CN113961012B - Incremental dynamic inverse control method based on EKF filtering noise reduction - Google Patents
Incremental dynamic inverse control method based on EKF filtering noise reduction Download PDFInfo
- Publication number
- CN113961012B CN113961012B CN202111123733.0A CN202111123733A CN113961012B CN 113961012 B CN113961012 B CN 113961012B CN 202111123733 A CN202111123733 A CN 202111123733A CN 113961012 B CN113961012 B CN 113961012B
- Authority
- CN
- China
- Prior art keywords
- angular velocity
- noise
- angular
- dynamic inverse
- follows
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000001914 filtration Methods 0.000 title claims abstract description 33
- 230000009467 reduction Effects 0.000 title claims abstract description 14
- 230000001133 acceleration Effects 0.000 claims abstract description 17
- 238000005096 rolling process Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims description 23
- 230000008569 process Effects 0.000 claims description 8
- 238000005259 measurement Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 5
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 238000005312 nonlinear dynamic Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 238000007664 blowing Methods 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000036039 immunity Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
- G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
- G05D1/0825—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability using mathematical models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Aviation & Aerospace Engineering (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Algebra (AREA)
- Feedback Control In General (AREA)
Abstract
The application provides an increment dynamic inverse control method based on EKF filtering noise reduction, which comprises the following steps: taking the angular speed of the airplane as a controlled state quantity, and further establishing a motion model comprising pitching, rolling and yawing angular speeds; step two, constructing an angular velocity increment dynamic inverse control law according to the established motion model; and thirdly, constructing an EKF filter for filtering noise introduced by the sensor in the angular velocity according to the characteristics of the sensor and the noise influence, and obtaining a clean angular velocity signal again so as to reduce the influence of the noise on angular acceleration.
Description
Technical Field
The application belongs to the technical field of airplane control, and particularly relates to an incremental dynamic inverse control method based on EKF filtering noise reduction.
Background
The incremental dynamic inverse control method has the advantages of excellent control performance, no dependence on information of a model and strong robustness, so that the incremental dynamic inverse control method has wide application prospect in the field of aircraft control. However, in the angular velocity increment dynamic inverse control method, an angular acceleration signal is used, and the accuracy of the angular acceleration directly influences the performance of the controller. However, an angular acceleration sensor is not usually provided in an aircraft, so in general, the angular acceleration is obtained by differentiating the angular velocity, and noise in the angular velocity is amplified by the differentiating method, so that the angular velocity needs to be filtered to ensure that the angular velocity is not affected by the noise, and then clean angular acceleration can be obtained.
The EKF filtering method is simple in structure, mature filtering method is applied, noise in signals is filtered through a prediction-correction mode, and instantaneity of the signals is guaranteed. Therefore, the EKF filtering method is combined with the incremental dynamic inverse control to reduce the influence of noise on the incremental dynamic inverse controller, namely the noise immunity of the control system is improved.
Disclosure of Invention
The way angular acceleration is obtained is typically by differentiating angular velocity, so noise in angular velocity needs to be filtered out. The use of a filter to remove noise from the angular velocity also causes angular velocity delays that reduce the stability of the system and, to some extent, cause the system to diverge. The EKF filtering method filters noise in the useful signal in a prediction-correction mode, so that the useful signal is not influenced by the noise and the real-time performance of the signal is ensured to a great extent. Therefore, the EKF filter is used in an angular velocity increment dynamic inverse control system and used for eliminating noise in the angular velocity, guaranteeing that the angular acceleration is not affected by the noise and finally guaranteeing the dynamic performance of the increment dynamic inverse controller.
Aiming at the problem that the increment dynamic inverse is affected by noise, the application adopts an EKF filtering method to improve the conventional increment dynamic inverse control method, and provides an EKF filtering noise reduction-based increment dynamic inverse control method which is used for solving the influence of sensor noise on an increment dynamic inverse controller so as to improve the noise resistance of the increment dynamic inverse control system.
In order to achieve the above objective, the implementation scheme of the present application can be divided into 3 steps, which are specifically as follows:
taking the angular speed of the airplane as a controlled state quantity, and further establishing an angular speed motion model comprising pitching, rolling and yawing;
step two, constructing an angular velocity increment dynamic inverse control law according to the established motion model;
and thirdly, constructing an EKF filter for filtering noise introduced by the sensor in the angular velocity according to the characteristics of the sensor and the noise influence, and obtaining a clean angular velocity signal again so as to reduce the influence of the noise on angular acceleration.
