CN109583144A - A kind of the Dynamics Optimization controller architecture and design method of unmanned ocean navigation device - Google Patents
A kind of the Dynamics Optimization controller architecture and design method of unmanned ocean navigation device Download PDFInfo
- Publication number
- CN109583144A CN109583144A CN201910016713.XA CN201910016713A CN109583144A CN 109583144 A CN109583144 A CN 109583144A CN 201910016713 A CN201910016713 A CN 201910016713A CN 109583144 A CN109583144 A CN 109583144A
- Authority
- CN
- China
- Prior art keywords
- navigation device
- component
- dynamics
- ocean navigation
- unmanned
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a kind of unmanned ocean navigation device Dynamics Optimization controller architecture and design method, the controller architecture includes disturbance observer, demand regulator and Dynamics Controller.The present invention combines dimensionality reduction disturbance observer, optimization adjuster and Dynamics Controller, so that inside and outside disturbance is accurately estimated with unascertained information and is sent to Dynamics Controller.To solve the problems, such as the Dynamic Constraints of the inside and outside of unmanned vehicles disturbance and unmanned ocean navigation device, the present invention is independent of accurate unmanned ocean navigation device model, more easily Project Realization.The present invention considers input constraint and state constraint simultaneously, and it is predicted using rolling time horizon, establish optimization object function, the guidance signal for meeting constraint condition is obtained with neurodynamics Optimization Solution, so that control signal meets the actual physics constraint of unmanned ocean navigation device, to greatly improve the performance of unmanned ocean navigation device.
Description
Technical field
The present invention relates to unmanned ocean navigation device field, the Dynamics Optimization of especially a kind of unmanned ocean navigation device is controlled
Device structure and design method.
Background technique
The autonomy-oriented and intelligent water of unmanned ocean navigation device Dynamics Optimization control ocean navigation device unmanned for raising
It is flat, promote its industrial applications to have great importance.
Chinese patent CN106773713A discloses a kind of high precision nonlinear path for drive lacking ocean navigation device
Tracking and controlling method, this method regard the change rate of aircraft yaw angle as an indeterminate, by parameter in kinetic model
Uncertain, Unmarried pregnancy and external environment disturbance are as lump uncertainty, using observer to kinematics uncertainty
It is observed in real time with uncertainty in dynamics；The expectation angle of sight is calculated using conventional line-of-sight angle method of guidance；Design is based on sight
The nonlinear path tracking control unit of device is surveyed, and kinematics and uncertainty in dynamics observation are compensated；And it uses
Nonlinear Tracking Differentiator simplifies controller so that the controller is more suitable for engineer application.This invention removes model ginsengs
The influence to path trace such as number uncertainty, Unmarried pregnancy and external environment disturbance, is realized to aircraft expected path
Accurate tracing control.
Chinese patent CN108427414A proposes a kind of horizontal surface self-adaption Trajectory Tracking Control of Autonomous Underwater Vehicle
Method uses radial basis function (Radial# using the speed and angular speed of the method estimation AUV of high gain state observer
Basis#Function, RBF) neural network highly precise approach function compensation model parameter indeterminate and external disturbance item,
AUV track following problem is converted into tracking problem under polar coordinate system through coordinate transform.Kinematics is designed first when specific solution
The expectation of model inputs, then the expectation input of design motivation model, finally using in RBF neural estimation expectation input
Uncertain item, design neural network weight more new law, AUV finally made to track desired track.
But existing control method has the following problems:
First, some only accounts for the model uncertainty and ocean ring of unmanned ocean navigation device in existing control method
External disturbance problem caused by border but has ignored the Dynamic Constraints problem of unmanned ocean navigation device, and some control methods are only capable of
The kinematics and dynamics constraint for handling unmanned ocean navigation device, is but bad at external disturbance caused by handling marine environment and asks
It inscribes, lacks the control method that can solve the problems, such as Dynamic Constraints and external disturbance and uncertain problem simultaneously in existing method.
