant is gone out according to solving kinematic equation
S3: it is further updated, is obtained according to upper sampling instant state error covariance
S4: according toAnd H, solve kalman gain；
S5: attitude angle movement-state and covariance correction are carried out.
2. a kind of Angular Acceleration Estimation based on Kalman filtering according to claim 1, it is characterised in that: described
The quantity of state of next sampling instant
3. a kind of Angular Acceleration Estimation based on Kalman filtering according to claim 1, it is characterised in that: described
State covariance more new calculation method is
4. a kind of Angular Acceleration Estimation based on Kalman filtering according to claim 1, it is characterised in that: described
Solving kalman gain formula is
5. a kind of Angular Acceleration Estimation based on Kalman filtering according to claim 1, it is characterised in that: described
Attitude angle movement-state and covariance correction representation method are as follows:
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111523076A (en) * | 2020-03-24 | 2020-08-11 | 中国人民解放军军事科学院评估论证研究中心 | Method, device and system for calculating angular acceleration based on Fal function |
| CN113961012A (en) * | 2021-09-24 | 2022-01-21 | 中国航空工业集团公司沈阳飞机设计研究所 | Incremental dynamic inverse control method based on EKF filtering noise reduction |
| CN114323011A (en) * | 2022-01-05 | 2022-04-12 | 中国兵器工业计算机应用技术研究所 | Kalman filtering method suitable for relative pose measurement |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1903306A2 (en) * | 2006-09-19 | 2008-03-26 | Alpine Electronics, Inc. | Method and system for estimating ground vehicle dynamics based on an integrated MEMS-INS/GPS navigation system |
| CN104777747A (en) * | 2014-01-15 | 2015-07-15 | 中国人民解放军海军工程大学 | Sliding mode control method for opto-electronic tracking turntable based on square root volume kalman wave filtering |
| CN108919841A (en) * | 2018-08-24 | 2018-11-30 | 湖北三江航天红峰控制有限公司 | A kind of compound heavy metal method and system of photoelectric follow-up |
-
2019
- 2019-07-30 CN CN201910693265.7A patent/CN110440795A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1903306A2 (en) * | 2006-09-19 | 2008-03-26 | Alpine Electronics, Inc. | Method and system for estimating ground vehicle dynamics based on an integrated MEMS-INS/GPS navigation system |
| CN104777747A (en) * | 2014-01-15 | 2015-07-15 | 中国人民解放军海军工程大学 | Sliding mode control method for opto-electronic tracking turntable based on square root volume kalman wave filtering |
| CN108919841A (en) * | 2018-08-24 | 2018-11-30 | 湖北三江航天红峰控制有限公司 | A kind of compound heavy metal method and system of photoelectric follow-up |
Non-Patent Citations (3)
| Title |
|---|
| 尹航，等: "基于Kalman预报观测器的增量动态逆控制", 《清华大学学报(自然科学版)》 * |
| 杨露菁，等: "《多源信息融合理论与应用》", 28 February 2006 * |
| 邵雷，等: "一种基于非线性跟踪-微分器的角加速度估计方法", 《飞行力学》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111523076A (en) * | 2020-03-24 | 2020-08-11 | 中国人民解放军军事科学院评估论证研究中心 | Method, device and system for calculating angular acceleration based on Fal function |
| CN111523076B (en) * | 2020-03-24 | 2021-04-02 | 中国人民解放军军事科学院评估论证研究中心 | Method, device and system for calculating angular acceleration based on Fal function |
| CN113961012A (en) * | 2021-09-24 | 2022-01-21 | 中国航空工业集团公司沈阳飞机设计研究所 | Incremental dynamic inverse control method based on EKF filtering noise reduction |
| CN113961012B (en) * | 2021-09-24 | 2023-09-22 | 中国航空工业集团公司沈阳飞机设计研究所 | Incremental dynamic inverse control method based on EKF filtering noise reduction |
| CN114323011A (en) * | 2022-01-05 | 2022-04-12 | 中国兵器工业计算机应用技术研究所 | Kalman filtering method suitable for relative pose measurement |
| CN114323011B (en) * | 2022-01-05 | 2024-04-23 | 中国兵器工业计算机应用技术研究所 | Kalman filtering method suitable for relative pose measurement |
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