[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

CN112650262A - Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters - Google Patents

Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters Download PDF

Info

Publication number
CN112650262A
CN112650262A CN202011437432.0A CN202011437432A CN112650262A CN 112650262 A CN112650262 A CN 112650262A CN 202011437432 A CN202011437432 A CN 202011437432A CN 112650262 A CN112650262 A CN 112650262A
Authority
CN
China
Prior art keywords
carrier
delta
variation
information
instruction
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.)
Pending
Application number
CN202011437432.0A
Other languages
Chinese (zh)
Inventor
彭淼
张志冰
张秀林
薛艺璇
甄子洋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Aeronautics and Astronautics
Original Assignee
Nanjing University of Aeronautics and Astronautics
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nanjing University of Aeronautics and Astronautics filed Critical Nanjing University of Aeronautics and Astronautics
Priority to CN202011437432.0A priority Critical patent/CN112650262A/en
Publication of CN112650262A publication Critical patent/CN112650262A/en
Pending legal-status Critical Current

Links

Images

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
    • G05D1/0833—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability using limited authority control

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses an automatic carrier landing system of a carrier aircraft based on robust predictive control under the condition of uncertain parameters, which comprises a carrier landing instruction generation module, a guidance and control integrated module and a carrier aircraft model, wherein the carrier landing instruction generation module comprises a deck motion estimation module and a glide track generation module based on a time sequence AR model; the guidance control integrated module comprises a longitudinal robust prediction controller and a transverse robust prediction controller; the carrier landing instruction generation module generates a height instruction HcAnd a yaw instruction YcGenerating an elevator deflection angle increment delta through a guidance control integrated moduleeThrottle increment deltaTAileron deflection angle increment deltaaAnd rudder deflection angle increment deltarAnd the carrier-based aircraft receives the instruction information and outputs flight attitude and position information. The invention ensures the good tracking performance and robustness of the whole automatic carrier landing system of the carrier-based aircraft.