Second, constraint condition is ubiquitous in the motion control of unmanned ocean navigation device.About such as input constraint, state
Beam.The existing method of processing restricted problem has barrier function design, design in auxiliary system and model predictive control method etc..But hinder
Function design is hindered to only account for output constraint and non-input and state constraint, design in auxiliary system, which only accounts for input constraint, not to be had but
In view of state constraint, model predictive control method considers input and state constraint, be but highly dependent on it is accurate nobody
Ocean navigation device model, this is difficult to realize in engineer application.Allow to obtain accurate unmanned ocean navigation device mould
Type, model predictive control method can not be solved by uncertain hydrodynamic parameter, modeling error and utilizing ocean current bring not
Know that the caused lump of disturbance is uncertain, poor anti jamming capability.
The uncertainty of third, the dynamic system of the existing unmanned ocean navigation device of estimation mostly uses mind with external disturbance
Through network method, and in actual application, the disadvantages of computation burden is big, adjustment parameter is more, is unfavorable for neural network method
Project Realization.
Summary of the invention
To solve the shortcomings of the prior art, the present invention will propose a kind of unmanned ocean navigation for being able to achieve following purpose
Device Dynamics Optimization controller architecture and design method:
1, Dynamic Constraints and external disturbance and uncertain problem can be solved the problems, such as simultaneously；
2, independent of accurate unmanned ocean navigation device model, strong antijamming capability
3, computation burden is small, adjustment parameter is few, is easy to Project Realization.
To achieve the goals above, technical scheme is as follows: a kind of unmanned ocean navigation device Dynamics Optimization control
Device structure processed, including disturbance observer, demand regulator and Dynamics Controller, the input terminal of the disturbance observer respectively with
The output end of unmanned ocean navigation device, the output end of Dynamics Controller are connected；The input terminal of the Dynamics Controller is distinguished
It is connected with the output end of unmanned ocean navigation device, the output end of disturbance observer and the output end of demand regulator；Described instruction
The input terminal of adjuster is connected with the output end of Dynamics Controller and outer ring controller respectively.
A kind of design method of unmanned ocean navigation device Dynamics Optimization controller architecture, the unmanned ocean navigation device
Six-degree-of-freedom dynamic model is indicated with following formula:
Wherein:
Indicate the position and attitude information of unmanned ocean navigation device, wherein x,φ, θ and ψ respectively indicate length travel component, lateral displacement component, vertical deviation component, yaw angle, roll angle and indulge
Cradle angle；Represent a solid space.
Indicate the speed signal of unmanned vehicles, wherein u, v, ω, p, q and r distinguish table
Show range velocity component, lateral velocity component, vertical velocity component, yawing angular speed, angular velocity in roll and angular velocity in pitch；It is full
The unmanned ocean navigation device state constraint ν of footmin≤ν≤νmax, wherein νminAnd νmaxRespectively represent the constraint bound of ν.
Indicate inertial matrix.
Represent the centripetal matrix of Coriolis.
Represent nonlinear dampling matrix.
G (ν, η) indicates the restoring force as caused by buoyancy and gravity collective effect.
Indicate the control signal of unmanned ocean navigation device, wherein τu、τv、τω、τp、
τqAnd τrRespectively indicate crosswise joint component, longitudinally controlled component, vertical control component, yaw angle control component, roll angle control
Component and pitch angle control component；Meet control input constraint τmin≤τ≤τmax, wherein τminAnd τmaxRespectively represent the constraint of τ
Bound.
Time-varying ocean current is represented in marine environment to unmanned ocean navigation device
Bring external disturbance, wherein τωu、τωv、τωω、τωp、τωqAnd τωrIt respectively indicates transversely outer disturbance component, longitudinally outer disturb
Dynamic component, vertical external disturbance component, yaw angle external disturbance component, roll angle external disturbance component and pitch angle external disturbance
Component.Represent the modeling error of unmodeled fluid dynamics and unmanned ocean navigation device.
The design method, comprising the following steps:
A, the design of disturbance observer
The six-degree-of-freedom dynamic model of ocean navigation device unmanned in formula (1) is rewritten into following formula:
WhereinFor a known matrix；
B=M-1；
σ ()=- C (ν) ν-D (ν) ν-Δ (ν, η)-g (η)+τw(t)-M-1A ν is represented by model uncertainty, is not known
In Hydrodynamic Parameters and marine environment time-varying ocean current bring external disturbance and caused by lump it is uncertain.