Further, in the first step, the aircraft angular velocity is used as a controlled state quantity, and then an angular velocity motion model including pitch, roll and yaw is established, and the process is as follows:
the aircraft p, q and r are selected as state quantities, and the corresponding aircraft control surface is u= [ delta ] e δ a δ r ] T . The differential equation of angular velocity can be expressed asThe following steps:
wherein J is a moment of inertia matrix; m is triaxial moment, which can be split into two parts, one part is only related to aerodynamic parameters and aircraft state, and is recorded asThe other part being related to the steering derivative only>The method comprises the following steps:
wherein Q is dynamic pressure; s represents the wing area; b is the extension length;is the average aerodynamic chord length.Representing steering derivatives related to ailerons, elevators and rudder +.>Representing aerodynamic derivatives independent of manipulating aerodynamic derivatives, all of which were derived from wind tunnel blowing or simulated by CFD software, as follows:
x cg is an aircraftThe position of the center of gravity; x is x cgr Is the position of the aerodynamic focus of the airfoil. Defining its dimension on the average geometrical chord, i.e
Simplifying the angular velocity differential equation, equation (1), into an affine nonlinear form:
further, in the second step, according to the motion model, the process of designing the angular velocity increment dynamic inverse control law is as follows:
(a) Incremental dynamic inverse control method
The nonlinearity of the controlled system can be described as:
in the formula, the system state variable x (t) epsilon R n System input u (t) ∈r p System output y e R m F (x) is a nonlinear dynamic function, control independent, and g (x) is a control dependent nonlinear control distribution function.
The nonlinear dynamic inversion concept is to find a direct relation between the input quantity and the output quantity (state quantity) to perform inversion operation solution, and the incremental nonlinear dynamic inversion control method is to seek a relation between differentiation of the input quantity and the output quantity (state quantity), so that the output equation is differentiated first.
in the formula ,is a jacobian operator. Since f (x, u) is a function of x and u, taylor's are given by the above equationThe expansion of the series can be obtained:
the control quantity generally has a much larger effect on the system state than the state change quantity, i.e. f (x) 0 ,u 0 )(x-x 0 )＜＜g(x 0 ,u 0 )(u-u 0 ) In order to grasp the main contradiction, the above formula can be further simplified to:
according to the above equation, the amount of change in the control amount is inversely solved, that is:
wherein v=ω (x cmd -x), the incremental dynamic inverse control law can therefore be written as:
(b) Angular velocity increment dynamic inverse control law design
The differential equation of angular velocity can be expressed as:
the taylor series expansion is performed on the angular velocity differential equation (5), and since the sampling interval is short, the influence of the state quantity on the angular acceleration is ignored. The results were as follows:
wherein Is the angular acceleration signal at the previous moment. The angular velocity increment dynamic inverse control law is designed as follows:
wherein vω Is a virtual control quantity, which represents the expected dynamic performance of the angular velocity, and is designed as follows:
ω p ,ω q ,ω r representing the angular velocity bandwidth.
Further, in the third step, according to the characteristics of the sensor and the influence of noise, an EKF filter is constructed to filter noise introduced by the sensor in the angular velocity, and a clean angular velocity signal is obtained again, so that the process of reducing the influence of the noise on the angular acceleration is as follows:
the angular velocity measured by the sensor is inevitably mixed with noise, so an EKF filter is designed to filter the noise in the angular velocity. The sensor noise may be considered gaussian white noise with a variance of 0. Under the influence of noise, the angular velocity equation at time k can be expressed as:
ω k ＝Φ k,k-1 ω k-1 +g(x) k-1 u k-1 +Γ k-1 W k-1 (15)
in the formula ,Φk,k-1 At t k-1 From time to t k A step-by-step transfer matrix of time; omega k-1 and ωk Respectively t k-1 Time sum t k Angular velocity at time; g (x) k-1 and uk-1 Respectively represent t k-1 A control input matrix and control surface input at the moment; Γ -shaped structure k-1 To drive the array for system noise, W k-1 Is a system noise sequence.