Description

Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters
Technical Field
The invention belongs to the technical field of automatic landing and guidance, and particularly relates to an automatic landing system of a carrier-based aircraft based on robust predictive control under the condition of uncertain parameters.
Background
The automatic carrier landing system is very important for the carrier aircraft to realize the maritime combat task. The automatic landing problem can be expressed as that the carrier-based aircraft with the characteristics of nonlinearity, multivariable coupling and parameter uncertainty accurately tracks the reference track of the glide slope, and landing is finally completed under complex sea conditions and wake disturbance caused by ship motion.
The automatic carrier landing system of the carrier-based aircraft comprises a carrier landing instruction generation module, a guidance and control integrated module and a carrier-based aircraft model. And the measured deck motion information is subjected to data processing, so that the ship motion influence is eliminated from the measurement information, and the accurate position of the airplane in an inertial space coordinate system is obtained. And comparing with the reference gliding track, thereby generating two kinds of instruction information: and the track error instruction information and the flight control system instruction information guide and control the airplane to fly along the glidepath.
Most of the traditional automatic carrier landing systems adopt PID (proportion integration, derivative) controllers, and PID control strategies are mature and easy to implement but cannot always meet the high-precision requirement under complex sea conditions. Therefore, the advanced control method is considered to be applied to the automatic carrier landing system of the carrier-based aircraft so as to improve the success rate and the safety of the automatic carrier landing of the carrier-based aircraft.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides an automatic carrier landing system of a carrier-based aircraft based on robust predictive control under the condition of uncertain parameters, so that the carrier-based aircraft with nonlinear, multivariable coupling and parameter uncertainty characteristics can accurately track the reference track of a glide slope.
The technical scheme is as follows: the invention relates to an automatic carrier landing system of a carrier-based aircraft based on robust predictive control under the condition of uncertain parameters, which comprises a carrier landing instruction generation module, a guidance control integrated module and a carrier-based aircraft model; the landing instruction generation module comprises a deck motion estimation module and a glide track generation module based on a time series AR model; the guidance control integrated module comprises a longitudinal robust prediction controller and a transverse robust prediction controller; the carrier landing instruction generatorModule generates height instruction HcAnd a yaw instruction YcGenerating an elevator deflection angle increment delta through a guidance control integrated moduleeThrottle increment deltaTAileron deflection angle increment deltaaAnd rudder deflection angle increment deltarAnd the carrier-based aircraft receives the instruction information and outputs flight attitude and position information.
Further, the deck motion estimation module based on the time series AR model adds the estimation information of the deck motion into an automatic landing guidance system guidance law for estimating the value of the future first step of the ship motion:
Figure BDA0002829582510000021
wherein x is a known ship motion state,
Figure BDA0002829582510000024
for the estimated ship motion state, p is the order of the estimated model, { a }iI 1, 2.. p } is the coefficient in the model estimated by modeling with N data, and l is the estimated step number.
Further, the vertical robust look ahead controller is:
Figure BDA0002829582510000022
Figure BDA0002829582510000023
wherein the longitudinal control input ulon＝[Δδe,ΔδT]T，ΔδeDelta for elevator yaw angle increaseTIs the throttle increment; longitudinal state variable xlon＝[ΔV,Δα,Δq,Δθ]TDelta V is the speed variation, delta alpha is the attack angle variation, delta q is the pitch angle rate variation, and delta theta is the pitch angle variation; x is the number oftlon＝[Hc(k),...,Hc(k+N-1)]TForecast information for longitudinal lanes; hcIs a height instruction; h is height information; herIs a height error; w is alonIs the longitudinal component of the wake disturbance; a. thelon、Bulon、Bwlon、AeblonIs a longitudinal state space matrix of the airplane; a. thetlon、Btlon、CtlonThe state space coefficient of the N-step delayer is obtained; x is the number ofplonComprising a longitudinal state variable x comprising a height variation deltaHglon＝[ΔV,Δα,Δq,Δθ,ΔH]TAnd height anticipation information; fglonIs a longitudinal state variable error parameter matrix; frlonForecasting an information parameter matrix for the ideal glide height; fwlonA parameter matrix of the longitudinal component of the wake interference of the warship; y islon、