Disturbance observer is designed to estimate lump uncertainty σ, the input signal of disturbance observer is unmanned ocean navigation
The speed signal ν and control signal τ, disturbance observer of device are expressed as follows:
WhereinFor the secondary status of disturbance observer；It is the estimated value to lump uncertainty σ；It represents
One gain matrix.
B, the design of Dynamics Controller
The input signal of Dynamics Controller includes the output signal of demand regulatorUnmanned ocean navigation device output
The lump uncertainty estimation value of speed signal ν and disturbance observer outputDynamics Controller output signal τ, νmIf
It counts as follows:
WhereinSystem of representatives dynamic system response matrix meets:
WhereinWithFor positive definite matrix.
WhereinMeet Am=A+BKx；Meet Bm=BKr；The Dynamics Controller output signal
For τ and νm。
C, the design of demand regulator
The input signal of demand regulator is the reference speed signal ν of Dynamics Controller outputmWith the given instruction of outer ring
Signal νr.Output signal is to meet unmanned ocean navigation device state constraint to guidance command with optimal under the conditions of control input constraint
SignalThe design method of demand regulator is as follows.
The unmanned ocean navigation device dynamics reference model progress sliding-model control of formula (4) is obtained discrete in demand regulator
The Dynamic Prediction model of change:
WhereinWithRespectively AmAnd BmDiscrete form.Using rolling time horizon mode at the k moment in prediction time domain
Reference speed signal νmIt is predicted to obtain following formula:
Wherein:
It is the k moment to reference speed signal νmPredicted value；For the k moment
The increment of guidance signal；InIndicate a n dimension unit matrix；N is prediction time domain,To control time domain.Demand regulator optimization
Objective function is expressed as follows:
Wherein:It is constant vector, respectively indicates
Guidance signal increment bound and guidance signal bound after considering control input constraint；
It indicates to consider the constraint bound after state constraint,Just for one
Constant；
Q, R respectively indicates velocity state vectors weight and control input weight, and
Formula (8) is converted to following formula:
Wherein:
W=2 (MTQM+R)
Design neurodynamics optimization method is solved to obtain to objective function meets input constraint and state about
The optimal Guidance signal of beam, designed neurodynamics optimization method are as follows:
Wherein:For time constant；For a normal number；It indicatesGradient vector；It indicatesGradient vector；Projection function g[a,b](ρ)=[g[a,b](ρ1),...,g[a,b](ρn)] design method
It is as follows:
Wherein: ρ=[ρ1,...,ρn]T；A=0；C=1；I=1 ..., n.
Compared with prior art, the invention has the following advantages:
First, it only accounts for unmanned ocean navigation device perturbed problem with existing and has ignored unmanned ocean navigation device dynamics
The method of constraint is compared, the carried mechanics optimization controller architecture of the present invention and design method by dimensionality reduction disturbance observer, instruct
Optimizing regulation device and Dynamics Controller combine, so that inside and outside disturbance is accurately estimated and transmitted with unascertained information
To Dynamics Controller.It is asked to solve the Dynamic Constraints of the disturbance of the inside and outside of unmanned vehicles and unmanned ocean navigation device
Topic, and the realization of method designed by the present invention, independent of accurate unmanned ocean navigation device model, more easily engineering is real
It is existing.
Second, existing control method such as barrier function design, design in auxiliary system etc. does not consider unmanned ocean comprehensively
Some constraint conditions in aircraft motion control, such as input constraint, state constraint.The present invention consider simultaneously input constraint with
State constraint, and predicted using rolling time horizon, optimization object function is established, obtains meeting about with neurodynamics Optimization Solution
The guidance signal of beam condition, so that control signal meets the actual physics constraint of unmanned ocean navigation device, to greatly improve nothing
The performance of sea of faces ocean aircraft.
Third approaches uncertainty compared with the method for disturbance using neural network with existing, and the present invention is designed without the sea of faces
The disturbance observer of foreign aircraft brings external disturbance to form to by unmanned ocean navigation device modeling uncertainty with marine environment
Lump uncertainty estimated that required adjustment parameter is few, be easy to adjust ginseng.It is dynamic to mention unmanned ocean navigation device by the present invention simultaneously
Mechanics optimization control method is applicable not only to the unmanned ocean navigation device of the water surface, while being also applied for underwater unmanned ocean navigation device,
There is important application valence in motion controls occasions such as target following, the trajectory path tracking of the unmanned ocean navigation device of underwater surface
Value.