Sensor measurement Z k Can be expressed as:
Z k ＝H k ω k +V k-1 (16)
wherein ,Hk To measure the matrix, since the sensor measurement is the angular velocity state quantity, H k ＝I。V k Is to measure the noise sequence. At the same time W k and Vk Meets the requirement that the average value is 0, and the different moments are mutually independent, namely
E[V k ]＝0,E[W k ]＝0,Cov[V k ,V j ]＝R k δ kj ,Cov[W k ,W j ]＝Q k δ kj ,Cov[V k ,W j ]＝0
R k Measuring a variance matrix of the noise sequence; q (Q) k Is a variance matrix of the system noise sequence. Angular velocity ω of the aircraft k Estimation of (a)The solution is as follows:
one-step prediction of angular velocity
Because of the non-linear system of the aircraft, phi when the sampling period T is small k,k-1 Can be expressed by a one-time approximation, i.e
Φ k,k-1 ＝I+F(t k-1 )·T(18)
F(t k-1 ) Jacobian matrix representing the system state equation versus angular velocity. The angular velocity differential equation is developed to obtain:
wherein the moment of inertia components are as follows:
then, jacobian matrix F (t k-1 ) The specific method can be written as follows:
filtering gain: k (K) k ＝P k/k-1 (P k/k-1 +R k ) -1 (22)
Angular velocity one-step prediction mean square error:
estimating a mean square error: p (P) k ＝(I-K k )P k/k-1 (24)
Angular velocity estimation:
the method comprises the step of adopting an incremental dynamic inverse control method based on EKF filtering to complete angular speed control of the aircraft under noise interference. The method can reduce the influence of sensor noise on the system, and can reduce the angular velocity noise without introducing hysteresis, thereby greatly ensuring the dynamic performance of the system.
Aiming at the influence of noise on a system in engineering practice, the method of the application provides an incremental dynamic inverse control method based on EKF filtering noise reduction to reduce the influence of sensor noise on the system, and the method of the application can accurately extract an angular velocity signal from the angular velocity mixed with noise.
Drawings
In order to more clearly illustrate the technical solution provided by the present application, the following description will briefly refer to the accompanying drawings. It will be apparent that the figures described below are merely some embodiments of the application.
Fig. 1 is a block diagram of an incremental dynamic inverse control structure based on EKF filtering noise reduction.
Fig. 2 is an angular velocity filtering scheme based on an EKF filtering method.
Fig. 3 is a graph of roll angle speed versus graph.
FIG. 4 is a graph of roll angle velocity versus time before and after filtration.
Fig. 5 is a graph of pitch rate versus time.
Fig. 6 is a graph of pitch angle rate versus back-and-forth filtration.
Fig. 7 is a yaw rate comparison chart.
Fig. 8 is a yaw rate comparison graph before and after filtering.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application become more apparent, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application.
FIG. 1 is a block diagram of an incremental dynamic inverse control architecture based on EKF noise reduction in the present application. Aiming at the problem that the increment dynamic inverse is affected by noise, the application provides an increment dynamic inverse control method based on EKF filtering noise reduction, which is used for solving the influence of sensor noise on an increment dynamic inverse controller and further improving the noise resistance of an increment dynamic inverse control system.
As shown in fig. 2, the whole process of the angular velocity denoising based on the EKF filtering method is shown in detail, and the steps of implementing the incremental dynamic inverse control method based on the EKF filtering denoising are as follows:
step one: taking the aircraft angular velocity as a controlled state quantity, and further establishing a (pitching, rolling and yawing) angular velocity motion model, wherein the detailed process is as follows:
the aircraft p, q and r are selected as state quantities, and the corresponding aircraft control surface is u= [ delta ] e δ a δ r ] T 。
The differential equation of angular velocity can be expressed as follows:
wherein J is a moment of inertia matrix;the three-axis moment can be divided into two parts, wherein one part is only related to aerodynamic parameters and airplane states and is marked as +.>The other part being related to the steering derivative only>The method comprises the following steps:
wherein Q is dynamic pressure; s represents the wing area; b is the extension length;is the average aerodynamic chord length.Representing steering derivatives related to ailerons, elevators and rudder +.>Representing aerodynamic derivatives independent of manipulating aerodynamic derivatives, all of which were simulated by wind tunnel wind-up or CFD software. The concrete expression is as follows:
in the formula ,xcg Is the position of the center of gravity of the aircraft; x is x cgr Is the position of the aerodynamic focus of the airfoil. Defining its dimension on the average geometrical chord, i.e
Simplifying the angular velocity differential equation, equation (1), into an affine nonlinear form:
step two: according to the established motion model, designing an angular velocity increment dynamic inverse control law;
the differential equation of angular velocity can be expressed as:
the taylor series expansion is performed on the angular velocity differential equation (5), and since the sampling interval is short, the influence of the state quantity on the angular acceleration is ignored. The results were as follows:
wherein ,is the angular acceleration signal at the previous moment. The angular velocity increment dynamic inverse control law is designed as follows:
wherein vω Is a virtual control quantity, which represents the expected dynamic performance of the angular velocity, and is designed as follows:
in the formula ,ωp ,ω q ,ω r Representing the angular velocity bandwidth.