Figure BDA0002829582510000031
The correlation term coefficients are controlled for robustness.
Further, the lateral robust predictive controller is:
Figure BDA0002829582510000032
Figure BDA0002829582510000033
wherein the lateral control input ulat＝[Δδa,Δδr]T，ΔδaFor the aileron declination angle increment, Δ δrIs the rudder increment; transverse lateral state variable xlat＝[Δβ,Δp,Δr,Δφ,Δψ]TDelta beta is the variation of the sideslip angle, delta p is the variation of the rolling angle rate, delta r is the variation of the yaw angle rate, delta phi is the variation of the rolling angle, and delta psi is the variation of the yaw angle; x is the number oftlat＝[Yc(k),...,Yc(k+N-1)]TPredicting information for lateral deviation of the transverse lateral channel; y iscIs a yaw instruction; y is lateral deviation information; y iserIs the lateral offset error; w is alatIs the transverse component of the wake disturbance; a. thelat、Bulat、Bwlat、AeblatThe method comprises the steps of (1) obtaining a transverse state space matrix of the airplane; a. thetlat、Btlat、CtlatThe state space coefficient of the N-step delayer is obtained; x is the number ofplatIncluding lateral state variables x including lateral variation Δ Yglat＝[Δβ,Δp,Δr,Δφ,Δψ,ΔY]TAnd lateralization forecast information; fglatA transverse lateral state variable error parameter matrix; fwlatA parameter matrix of the transverse and lateral component of the wake disturbance of the warship is obtained; y islat、
Figure BDA0002829582510000034
The correlation term coefficients are controlled for robustness.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that: 1. the invention designs a deck motion predictor based on a time series AR model, so that a carrier-based aircraft can more accurately track an ideal landing track after deck motion; 2. the method considers the parameter uncertainty of the fixed-wing aircraft caused by external interference such as tail airflow disturbance in the landing process, takes the future information as a feedforward control signal and takes the current information as a feedback control signal, and therefore the landing response speed and the robustness of the carrier-based aircraft are improved.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a graph showing the actual height value of an ideal landing point and the predicted value thereof under a 3-level sea condition;
FIG. 3 is a graph of ideal altitude and actual flying altitude for a class 3 sea state;
fig. 4 is a graph of lateral tracking under grade 3 sea conditions.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a robust predictive control-based automatic carrier landing system for a carrier-based aircraft under the condition of uncertain parameters, which comprises a carrier landing instruction generation module, a guidance control integrated module and a carrier-based aircraft model. Carrier landing instruction generation module generates altitude instructionHcAnd a yaw instruction YcGenerating an elevator deflection angle increment delta through a guidance control integrated moduleeThrottle increment deltaTAileron deflection angle increment deltaaAnd rudder deflection angle increment deltarAnd the carrier-based aircraft receives the instruction information and outputs flight attitude and position information. The specific module implementation process is as follows:
module 1: and a carrier landing instruction generation module. The landing instruction generation module further comprises a deck motion predictor based on the time series AR model and glide track generation. The estimated information of the deck motion is obtained by a deck motion estimator based on an Autoregressive (AR) Model, and the estimated information of the deck motion is added into a guidance law of an automatic landing guidance system, so that a more accurate gliding track is generated.
The input signal of the deck motion predictor is an actual value of the height of an ideal landing point, and the output signal is a predicted value of the height of the ideal landing point. And (3) sending the actual height value of the ideal landing point into a deck motion predictor to obtain the prediction information of deck motion, and adding the prediction information into the guidance law of the automatic landing guidance system 12.5 seconds before landing to generate a new gliding track. The actual value of the ideal landing point height under the grade 3 sea condition and the estimated value thereof are shown in fig. 2.
The calculation method of the deck motion predictor comprises the following steps:
1) the AR model coefficients are calculated. The general form of the AR model is:
Figure BDA0002829582510000041
wherein, the time sequence { x (N), N ═ 1, 2.. and N } is known ship motion measurement data, N is the number of data used for establishing a model, { ξ (N), N ═ 1, 2.. and N } is a measurement error sequence, p is a model order, { a ═ a · biI 1, 2.. p } are coefficients in the model estimated by modeling with N data.
2) And (5) estimating parameters of the AR model. Performing parameter estimation on the AR model by adopting a Kalman filtering algorithm, wherein a state space equation is as follows:
Figure BDA0002829582510000042
where x (i) is (x (i-1), x (i-2),.. times, x (i-p)), i is p + 1.. times, N is the input vector, v (t), and e (t) are white noise sequences that are independent of each other and have a zero mean,
Figure BDA0002829582510000051
let q (t), r (t) be the variances of v (t), e (t), respectively, p 1, 2.
The parameter estimation based on Kalman filtering comprises the following steps:
calculating an optimal prediction error covariance matrix:
P(t,t-1)＝P(t-1)+Q(t-1)
calculating an optimal gain matrix:
K(t)＝P(t,t-1)XT(t)[X(t)P(t,t-1)XT(t)-R(t)]-1
calculating the optimal Kalman filtering value:
Figure BDA0002829582510000052
calculating an optimal filter error covariance matrix:
P(t)＝P(t,t-1)-K(t)X(t)P(t,t-1)。
3) the sequence order of the AR model is determined by using an AIC criterion (Akaike information criterion). Residual sum of squares S of prediction modelsp(N) and AIC criterion functions I (p) can be described as:
Figure BDA0002829582510000053
wherein,
Figure BDA0002829582510000054
and is
Figure BDA0002829582510000055
The order of the prediction model is obtained.
4) Obtaining an estimated model of the ship motion, wherein the estimated value of the future first step of the ship motion is as follows:
Figure BDA0002829582510000056
wherein,
Figure BDA0002829582510000057
for the estimated ship motion state, p is the order of the estimated model, { a }iI 1, 2.. p } is the system in the model estimated by modeling with N data, and l is the estimated step number.
And (3) module 2: and a guidance control integrated module. The guidance control integration module comprises a longitudinal robust forecasting controller and a transverse robust forecasting controller. Height instruction H generated by carrier landing instruction generation modulecAnd a yaw instruction YcGenerating an elevator deflection angle increment delta through a guidance control integrated moduleeThrottle increment deltaTAileron deflection angle increment deltaaAnd rudder deflection angle increment deltar。
The design of the longitudinal robust predictive controller is as follows:
the longitudinal discretization model of the aircraft is:
Figure BDA0002829582510000061
wherein the longitudinal control input ulon＝[Δδe,ΔδT]T，ΔδeDelta for elevator yaw angle increaseTIs the throttle increment; w is alonIs the longitudinal component of the wake disturbance; longitudinal state variable xlon＝[ΔV,Δα,Δq,Δθ]TDelta V is the speed variation, delta alpha is the attack angle variation, delta q is the pitch angle rate variation, and delta theta is the pitch angle variation; x is the number oftlon＝[Hc(k),...,Hc(k+N-1)]TForeseeing information for the height of the longitudinal channel; x is the number ofplon＝[xglon,xtlon]TComprising a longitudinal state variable x comprising a height change Δ Hglon＝[ΔV,Δα,Δq,Δθ,ΔH]TAnd height anticipation information; hcIs a height instruction; h is height information; herIs a height error; a. thelon、Bulon、Bwlon、AeblonIs a longitudinal state space matrix of the airplane; a. thetlon、Btlon、CtlonIs the state space coefficient of the N-step delayer.
The robust predictive controller consists of a feedforward control component and a feedback control component. The feedforward control component is height forecast information, and the feedback control component is the difference between the actual flying height and the ideal gliding height and the longitudinal state variable error. Under the condition of wake flow interference of a warship, future information is used as a feedforward control signal, and current information is used as a feedback control signal, so that the height tracking of the lower slideway is realized; the control law is as follows:
Figure BDA0002829582510000062
wherein, FglonIs a longitudinal state variable error parameter matrix; frlonForecasting an information parameter matrix for the ideal glide height; fwlonA parameter matrix of the longitudinal component of the wake interference of the warship; y islon、
Figure BDA0002829582510000063
The correlation term coefficients are controlled for robustness.
Matrix Fglon、Frlon、Fwlon、Ylon、
Figure BDA0002829582510000064
The formula of (c) is as follows:
Figure BDA0002829582510000071
Figure BDA0002829582510000072
wherein:
Figure BDA0002829582510000073
is a steady state solution of the Riccati equation, W1、W2For the constant gain matrix used to anticipate control adjustments,