4th, disturbance observer of the invention can by inside and outside disturb with unascertained information estimate transmission send to
Dynamics Controller, Dynamics Controller design considers the control law of disturbance information, to improve anti-interference ability.
Detailed description of the invention
Fig. 1 is unmanned ocean navigation device Dynamics Optimization controller architecture schematic diagram；
Fig. 2 is the tracking effect schematic diagram of yawing angular speed；
Fig. 3 is the tracking effect schematic diagram of angular velocity in roll；
Fig. 4 is the tracking effect schematic diagram of angular velocity in pitch；
Fig. 5 is the tracking effect schematic diagram of longitudinal velocity；
Fig. 6 is the tracking effect schematic diagram of lateral velocity；
Fig. 7 is the tracking effect schematic diagram of vertical velocity；
Fig. 8 is lateral, longitudinal, vertical lump uncertainty observability estimate effect；
Fig. 9 is yaw angle, roll angle and pitching angular direction lump uncertainty observability estimate effect；
Figure 10 be without Dynamics Optimization longitudinally, laterally with vertical control component schematic diagram；
Figure 11 is yaw angle, roll angle and pitch angle direction controlling component schematic diagram without Dynamics Optimization；
Figure 12 be through Dynamics Optimization longitudinally, laterally with vertical control component schematic diagram；
Figure 13 is yaw angle, roll angle and pitch angle direction controlling component schematic diagram through Dynamics Optimization.
Specific embodiment
The present invention is further described through with reference to the accompanying drawing.As shown in figures 1-13,
The present invention is further described by taking a specific unmanned ocean navigation device Dynamics Optimization control as an example below, Fig. 1
For structural schematic diagram of the invention, unmanned ocean navigation device meets formula in unmanned ocean navigation device Dynamics Optimization control system
(1) design parameter of the kinetic model in, model is as follows:
In this embodiment, the control target of the Dynamics Optimization controller of unmanned ocean navigation device is to guarantee unmanned ocean
Aircraft accurately tracks an outer ring and gives command signal νr.Controller meets controller architecture described in formula (1)-(11),
Specific control parameter is as follows:
Am=diag (- 3.5-4-4.5-3-4-5)；
Bm=diag (3.5 4 4.5 34 5)；
L=diag (1,000 1,000 1,000 500 500 500)；
Kr=diag (204.4 95.2 107.1 10.14 4.72 13.35)；
ε=0.00001；
τω=E sin (2 π ω t), whereinω is randomly generated.
Simulation result is as shown in Fig. 2-13.Fig. 2 is the tracking effect schematic diagram of yawing angular speed, and lines p is nobody in figure
Yawing angular velocity component in the actual speed signal of ocean navigation device, lines pmFor yawing angular speed in reference speed signal point
Amount, lines prYawing angular velocity component in command speed signal is given for outer ring, as seen from Figure 2 practical actual speed signal
Middle yawing angular velocity component can accurately track outer ring and give yawing angular velocity component in command speed signal.Fig. 3 is rolling
The tracking effect schematic diagram of angular speed, lines q is angular velocity in roll point in the actual speed signal of unmanned ocean navigation device in figure
Amount, lines qmFor angular velocity in roll component in reference speed signal, lines qrRoll angle speed in command speed signal is given for outer ring
Component is spent, angular velocity in roll component can accurately track the given finger of outer ring in practical actual speed signal as seen from Figure 3
Enable angular velocity in roll component in speed signal.Fig. 4 is the tracking effect schematic diagram of angular velocity in pitch, and lines r is no sea of faces in figure
Angular velocity in pitch component in the actual speed signal of foreign aircraft, lines rmFor angular velocity in pitch component in reference speed signal,
Lines rrAngular velocity in pitch component in command speed signal is given for outer ring, as seen from Figure 4 in practical actual speed signal
Angular velocity in pitch component can accurately track outer ring and give angular velocity in pitch component in command speed signal.Fig. 5 is longitudinal speed
The tracking effect schematic diagram of degree, lines u is range velocity component in the actual speed signal of unmanned ocean navigation device, lines in figure
umFor range velocity component in reference speed signal, lines urRange velocity component in command speed signal is given for outer ring, by scheming