Step three: considering the characteristics of the sensor and the influence of noise, an EKF filter is designed for filtering noise introduced by the sensor in the angular velocity, and a clean angular velocity signal is obtained again, so that the influence of the noise on angular acceleration is reduced.
The angular velocity measured by the sensor is inevitably mixed with noise, so an EKF filter is designed to filter the noise in the angular velocity. The sensor noise may be considered gaussian white noise with a variance of 0. Under the influence of noise, the angular velocity equation at time k can be expressed as:
ω k ＝Φ k,k-1 ω k-1 +g(x) k-1 u k-1 +Γ k-1 W k-1 (9)
in the formula ,Φk,k-1 At t k-1 From time to t k A step-by-step transfer matrix of time; omega k-1 and ωk Respectively t k-1 Time sum t k Angular velocity at time; g (x) k-1 and uk-1 Respectively represent t k-1 A control input matrix and control surface input at the moment; Γ -shaped structure k-1 To drive the array for system noise, W k-1 Is a system noise sequence.
Sensor measurement Z k Can be expressed as:
Z k ＝H k ω k +V k-1 (10)
wherein ,Hk To measure the matrix, since the sensor measurement is the angular velocity state quantity, H k ＝I。V k Is to measure the noise sequence. At the same time W k and Vk The average value is 0, and the different moments are mutually independent, namely:
E[V k ]＝0,E[W k ]＝0,Cov[V k ,V j ]＝R k δ kj ,Cov[W k ,W j ]＝Q k δ kj ,Cov[V k ,W j ]＝0
in the formula ,Rk Measuring a variance matrix of the noise sequence; q (Q) k Is a variance matrix of the system noise sequence. Angular velocity ω of the aircraft k Estimation of (a)The solution is as follows:
one-step prediction of angular velocity:
because of the non-linear system of the aircraft, phi when the sampling period T is small k,k-1 Can be approximated by one time
The formula is as follows: phi k,k-1 ＝I+F(t k-1 )·T(12)
F(t k-1 ) Jacobian matrix representing the system state equation versus angular velocity. The angular velocity differential equation is developed to obtain:
wherein the moment of inertia components are as follows:
then, jacobian matrix F (t k-1 ) The specific method can be written as follows:
filtering gain: k (K) k ＝P k/k-1 (P k/k-1 +R k ) -1 (16)
Angular velocity one-step prediction mean square error:
estimating a mean square error: p (P) k ＝(I-K k )P k/k-1 (18)
Angular velocity estimation:
the simulation verification of the application is carried out by giving the same instruction signal under the influence of noise disturbance: and comparing the triaxial angular velocity response under the conventional incremental dynamic inverse control and the incremental dynamic inverse control based on EKF filtering noise reduction. Pairs of angular velocities under different controllers, such as shown in fig. 3, 5, and 7, and pairs of EKF filtered front-to-back angular velocities, such as shown in fig. 4, 6, and 8. Comprehensive simulation results show that the angular velocity increment dynamic inverse controller based on EKF filtering designed by the application has strong noise reduction characteristics, can obviously reduce the influence of noise in angular velocity on a system, and ensures that the angular velocity increment dynamic inverse controller still achieves expected control performance under the influence of noise.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present application should be included in the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (3)
1. An incremental dynamic inverse control method based on EKF filtering noise reduction is characterized by comprising the following steps:
taking the angular speed of the airplane as a controlled state quantity, and further establishing a motion model comprising pitching, rolling and yawing angular speeds;
step two, constructing an angular velocity increment dynamic inverse control law according to the established motion model;
thirdly, according to the characteristics of the sensor and the influence of noise, an EKF filter is constructed for filtering noise introduced by the sensor in the angular velocity, a clean angular velocity signal is obtained again, and further the influence of the noise on angular acceleration is reduced, and the process comprises the following steps:
the angular velocity measured by the sensor is inevitably mixed with noise, so an EKF filter is designed to filter the noise in the angular velocity, the sensor noise is regarded as Gaussian white noise with variance of 0, and under the influence of the noise, the angular velocity equation at the moment k is expressed as:
ω k ＝Φ k,k-1 ω k-1 +g(x) k-1 u k-1 +Γ k-1 W k-1
in the formula ,Φk,k-1 At t k-1 From time to t k Moment of one step transferAn array; omega k-1 and ωk Respectively t k-1 Time sum t k Angular velocity at time; g (x) k-1 and uk-1 Respectively represent t k-1 A control input matrix and control surface input at the moment; Γ -shaped structure k-1 To drive the array for system noise, W k-1 Is a system noise sequence;
sensor measurement Z k Expressed as: z is Z k ＝H k ω k +V k-1
wherein ,Hk To measure the matrix, since the sensor measurement is the angular velocity state quantity, H k ＝I；V k Is to measure the noise sequence and at the same time W k and Vk The mean value is 0, and the different moments are mutually independent, namely:
E[V k ]＝0,E[W k ]＝0,Cov[V k ,V j ]＝R k δ kj ,Cov[W k ,W j ]＝Q k δ kj ,Cov[V k ,W j ]＝0
R k measuring a variance matrix of the noise sequence; q (Q) k A variance matrix for the system noise sequence; angular velocity ω of the aircraft k Estimation of (a)The solution is as follows:
one-step prediction of angular velocity:
because of the non-linear system of the aircraft, phi when the sampling period T is small k/k-1 Expressed by a first approximation, namely: phi k/k-1 ＝I+F(t k-1 )·T；
F(t k-1 ) Jacobian matrix representing the system state equation diagonal speed;
the angular velocity differential equation is developed to:
wherein the moment of inertia components are as follows:
then, jacobian matrix F (t k-1 ) The specific writing is as follows:
filtering gain: k (K) k ＝P k/k-1 (P k/k-1 +R k ) -1
Angular velocity one-step prediction mean square error:
estimating a mean square error: p (P) k ＝(I-K k )P k/k-1
Angular velocity estimation:
2. the method for incremental dynamic inverse control based on EKF filtering noise reduction according to claim 1, wherein in step one, the aircraft angular velocity is used as a controlled state quantity, and the process of building a motion model including pitch, roll and yaw angular velocity is as follows:
the aircraft p, q and r are selected as state quantities, and the corresponding aircraft control surface is u= [ delta ] e δ a δ r ] T The differential equation of angular velocity is expressed as follows:
wherein J is a moment of inertia matrix;the three-axis moment is divided into two parts, wherein one part is only related to aerodynamic parameters and aircraft states and is marked as +.>The other part being related to the steering derivative only>The method comprises the following steps:
wherein Q is dynamic pressure; s represents the wing area; b is the extension length; c is the average aerodynamic chord length;representing steering derivatives associated with ailerons, elevators and rudders, C l* C m* C n* Representing the aerodynamic derivative independent of manipulating the aerodynamic derivative, wherein:
in the formula ,xcg Is the position of the center of gravity of the aircraft; x is x cgr For the position of the aerodynamic focus of the wing, it is defined as its dimension on the mean geometrical chord, i.e
Simplifying the angular velocity differential equation into an affine nonlinear form:
3. the method for incremental dynamic inverse control based on EKF filtering noise reduction according to claim 1, wherein: in the second step, according to the motion model, the process of constructing the angular velocity increment dynamic inverse control law is as follows:
and (3) carrying out Taylor series expansion on the angular velocity differential equation to obtain:
wherein ,for the angular acceleration signal at the previous moment, the dynamic inverse control law of the angular velocity increment is designed as follows:
wherein ,vω Is a virtual control quantity representing the desired dynamic performance of the angular velocity:
in the formula ,ωp ,ω q ,ω r Representing the angular velocity bandwidth.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111123733.0A CN113961012B (en) | 2021-09-24 | 2021-09-24 | Incremental dynamic inverse control method based on EKF filtering noise reduction |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111123733.0A CN113961012B (en) | 2021-09-24 | 2021-09-24 | Incremental dynamic inverse control method based on EKF filtering noise reduction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113961012A CN113961012A (en) | 2022-01-21 |
| CN113961012B true CN113961012B (en) | 2023-09-22 |
Family
ID=79462678