Figure BDA0002829582510000074
Figure BDA0002829582510000075
Cg＝[0 I]；Ap、Bp1、Bp2、
Figure BDA0002829582510000076
a constant state space matrix of the uncertain system; the uncertainty term can be expressed as
Figure BDA0002829582510000077
Figure BDA0002829582510000078
ΔBw、ΔBuRespectively are the variation parameters of the uncertain model of the system;
Figure BDA0002829582510000079
comprising Fglon、Frlon、FwlonCorrelation term and adjustable gain, epsilon, for robust predictive control1ε2ε3Is an arbitrary positive scalar quantity, Ylon、
Figure BDA00028295825100000710
Is based on a Linear Matrix Inequality (LMI) method.
The design method of the transverse robust predictive controller in the guidance control integrated module comprises the following steps:
the lateral discretization model of the aircraft is:
Figure BDA00028295825100000711
wherein the lateral control input ulat＝[Δδa,Δδr]T，ΔδaFor the aileron declination angle increment, Δ δrIs the rudder increment; w is alatIs the transverse component of the wake disturbance; transverse lateral state variable xlat＝[Δβ,Δp,Δr,Δφ,Δψ]TDelta beta is the variation of the sideslip angle, delta p is the variation of the rolling angle rate, delta r is the variation of the yaw angle rate, delta phi is the variation of the rolling angle, and delta psi is the variation of the yaw angle; x is the number oftlat＝[Yc(k),...,Yc(k+N-1)]TPredicting information for lateral deviation of the transverse lateral channel; x is the number ofplat＝[xglat,xtlat]TIncluding lateral state variables x including lateral variation Δ Yglat＝[Δβ,Δp,Δr,Δφ,Δψ,ΔY]TAnd lateralization forecast information; y iscIs a yaw instruction; y iserIs the lateral offset error; a. thelat、Bulat、Bwlat、AeblatThe method comprises the steps of (1) obtaining a transverse state space matrix of the airplane; a. thetlat、Btlat、CtlatIs the state space coefficient of the N-step delayer.
The purpose of the transverse direction robust predictive controller is to realize zero lateral offset distance and lateral stability of the carrier-based aircraft under the condition of wake flow interference of a carrier; the control law is as follows:
Figure BDA0002829582510000081
wherein, FglatA transverse lateral state variable error parameter matrix; fwlatA parameter matrix of the transverse and lateral component of the wake disturbance of the warship is obtained; y islat、
Figure BDA0002829582510000082
The correlation term coefficients are controlled for robustness.
Matrix arrayFglat、Fwlat、Ylat、
Figure BDA0002829582510000083
The calculation method of (2) is corresponding to a matrix of a longitudinal control law.
And a module 3: a ship-based airplane model. And the carrier-based aircraft receives the instruction information and outputs flight attitude and position information.
In order to verify the effectiveness of the method in automatic landing control of the carrier-based aircraft, the following simulation experiment is carried out. Matlab simulation software is adopted as a simulation tool, and an F/A-18 airplane is adopted as a carrier aircraft. The maximum order M of the AR model adopted in the simulation experiment is N/3, the acquisition time interval is 0.5s, the estimated step number is 5 steps, the height of the ideal landing point of the ship is estimated and simulated under the conditions of 3-level sea conditions, 18 knots of ship speed and pi/3 encounter angle, and the comparison between the actual value and the estimated value of the height of the ideal landing point under the 3-level sea conditions is obtained, as shown in figure 2. The height change of the ideal landing point can be accurately and effectively estimated by adopting the AR model.
The simulation experiment adopted lower slipway tracking time is 56.3s, the initial height of the lower slipway is 240.3m, the inclination angle of the lower slipway is-3.5 degrees, the initial speed of the carrier-based aircraft is 69.3m/s, the attack angle is 8.5 degrees, the pitch angle is 5 degrees, the initial height of the aircraft is 245.3m, the initial lateral offset distance is 5m, the sampling time is 0.1s, the predicted step number is 12, the comparison between the ideal height and the actual flying height under the 3-level sea condition is obtained, as shown in fig. 3, the lateral tracking curve under the 3-level sea condition is shown in fig. 4.
Fig. 3 shows the ideal altitude and the actual flying altitude under the sea condition of class 3, and fig. 4 shows the lateral tracking curve under the sea condition of class 3. It can be seen from fig. 3 and 4 that under the sea condition of level 3, errors between the actual flight height and the ideal height of the carrier aircraft under the robust prediction control and errors between the actual yaw distance and the ideal yaw distance are small, the system tracking effect is good, and the robustness is strong.

Claims (4)

1. The robust predictive control-based carrier-based aircraft automatic carrier landing system under the condition of uncertain parameters comprises a carrier landing instruction generation module and guidance control integrationThe system comprises a module and a carrier-based aircraft model, and is characterized in that the carrier landing instruction generation module comprises a deck motion estimation module and a glide track generation module based on a time series AR model; the guidance control integrated module comprises a longitudinal robust prediction controller and a transverse robust prediction controller; the carrier landing instruction generation module generates a height instruction HcAnd a yaw instruction YcGenerating an elevator deflection angle increment delta through a guidance control integrated moduleeThrottle increment deltaTAileron deflection angle increment deltaaAnd rudder deflection angle increment deltarAnd the carrier-based aircraft receives the instruction information and outputs flight attitude and position information.
2. The automatic carrier landing system of the shipboard aircraft based on the robust prediction control under the condition of uncertain parameters of claim 1, wherein the time series AR model based deck motion prediction module adds the prediction information of the deck motion into the guidance law of the automatic carrier landing guidance system for predicting the value of the future first step of the ship motion:
Figure FDA0002829582500000011
wherein x is a known ship motion state,
Figure FDA0002829582500000012
for the estimated ship motion state, p is the order of the estimated model, { a }iI 1, 2.. p } is the coefficient in the model estimated by modeling with N data, and l is the estimated step number.
3. The automatic carrier landing system of the carrier-based aircraft based on the robust predictive control under the condition of uncertain parameters of claim 1, wherein the longitudinal robust predictive controller is as follows:
Figure FDA0002829582500000013
Figure FDA0002829582500000014
wherein the longitudinal control input ulon＝[Δδe,ΔδT]T，ΔδeDelta for elevator yaw angle increaseTIs the throttle increment; longitudinal state variable xlon＝[ΔV,Δα,Δq,Δθ]TDelta V is the speed variation, delta alpha is the attack angle variation, delta q is the pitch angle rate variation, and delta theta is the pitch angle variation; x is the number oftlon＝[Hc(k),...,Hc(k+N-1)]TForecast information for longitudinal lanes; hcIs a height instruction; h is height information; herIs a height error; w is alonIs the longitudinal component of the wake disturbance; a. thelon、Bulon、Bwlon、AeblonIs a longitudinal state space matrix of the airplane; a. thetlon、Btlon、CtlonThe state space coefficient of the N-step delayer is obtained; x is the number ofplonComprising a longitudinal state variable x comprising a height variation deltaHglon＝[ΔV,Δα,Δq,Δθ,ΔH]TAnd height anticipation information; fglonIs a longitudinal state variable error parameter matrix; frlonForecasting an information parameter matrix for the ideal glide height; fwlonA parameter matrix of the longitudinal component of the wake interference of the warship; y islon、
Figure FDA0002829582500000021
The correlation term coefficients are controlled for robustness.
4. The automatic carrier landing system of the carrier-based aircraft based on the robust predictive control under the condition of uncertain parameters of claim 1, wherein the lateral robust predictive controller is as follows:
Figure FDA0002829582500000022
Figure FDA0002829582500000023
wherein the lateral control input ulat＝[Δδa,Δδr]T，ΔδaFor the aileron declination angle increment, Δ δrIs the rudder increment; transverse lateral state variable xlat＝[Δβ,Δp,Δr,Δφ,Δψ]TDelta beta is the variation of the sideslip angle, delta p is the variation of the rolling angle rate, delta r is the variation of the yaw angle rate, delta phi is the variation of the rolling angle, and delta psi is the variation of the yaw angle; x is the number oftlat＝[Yc(k),...,Yc(k+N-1)]TPredicting information for lateral deviation of the transverse lateral channel; y iscIs a yaw instruction; y is lateral deviation information; y iserIs the lateral offset error; w is alatIs the transverse component of the wake disturbance; a. thelat、Bulat、Bwlat、AeblatThe method comprises the steps of (1) obtaining a transverse state space matrix of the airplane; a. thetlat、Btlat、CtlatThe state space coefficient of the N-step delayer is obtained; x is the number ofplatIncluding lateral state variables x including lateral variation Δ Yglat＝[Δβ,Δp,Δr,Δφ,Δψ,ΔY]TAnd lateralization forecast information; fglatA transverse lateral state variable error parameter matrix; fwlatA parameter matrix of the transverse and lateral component of the wake disturbance of the warship is obtained; y islat、
Figure FDA0002829582500000024
The correlation term coefficients are controlled for robustness.
CN202011437432.0A 2020-12-11 2020-12-11 Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters Pending CN112650262A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011437432.0A CN112650262A (en) 2020-12-11 2020-12-11 Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011437432.0A CN112650262A (en) 2020-12-11 2020-12-11 Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters

Publications (1)

Publication Number Publication Date
CN112650262A true CN112650262A (en) 2021-04-13

Family

ID=75350750

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011437432.0A Pending CN112650262A (en) 2020-12-11 2020-12-11 Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters

Country Status (1)

Country Link
CN (1) CN112650262A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115407650A (en) * 2021-05-27 2022-11-29 中国人民解放军海军航空大学航空基础学院 Shipboard aircraft carrier landing control method based on protection Xin Suanfa and model predictive control

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102854885A (en) * 2012-08-24 2013-01-02 南京航空航天大学 Longitudinal deck motion compensation method for shipboard aircraft landing
CN104880946A (en) * 2015-04-03 2015-09-02 南京航空航天大学 Carrier-based aircraft automatic landing control method based on robust preview control
CN109213203A (en) * 2018-09-13 2019-01-15 南京航空航天大学 A kind of carrier-borne aircraft auto landing on deck control method based on prediction control

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102854885A (en) * 2012-08-24 2013-01-02 南京航空航天大学 Longitudinal deck motion compensation method for shipboard aircraft landing
CN104880946A (en) * 2015-04-03 2015-09-02 南京航空航天大学 Carrier-based aircraft automatic landing control method based on robust preview control
CN109213203A (en) * 2018-09-13 2019-01-15 南京航空航天大学 A kind of carrier-borne aircraft auto landing on deck control method based on prediction control

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZIYANG ZHEN等: "Robust Preview Control and Autoregressive Prediction for Aircraft Automatic Carrier Landing", 《IEEE ACCESS》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115407650A (en) * 2021-05-27 2022-11-29 中国人民解放军海军航空大学航空基础学院 Shipboard aircraft carrier landing control method based on protection Xin Suanfa and model predictive control

Similar Documents

Publication Publication Date Title
CN107168312B (en) Space trajectory tracking control method for compensating UUV kinematic and dynamic interference
CN109976349B (en) Design method of path tracking guidance and control structure of constraint-containing unmanned ship
CN108427406B (en) Shipboard aircraft landing control and stern flow suppression method based on predictive control
CN106444806B (en) The drive lacking AUV three-dimensional track tracking and controlling method adjusted based on biological speed
CN106292294B (en) Shipborne UAV auto landing on deck control device based on model reference self-adapting control
CN106950974B (en) Three-dimensional path understanding and tracking control method for under-actuated autonomous underwater vehicle
CN109213203B (en) Automatic carrier landing control method of carrier-based aircraft based on anticipation control
CN109782785B (en) Automatic aircraft landing control method based on direct force control
CN108594639B (en) Reinforced learning-based track tracking control method for hovercraft
CN108563113B (en) Multi-loop model-free adaptive heading control method for ship
CN109032136B (en) Under-actuated multi-unmanned ship formation tracking method based on master-slave distributed model predictive control
CN108415423B (en) High-interference-rejection self-adaptive path following method and system
CN104880946B (en) A kind of carrier-borne aircraft auto landing on deck control method based on robust preview control
CN108319140B (en) Redefined output type model-free self-adaptive course control method and system
CN113467231B (en) Unmanned ship path tracking method based on sideslip compensation ILOS guidance law
CN106292293A (en) The self adaptation auto landing on deck of the unmanned carrier-borne aircraft of a kind of fixed-wing guides control system
CN104991552B (en) Carrier-borne aircraft auto landing on deck longitudinal controller and its control method based on controller switching
CN107957686B (en) Unmanned helicopter auto landing on deck control system based on prediction control
CN107315348B (en) Position and posture control method of unmanned underwater vehicle based on punishment type wavelet network
CN106502255A (en) A kind of method for designing of carrier-borne aircraft auto landing on deck control system and control method
Wang et al. Path following with prescribed performance for under-actuated autonomous underwater vehicles subjects to unknown actuator dead-zone
CN112650262A (en) Automatic carrier-based aircraft carrier landing system based on robust predictive control under condition of uncertain parameters
CN115480580A (en) NMPC-based underwater robot path tracking and obstacle avoidance control method
CN118244770B (en) Repeated learning composite disturbance-resistant error-tolerant control method for unmanned ship
CN117406746B (en) Design method of fishing boat track maintenance controller considering severe sea conditions

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20210413