5 can be seen that range velocity component can accurately track vertical in the given command speed signal of outer ring in practical actual speed signal
To velocity component.Fig. 6 is the tracking effect schematic diagram of lateral velocity, and lines v is the actual speed of unmanned ocean navigation device in figure
Lateral velocity component in signal, lines vmFor lateral velocity component in reference speed signal, lines vrCommand speed is given for outer ring
Lateral velocity component in signal, lateral velocity component can accurately track in practical actual speed signal as seen from Figure 6
Outer ring gives lateral velocity component in command speed signal.Fig. 7 is the tracking effect schematic diagram of vertical velocity, and lines ω is in figure
Vertical velocity component in the actual speed signal of unmanned ocean navigation device, lines ωmFor vertical velocity in reference speed signal point
Amount, lines ωrVertical velocity component in command speed signal is given for outer ring, as seen from Figure 7 practical actual speed signal
Middle vertical velocity component can accurately track outer ring and give vertical velocity component in command speed signal.Fig. 8 is lateral, vertical
To, vertical lump uncertainty observability estimate effect picture, lines σ in figure1, lines σ2, lines σ3It is respectively lateral, longitudinal, vertical
Lump uncertainty actual value, lines in figureLinesLinesRespectively laterally, longitudinal, vertical lump is uncertain
Observability estimate value, lateral as seen from Figure 8, longitudinal, vertical lump uncertainty can be gone out by real-time accurately observability estimate
Come.Fig. 9 is yaw angle, roll angle and pitching angular direction lump uncertainty observability estimate effect picture, lines σ in figure4, lines σ5、
Lines σ6Respectively yaw angle, roll angle and pitching angular direction lump uncertainty actual value, lines in figureLinesLine
ItemRespectively yaw angle, roll angle and pitching angular direction lump uncertainty observability estimate value, as seen from Figure 9 yawing
Angle, roll angle and pitching angular direction lump uncertainty can be come out by real-time accurately observability estimate.Figure 10 is without power
Learn optimization longitudinally, laterally with vertical control component schematic diagram, lines τ in figureu, lines τv, lines τωRespectively without dynamics
Lateral, longitudinal, the vertical control component of optimization, as seen from Figure 10 in 10S, 20S and 30S moment, the control in three directions
Component exceeds the boundary up and down of input constraint.Figure 11 is yaw angle, roll angle and the control of pitching angular direction without Dynamics Optimization
Component schematic diagram processed, lines τ in figurep, lines τq, lines τrYaw angle, roll angle and pitching respectively without Dynamics Optimization
Angular direction controls component, and as seen from Figure 11 in 10S and 30S moment, the control component in three directions will appear spike
With burr.Figure 12 is through Dynamics Optimization longitudinally, laterally with vertical control component schematic diagram, lines τ in figureu, lines τv, line
τωRespectively lateral, longitudinal, the vertical control component through Dynamics Optimization, the control component in three directions can be seen by Figure 12
Meet the boundary up and down of input constraint.Figure 13 is yaw angle, roll angle and pitch angle direction controlling component through Dynamics Optimization
Schematic diagram, lines τ in figurep, lines τq, lines τrYaw angle, roll angle and the control of pitching angular direction respectively through Dynamics Optimization
Component processed, there is not spike and burr in the control component in three directions as seen from Figure 13, and variation is smooth continuous.
The present invention is not limited to the present embodiment, any equivalent concepts within the technical scope of the present disclosure or changes
Become, is classified as protection scope of the present invention.
Claims (2)
1. a kind of unmanned ocean navigation device Dynamics Optimization controller architecture, it is characterised in that: adjusted including disturbance observer, instruction
Save device and Dynamics Controller, the input terminal of the disturbance observer respectively with the output end of unmanned ocean navigation device, dynamics
The output end of controller is connected；The input terminal of the Dynamics Controller respectively with the output end of unmanned ocean navigation device, disturbance
The output end of observer is connected with the output end of demand regulator；The input terminal of described instruction adjuster respectively with dynamics Controlling
The output end of device is connected with outer ring controller.
2. a kind of design method of unmanned ocean navigation device Dynamics Optimization controller architecture, it is characterised in that: the no sea of faces
The six-degree-of-freedom dynamic model of foreign aircraft is indicated with following formula:
Wherein:
Indicate the position and attitude information of unmanned ocean navigation device, wherein x, y, z, φ, θ
Length travel component, lateral displacement component, vertical deviation component, yaw angle, roll angle and pitch angle are respectively indicated with ψ；Represent a solid space；
Indicate the speed signal of unmanned vehicles, wherein u, v, w, p, q and r respectively indicate longitudinal direction
Velocity component, lateral velocity component, vertical velocity component, yawing angular speed, angular velocity in roll and angular velocity in pitch；Meet nobody
Ocean navigation device state constraint νmin≤ν≤νmax, wherein νminAnd νmaxRespectively represent the constraint bound of ν；
Indicate inertial matrix；
Represent the centripetal matrix of Coriolis；
Represent nonlinear dampling matrix；
G (ν, η) indicates the restoring force as caused by buoyancy and gravity collective effect；
Indicate the control signal of unmanned ocean navigation device, wherein τu、τv、τw、τp、τqAnd τr
Respectively indicate crosswise joint component, longitudinally controlled component, vertical control component, yaw angle control component, roll angle control component
Component is controlled with pitch angle；Meet control input constraint τmin≤τ≤τmax, wherein τminAnd τmaxRespectively represent the constraint of τ or more
Boundary；
Time-varying ocean current in marine environment is represented to bring unmanned ocean navigation device
External disturbance, wherein τwu、τwv、τww、τwp、τwqAnd τwrRespectively indicate transversely outer disturbance component, longitudinally outer disturbance component,
Vertical external disturbance component, yaw angle external disturbance component, roll angle external disturbance component and pitch angle external disturbance component；Represent the modeling error of unmodeled fluid dynamics and unmanned ocean navigation device；
The design method, comprising the following steps:
A, the design of disturbance observer
The six-degree-of-freedom dynamic model of ocean navigation device unmanned in formula (1) is rewritten into following formula:
WhereinFor a known matrix；
B=M-1；
σ ()=- C (ν) ν-D (ν) ν-Δ (ν, η)-g (η)+τw(t)-M-1A ν is represented by model uncertainty, uncertain hydrodynamic(al)
In mechanics parameter and marine environment time-varying ocean current bring external disturbance and caused by lump it is uncertain；
Disturbance observer is designed to estimate lump uncertainty σ, the input signal of disturbance observer is unmanned ocean navigation device
Speed signal ν and control signal τ, disturbance observer are expressed as follows:
WhereinFor the secondary status of disturbance observer；It is the estimated value to lump uncertainty σ；Represent one
Gain matrix；
B, the design of Dynamics Controller
The input signal of Dynamics Controller includes the output signal of demand regulatorThe speed of unmanned ocean navigation device output
The lump uncertainty estimation value of signal ν and disturbance observer outputDynamics Controller output signal τ, νmDesign is such as
Under:
WhereinSystem of representatives dynamic system response matrix meets:Its
InWithFor positive definite matrix；
WhereinMeet Am=A+BKx；Meet Bm=BKr；The Dynamics Controller output signal is τ
With νm；
C, the design of demand regulator
The input signal of demand regulator is the reference speed signal ν of Dynamics Controller outputmCommand signal is given with outer ring
νr；Output signal is to meet unmanned ocean navigation device state constraint and control optimal under the conditions of input constraint to guidance command signalThe design method of demand regulator is as follows；
The unmanned ocean navigation device dynamics reference model of formula (4) is subjected to sliding-model control and obtains discretization in demand regulator
Dynamic Prediction model:
WhereinWithRespectively AmAnd BmDiscrete form；Using rolling time horizon mode at the k moment to the ginseng in prediction time domain
Examine speed signal νmIt is predicted to obtain following formula:
Wherein:
It is the k moment to reference speed signal νmPredicted value；For the guidance at k moment
The increment of signal；InIndicate a n dimension unit matrix；N is prediction time domain, NQTo control time domain；Demand regulator optimization aim letter
Number is expressed as follows:
Wherein:It is constant vector, respectively indicates consideration
Guidance signal increment bound and guidance signal bound after controlling input constraint；
It indicates to consider the constraint bound after state constraint,It is normal for one
Number；
Q, R respectively indicates velocity state vectors weight and control input weight, and
Formula (8) is converted to following formula:
Wherein:
W=2 (MTQM+R)
Design neurodynamics optimization method is solved to obtain to objective function meets input constraint and state constraint
Optimal Guidance signal, designed neurodynamics optimization method are as follows:
Wherein:For time constant；For a normal number；It indicatesGradient vector；Table
ShowGradient vector；Projection function g[a,b](ρ)=[g[a,b](ρ1),...,g[a,b](ρn)] design method is as follows:
Wherein: ρ=[ρ1,...,ρn]T；A=0；C=1；I=1 ..., n.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910016713.XA CN109583144B (en) | 2019-01-08 | 2019-01-08 | Dynamics optimization controller structure of unmanned marine vehicle and design method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910016713.XA CN109583144B (en) | 2019-01-08 | 2019-01-08 | Dynamics optimization controller structure of unmanned marine vehicle and design method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109583144A true CN109583144A (en) | 2019-04-05 |
| CN109583144B CN109583144B (en) | 2022-10-14 |
Family
ID=65915932
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910016713.XA Active CN109583144B (en) | 2019-01-08 | 2019-01-08 | Dynamics optimization controller structure of unmanned marine vehicle and design method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109583144B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109976349A (en) * | 2019-04-12 | 2019-07-05 | 大连海事大学 | A kind of design method containing the path trace guidance and control structure that constrain unmanned boat |
| CN110262513A (en) * | 2019-07-12 | 2019-09-20 | 大连海事大学 | A kind of design method of ocean robot Trajectory Tracking Control structure |
| CN111736617A (en) * | 2020-06-09 | 2020-10-02 | 哈尔滨工程大学 | Speed observer-based benthonic underwater robot preset performance track tracking control method |
| CN112069590A (en) * | 2020-08-11 | 2020-12-11 | 西北工业大学 | Design method of micro-caliber electric propulsion underwater vehicle |
| CN112486188A (en) * | 2020-11-11 | 2021-03-12 | 河北汉光重工有限责任公司 | Underwater unmanned vehicle trajectory tracking control method and system with output constraint |
| CN112835373A (en) * | 2020-12-30 | 2021-05-25 | 中国航天空气动力技术研究院 | Online modeling and prediction control integrated method and device |
| CN114564028A (en) * | 2022-03-18 | 2022-05-31 | 大连海事大学 | Unmanned ship navigational speed control system driven by discrete time data and learned by self |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106773713A (en) * | 2017-01-17 | 2017-05-31 | 北京航空航天大学 | For the high precision nonlinear path tracking control method of drive lacking ocean navigation device |
| US9694918B1 (en) * | 2016-05-26 | 2017-07-04 | Beihang University | Method for disturbance compensation based on sliding mode disturbance observer for spacecraft with large flexible appendage |
-
2019
- 2019-01-08 CN CN201910016713.XA patent/CN109583144B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9694918B1 (en) * | 2016-05-26 | 2017-07-04 | Beihang University | Method for disturbance compensation based on sliding mode disturbance observer for spacecraft with large flexible appendage |
| CN106773713A (en) * | 2017-01-17 | 2017-05-31 | 北京航空航天大学 | For the high precision nonlinear path tracking control method of drive lacking ocean navigation device |
Non-Patent Citations (1)
| Title |
|---|
| 李国胜等: "有横摇角约束的欠驱动船舶航迹跟踪协调控制", 《武汉理工大学学报(交通科学与工程版)》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109976349B (en) * | 2019-04-12 | 2021-09-24 | 大连海事大学 | Design method of path tracking guidance and control structure of constraint-containing unmanned ship |
| CN109976349A (en) * | 2019-04-12 | 2019-07-05 | 大连海事大学 | A kind of design method containing the path trace guidance and control structure that constrain unmanned boat |
| CN110262513A (en) * | 2019-07-12 | 2019-09-20 | 大连海事大学 | A kind of design method of ocean robot Trajectory Tracking Control structure |
| CN110262513B (en) * | 2019-07-12 | 2022-01-28 | 大连海事大学 | Design method of marine robot trajectory tracking control structure |
| CN111736617A (en) * | 2020-06-09 | 2020-10-02 | 哈尔滨工程大学 | Speed observer-based benthonic underwater robot preset performance track tracking control method |
| CN111736617B (en) * | 2020-06-09 | 2022-11-04 | 哈尔滨工程大学 | Track tracking control method for preset performance of benthonic underwater robot based on speed observer |
| CN112069590A (en) * | 2020-08-11 | 2020-12-11 | 西北工业大学 | Design method of micro-caliber electric propulsion underwater vehicle |
| CN112069590B (en) * | 2020-08-11 | 2022-02-18 | 西北工业大学 | Design method of micro-caliber electric propulsion underwater vehicle |
| CN112486188A (en) * | 2020-11-11 | 2021-03-12 | 河北汉光重工有限责任公司 | Underwater unmanned vehicle trajectory tracking control method and system with output constraint |
| CN112486188B (en) * | 2020-11-11 | 2023-05-02 | 河北汉光重工有限责任公司 | Method and system for tracking and controlling track of underwater unmanned aircraft with output constraint |
| CN112835373A (en) * | 2020-12-30 | 2021-05-25 | 中国航天空气动力技术研究院 | Online modeling and prediction control integrated method and device |
| CN112835373B (en) * | 2020-12-30 | 2023-05-26 | 中国航天空气动力技术研究院 | Online modeling and predictive control integrated method and device |
| CN114564028A (en) * | 2022-03-18 | 2022-05-31 | 大连海事大学 | Unmanned ship navigational speed control system driven by discrete time data and learned by self |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109583144B (en) | 2022-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109583144A (en) | A kind of the Dynamics Optimization controller architecture and design method of unmanned ocean navigation device | |
| CN109116856B (en) | Under-actuated ship path tracking control method based on disturbance observer | |
| Li et al. | Robust adaptive motion control for underwater remotely operated vehicles with velocity constraints | |
| Zhou et al. | Adaptive robust sliding mode control of autonomous underwater glider with input constraints for persistent virtual mooring | |
| CN108803632A (en) | Unmanned surface vehicle total state constrained trajectory tracking and controlling method based on saturation compensation technology | |
| Zhang et al. | Novel DVS guidance and path-following control for underactuated ships in presence of multiple static and moving obstacles | |
| CN105807789B (en) | UUV control methods based on the compensation of T-S Fuzzy Observers | |
| Bui et al. | A ship berthing system design with four tug boats | |
| Sun et al. | A formation collision avoidance system for unmanned surface vehicles with leader-follower structure | |
| CN111487966B (en) | Self-adaptive path tracking control method for unmanned surface vehicle based on waypoints | |
| CN108829099A (en) | A kind of drive lacking unmanned boat trace tracking method based on the control of limited Backstepping | |
| CN109100939A (en) | Consider the unmanned surface vehicle total state constrained trajectory tracking and controlling method of input saturation | |
| CN112147899B (en) | Underwater robot autonomous obstacle avoidance control method based on fuzzy sliding mode algorithm | |
| CN111857165B (en) | Trajectory tracking control method of underwater vehicle | |
| Du et al. | Cooperative multi-agent control for autonomous ship towing under environmental disturbances | |
| Li et al. | Soft-switching proximate time optimal heading control for underactuated autonomous underwater vehicle | |
| Zhou et al. | MPC-based path tracking control method for USV | |
| Zhang et al. | Tracking fault-tolerant control based on model predictive control for human occupied vehicle in three-dimensional underwater workspace | |
| CN113093804B (en) | Unmanned ship formation control method and control system based on inversion sliding mode control | |
| Yao et al. | Model predictive and adaptive neural sliding mode control for three-dimensional path following of autonomous underwater vehicle with input saturation | |
| CN110647161B (en) | Under-actuated UUV horizontal plane trajectory tracking control method based on state prediction compensation | |
| Cavanini et al. | LPV-MPC path planner for autonomous underwater vehicles | |
| CN113093739B (en) | Multi-unmanned-ship formation collision-prevention optimal controller, structure and design method thereof | |
| Jiang et al. | Design of novel S-plane controller of autonomous underwater vehicle established on sliding mode control | |
| Chu et al. | Obstacle Avoidance Trajectory Planning and Trajectory Tracking Control for Autonomous Underwater Vehicles |
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 |