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111123733.0A Active CN113961012B (en) | 2021-09-24 | 2021-09-24 | Incremental dynamic inverse control method based on EKF filtering noise reduction |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113961012B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107085435A (en) * | 2017-06-01 | 2017-08-22 | 南京航空航天大学 | Hypersonic aircraft attitude harmony control method based on coupling analysis |
| CN110109470A (en) * | 2019-04-09 | 2019-08-09 | 西安电子科技大学 | Joint method for determining posture based on Unscented kalman filtering, satellite attitude control system |
| CN110440795A (en) * | 2019-07-30 | 2019-11-12 | 北京航空航天大学 | A kind of Angular Acceleration Estimation based on Kalman filtering |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110799422A (en) * | 2018-07-23 | 2020-02-14 | 深圳市大疆创新科技有限公司 | Unmanned aerial vehicle control method and unmanned aerial vehicle |
| KR20210029518A (en) * | 2019-09-06 | 2021-03-16 | 삼성전자주식회사 | Multi-sensor based unmanned aerial vehicle and its control method |
-
2021
- 2021-09-24 CN CN202111123733.0A patent/CN113961012B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107085435A (en) * | 2017-06-01 | 2017-08-22 | 南京航空航天大学 | Hypersonic aircraft attitude harmony control method based on coupling analysis |
| CN110109470A (en) * | 2019-04-09 | 2019-08-09 | 西安电子科技大学 | Joint method for determining posture based on Unscented kalman filtering, satellite attitude control system |
| CN110440795A (en) * | 2019-07-30 | 2019-11-12 | 北京航空航天大学 | A kind of Angular Acceleration Estimation based on Kalman filtering |
Non-Patent Citations (1)
| Title |
|---|
| 基于Kalman预报观测器的增量动态逆控制;尹航;朱纪洪;周池军;张旭东;;清华大学学报(自然科学版)(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113961012A (en) | 2022-01-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107415939B (en) | Steering stability control method for distributed driving electric automobile | |
| CN106643737B (en) | Four-rotor aircraft attitude calculation method in wind power interference environment | |
| CN103777641B (en) | The compound Auto-disturbance-rejection Control of aircraft tracing control | |
| CN108490788B (en) | Aircraft pitching channel inversion control method based on double-interference observation | |
| CN103411479B (en) | Sliding mode and active disturbance rejection based composite control method of tank gun controlling system | |
| CN111708377B (en) | Flight control method based on inertial navigation/flight control system information fusion | |
| CN108099901B (en) | A kind of method of active front wheel steering control precision under raising limiting condition | |
| CN111367182A (en) | Hypersonic aircraft anti-interference backstepping control method considering input limitation | |
| CN104950901A (en) | Nonlinear robust control method with finite-time convergence capacity for unmanned helicopter attitude error | |
| CN106950999B (en) | mobile stage trajectory tracking control method adopting active disturbance rejection control technology | |
| Park | A simple output-feedback control for trajectory tracking of underactuated surface vessels | |
| CN112947518B (en) | Four-rotor robust attitude control method based on disturbance observer | |
| CN103994698A (en) | Guided missile pitching channel simple sliding-mode control method based on overload and angular velocity measurement | |
| CN110162071A (en) | A kind of hypersonic aircraft reenters terminal attitude control method and system | |
| CN111208735B (en) | Low-switching gain robust super-spiral sliding mode control method for coating conveying mechanism | |
| CN113682282A (en) | Vehicle stability control method and system, vehicle and storage medium | |
| CN113759718B (en) | Self-adaptive control method for aircraft wing damage | |
| CN117452831B (en) | Four-rotor unmanned aerial vehicle control method, device, system and storage medium | |
| CN110377044B (en) | Finite time height and attitude tracking control method of unmanned helicopter | |
| CN113961012B (en) | Incremental dynamic inverse control method based on EKF filtering noise reduction | |
| CN113625732A (en) | Incremental feedback inverse angular velocity control law design method based on angular acceleration estimation | |
| CN117289709A (en) | High-ultrasonic-speed appearance-changing aircraft attitude control method based on deep reinforcement learning | |
| CN115793449B (en) | Helicopter flight attitude controller design method and device, equipment and storage medium | |
| CN108413923B (en) | Vehicle roll angle and pitch angle estimation method based on robust hybrid filtering | |
| CN116820081A (en) | Under-actuated unmanned ship path tracking control method based on model-free self-adaptive sliding mode |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |