CN109343347A - A kind of Trajectory Tracking Control method of seabed flight node - Google Patents
A kind of Trajectory Tracking Control method of seabed flight node Download PDFInfo
- Publication number
- CN109343347A CN109343347A CN201811189823.8A CN201811189823A CN109343347A CN 109343347 A CN109343347 A CN 109343347A CN 201811189823 A CN201811189823 A CN 201811189823A CN 109343347 A CN109343347 A CN 109343347A
- Authority
- CN
- China
- Prior art keywords
- obfn
- formula
- coordinate system
- kinetic
- error
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
A kind of Trajectory Tracking Control method of seabed flight node, the present invention relates to the Trajectory Tracking Control methods of seabed flight node.The purpose of the present invention is to solve existing methods to lack the control ability to track following error convergence dynamic process, it is difficult to the problem of realizing overshoot limitation, default and arbitrary accuracy the tracking of error convergence time.Detailed process are as follows: one, the kinetic model based on Fossen outline six degree of freedom Building Nonlinear Model OBFN；Two, the kinetic model of the OBFN established to one converts, and obtains the kinetic model of transformed OBFN；Three, performance function is defined；Four: the kinetic model of the transformed OBFN obtained according to the performance function of three definition by two carries out error transform；Five, radial basis function neural network parameter is chosen；Six, based on the four and five adaptive contrail trackers of design.The present invention is used for the Trajectory Tracking Control field of seabed flight node.
Description
Technical field
The present invention relates to the Trajectory Tracking Control methods of seabed flight node.
Background technique
In recent years, Autonomous Underwater Vehicle (Autonomous underwater vehicle, AUV) is in marine environment
The research fields such as observation, military information collection, which have been obtained, to be widely applied.With the enhancing of ocean development dynamics, AUV's
Using the gradually expansion from observation to slight operation.Such as underwater foundation facility inspection, deep-sea oil gas exploration etc..Wherein, extra large
Bottom flight node (Ocean bottom flying node, OBFN) is exactly a kind of AUV expanded, carries seismic detection
Instrument can be explored, as shown in Figure 1 with large scale deployment to sea bed surface for deep-sea oil gas resource.
Track following is a basic function of AUV control system, but since nonlinearity, cross-linked system are dynamic
Mechanical characteristic and uncertain complicated underwater environment, introduce biggish model uncertainty to AUV system dynamics model
And external disturbance, and then increase the difficulty of controller design.And due to the complication of mission requirements, it will further increase
Requirement to AUV control precision.
AUV common disturbance and uncertainty includes ocean current disturbance, propeller failure and modeling uncertainty.Common place
Reason method is usually to consider the wherein influence of some or several factors to Trajectory Tracking Control, and analysis result is not comprehensive enough；
Or algorithm effect of constraint value is separately designed for above-mentioned factor, analytic process is excessively complicated.In recent years, occur a kind of by ocean current
The factors such as disturbance, propeller failure, modeling uncertainty are accordingly to be regarded as system always probabilistic method, and are approached with neural network
Total uncertainty of system.But this method lacks the control ability to track following error convergence dynamic process, especially face
Have the characteristics that large scale deployment, high-precision track following to OBFN this kind and sit heavy seabed, so that strict control be needed to miss
The AUV of difference convergence dynamic process is difficult to realize default and arbitrary accuracy the tracking of overshoot limitation, error convergence time.
Wherein, overshoot refers to an index of the Trajectory Tracking Control System of evaluation OBFN, refers to that the maximum of controlled variable and given value is inclined
Poor (the smaller the better).
Summary of the invention
The purpose of the present invention is to solve existing methods to lack the control energy to track following error convergence dynamic process
Power, it is difficult to the problem of realizing overshoot limitation, default and arbitrary accuracy the tracking of error convergence time, and propose a kind of seabed
The Trajectory Tracking Control method of flight node.
A kind of Trajectory Tracking Control method detailed process of seabed flight node are as follows:
Step 1: the kinetic model based on Fossen outline six degree of freedom Building Nonlinear Model OBFN；
The kinetic model of OBFN is used to be indicated based on Fossen outline six degree of freedom nonlinear model:
The OBFN is seabed flight node；
In formula, MηFor the induced variable of M, M is the mass inertia matrix of OBFN；CRBηFor CRBInduced variable, CRBFor OBFN
Rigid body coriolis force and centripetal force matrix；CAηFor CAInduced variable, CAFor the coriolis force and centripetal force of OBFN additional mass
Matrix；DηFor the induced variable of D, D is hydrodynamic damping matrix；gηThe power and torque vector generated for OBFN gravity and buoyancy, η
The six-degree of freedom position and attitude vectors that are OBFN under fixed coordinate system；For the first derivative of η；For the second dervative of η；For first derivative of the OBFN under fixed coordinate system relative to the motion vector of ocean current；τ is the actual of the propeller of OBFN
Control force；
Step 2: converting to the kinetic model for the OBFN that step 1 is established, the power of transformed OBFN is obtained
Learn model；
Step 3: defining performance function；
Step 4: the kinetic model for the transformed OBFN that the performance function defined according to step 3 obtains step 2
Carry out error transform；
Step 5: choosing radial basis function neural network parameter；
Step 6: designing adaptive contrail tracker based on step 4 and step 5.
The invention has the benefit that
Position and Attitude tracking control problem of the present invention for OBFN comprehensively consider ocean current, modeling uncertainty and push away
It is influenced into caused by device failure, proposes a kind of neural network control device based on default capabilities method, to realize pair
The Trajectory Tracking Control of OBFN.Firstly, traditional AUV six-degree-of-freedom dynamics are equations turned for the disturbance of consideration ocean current, modeling
Uncertain and propeller failure OBFN kinetics equation.Secondly, design performance function and corresponding error transform, by OBFN
Track following error system be converted into " no constraint " system of equivalence a kind of.Later, radial basis function neural network is utilized
(RBFNN) it approaches always uncertain by ocean current, the uncertain system formed with propeller failure of modeling, and introduces adaptively
Technology estimates the upper bound of the total probabilistic error of RBFNN approximation system.Finally, the contrail tracker of design OBFN, it will
The total uncertainty value of the system that RBFNN is approached and the resulting upper error value substitution of ART network counteract system and always do not know
Influence of the property to the Trajectory Tracking Control System of OBFN, and " no constraint " closed-loop system is demonstrated by Lyapunov's theory
Stability so that original OBFN track following obtains default capabilities, that is, realize OBFN track following overshoot limitation,
Default and arbitrary accuracy the track demand of error convergence time.
Fig. 3-Fig. 8 gives the 6DOF track following error curve of OBFN.Wherein, block curve, which represents, applies this hair
The position of bright default capabilities adaptive tracing device (39)-(41) and attitude angle tracking error curve, dashed curve represents preset
Performance bounds.It can be seen that the six-degree of freedom position of OBFN by Fig. 3-Fig. 8 and posture angle tracking error be in always by performance
Within the default capabilities boundary that function defines, since default capabilities boundary represents desired error convergence process, this hair
Bright proposed method realizes desired error convergence dynamic process and steady state controling precision.Simulation parameter according to the present invention
Setting, the track following steady-state error of OBFN are lower than 0.0035, and error convergence speed is greater than e-0.15t。
Detailed description of the invention
Fig. 1 is seabed flight node schematic diagram；
Fig. 2 is OBFN propeller arrangement schematic diagram, and T-1, T-2, T-3, T-4, T-5, T-6 are the propeller of OBFN；
Fig. 3 is the surging tracking error e under propeller catastrophic failure1Schematic diagram；
Fig. 4 is the swaying tracking error e under propeller catastrophic failure2Schematic diagram；
Fig. 5 is the heaving tracking error e under propeller catastrophic failure3Schematic diagram；
Fig. 6 is the roll tracking error e under propeller catastrophic failure4Schematic diagram；
Fig. 7 is the following in elevation error e under propeller catastrophic failure5Schematic diagram；
Fig. 8 is to shake first tracking error e under propeller catastrophic failure6Schematic diagram.
Specific embodiment
Specific embodiment 1: a kind of Trajectory Tracking Control method detailed process of seabed flight node of present embodiment
Are as follows:
Step 1: the kinetic model based on Fossen outline six degree of freedom Building Nonlinear Model OBFN；
Inertial coodinate system (E- ξ η ζ): origin E can be selected in the certain point on sea, and E ξ axis and E η axis are placed in horizontal plane and mutual
Perpendicular, E ξ axis forward direction is directed toward direct north.E ζ is directed toward the earth's core perpendicular to E ξ η plane, forward direction.
Kinetic coordinate system (G-xyz): origin G takes in the center of gravity of OBFN, and x-axis, y-axis and z-axis are respectively to pass through origin
Water Plane, cross section and central fore-and-aft vertical plane intersection.
The kinetic model of OBFN, which can be used, indicates [1] (Fossen based on Fossen outline six degree of freedom nonlinear model
T I.Handbook of Marine Craft Hydrodynamics and Motion Control [M] .2011.):
The OBFN is seabed flight node；
In formula, MηFor the induced variable of M, M is the mass inertia matrix of OBFN；CRBηFor CRBInduced variable, CRBFor OBFN
Rigid body coriolis force and centripetal force matrix；CAηFor CAInduced variable, CAFor the coriolis force and centripetal force of OBFN additional mass
Matrix；DηFor the induced variable of D, D is hydrodynamic damping matrix；gη=g (η), gηThe power and power generated for OBFN gravity and buoyancy
Square vector, η are six-degree of freedom position and attitude vectors of the OBFN under fixed coordinate system；For the first derivative of η；It is the two of η
Order derivative；For first derivative of the OBFN under fixed coordinate system relative to the motion vector of ocean current；τ is the propeller of OBFN
Actual control force；
Default capabilities control method: this method by introducing performance function and error transform, make convergence rate, overshoot and
Tracking error obtains preset performance, relaxes the requirement chosen to control parameter to a certain extent.
Radial basis function neural network: being a kind of feedforward network of construction, this kind of net based on function approaches theory
The study of network is equivalent to find the best-fitting plane of training data in hyperspace.The structure of the neural network is simple, instructs
Practice succinct, study fast convergence rate, being capable of Approximation of Arbitrary Nonlinear Function.
Step 2: the kinetic model of OBFN established to step 1 converts, obtain one kind consider ocean current disturbance,
The kinetic model of the uncertain transformed OBFN influenced with propeller failure of modeling；
Step 3: defining performance function；
Step 4: the kinetic model for the transformed OBFN that the performance function defined according to step 3 obtains step 2
(3) error transform is carried out；
Step 5: choosing radial basis function neural network parameter；
Step 6: designing adaptive contrail tracker based on step 4 and step 5.
Specific embodiment 2: the present embodiment is different from the first embodiment in that: in the step 1
The induced variable M of Mη=MJ-1, transition matrix of the J between fixed coordinate system and kinetic coordinate system；
CRBInduced variable For the first derivative of J, v is OBFN in the coordinates of motion
Speed and angular speed under system, v=[u ', a, w, p, q, r]T,
In formula, u ' is OBFN surging speed under kinetic coordinate system, and a is that OBFN swaying speed, w under kinetic coordinate system are
OBFN heaving speed under kinetic coordinate system, p are OBFN heel angular speed under kinetic coordinate system, and q is OBFN in the coordinates of motion
It is lower pitch velocity, r is that OBFN shakes first angular speed under kinetic coordinate system, and superscript T is matrix transposition symbol；
CAInduced variable CAη=CA(vr)J-1, vrSpeed for OBFN relative to ocean current；
The induced variable D of Dη=D (vr)J-1；
Six-degree of freedom position of the OBFN under fixed coordinate system and attitude vectors η=[x, y, z, φ, θ, ψ]T,
In formula, x is that x-axis direction is displaced under OBFN fixed coordinate system, and y is that the y-axis direction under fixed coordinate system OBFN is displaced,
Z is that the z-axis direction under fixed coordinate system OBFN is displaced, and φ is OBFN angle of heel under fixed coordinate system, and θ is OBFN in fixation
Trim angle under coordinate system, ψ are that OBFN shakes first angle under fixed coordinate system；
First derivative of the OBFN under fixed coordinate system relative to the motion vector of ocean currentvr=v-vc, vc
For the speed of ocean current under kinetic coordinate system.
Other steps and parameter are same as the specific embodiment one.
Specific embodiment 3: the present embodiment is different from the first and the second embodiment in that: it is right in the step 2
The kinetic model for the OBFN that step 1 is established is converted, and is obtained one kind and is considered ocean current disturbance, modeling uncertainty and push away
The kinetic model of the transformed OBFN influenced into device failure；
The model uncertainty considered for the invention patent and ocean current disturbance, propeller failure, consider its feasible number
Learn expression-form.
The failure of the propeller of OBFN influences that Δ B [3] (Wang can be defined as using the expression of thrust allocation matrix form
Y,Zhang M,Wilson P A,et al.Adaptive neural network-based backstepping fault
tolerant control for underwater vehicles with thruster fault[J].Ocean
Engineering,2015,110:15-24.)；
The actual control force and torque of the propeller of OBFN can be rewritten as τ+Δ τ:
τ+Δ τ=(B0- KB) u=(B0+ΔB)u (2)
In formula, B0It is the nominal value (nominal value is actual measurement acquisition) of thrust allocation matrix B, u is the propeller of OBFN
Control input, B be thrust allocation matrix, K is a diagonal matrix, element kii∈ [0,1] indicates corresponding propeller event
Barrier degree, 1 indicates that fault degree is high.
Formula (1) can transform to OBFN kinetic model:
In formula, Mη0For M induced variable MηNominal value, CRBη0For CRBInduced variable CRBηNominal value, CAη0For CAExport
Variable CAηNominal value, Dη0For D induced variable DηNominal value, gη0For gηNominal value, subscript 0 indicate nominal value；F is indicated
The overall uncertainty of the Trajectory Tracking Control System of OBFN.
Other steps and parameter are the same as one or two specific embodiments.
Specific embodiment 4: unlike one of present embodiment and specific embodiment one to three: the OBFN's
The overall uncertainty F expression formula of Trajectory Tracking Control System is as follows:
In formula,It indicates to influence caused by ocean current disturbance；Δ indicates uncertain value, Δ MηFor MηIt is uncertain
Value, Δ CRBηFor CRBηUncertain value, Δ CAηFor CAηUncertain value, Δ DηFor DηUncertain value, Δ gηFor gravity and float
The uncertain value of power and torque vector that power generates, ηrFor OBFN relative to the motion vector of ocean current under fixed coordinate system.
A value can be manually set in uncertain value in simulations, for proving that proposed method can effectively overcome
This uncertainty.
Other steps and parameter are identical as one of specific embodiment one to three.
Specific embodiment 5: unlike one of present embodiment and specific embodiment one to four: the step 3
Middle definition performance function；Detailed process are as follows:
Define a performance function:
ρ (t)=(ρ0-ρ∞)e-kt+ρ∞ (5)
In formula, ρ0For depending on the initial control precision of OBFN normal number (OBFN under fixed coordinate system initially
Position and the initial position of desired trajectory, ρ0Need it is more somewhat larger than the difference of the two, not so system carve at the beginning just send out
It dissipates)；ρ∞(the track following control of OBFN is wished depending on controller for the normal number depending on the control precision of OBFN stable state
Which type of precision is system processed can finally reach, this value is exactly final accuracy value)；K is the track following control according to OBFN
Normal number depending on the rate of convergence of system processed, k value is bigger, and convergence rate is faster；ρ (t) is performance function, and t is the time；
Utility function ρ (t) is by position of the OBFN under fixed coordinate system and attitude error ei(t) it indicates are as follows:
In formula, eiIt (t) is position and attitude error of the OBFN under fixed coordinate system, i is variable, due to the position OBFN
It include 6 freedom degrees with attitude error, therefore i=1,2,3,4,5,6；δiFor variable, 0≤δi≤1；ρi(t) freely for i-th
The performance function of degree；
Known according to the form of performance function formula (5) and formula (6), if the position OBFN and attitude error ei(t) just
Value satisfaction 0≤| | ei(0)||≤ρi(0), then k limits the minimum rate of convergence of tracking error, and ρi∞Given the steady of permission
The upper bound of state tracking error, while the overshoot of the Trajectory Tracking Control System response of OBFN does not exceed δiρi(t)；
ρi∞The upper bound for the steady track error allowed in i-th of freedom degree.
Other steps and parameter are identical as one of specific embodiment one to four.
Specific embodiment 6: unlike one of present embodiment and specific embodiment one to five: the step 4
The kinetic model of OBFN transformed in step 2 is carried out error transform by the middle performance function defined according to step 3；Specifically
Process are as follows:
OBFN kinetic simulation pattern (3) in step 2 is carried out error transform by the performance function defined according to step 3；
Unconfined stable control, definition auxiliary are converted for the tracking control problem under constraining using a kind of error transform mode
Function Si(εi):
In formula, εi∈ (- ∞ ,+∞) is known as mapping fault；
Auxiliary function Si(εi) have the property that
(1)Si(εi) smooth and strictly monotone increasing；
(2)
(3)
According to Si(εi) characteristic, formula (6) can be expressed equivalently as
ei(t)=ρi(t)Si(εi) (8)
Because of Si(εi) it is strictly monotone increasing, so obtaining mapping fault ε according to inverse function there are inverse functioni:
The tracking control problem of OBFN kinetic simulation pattern (3) is just converted into ε at this timeiFor variable closed-loop system it is steady
Determine control problem；
Consider Si(εi) form that takes equation (7), then have
In formula, ziFor the auxiliary variable of i-th of freedom degree, zi=ei(t)/ρi(t)；
Enable εiSingle order and second dervative are sought to time t:
In formula, riFor the auxiliary variable of i-th of freedom degree,It can be calculated by formula (10)
It obtains,For riFirst derivative,Indicate the actual position OBFN and attitude angle,Indicate the desired position OBFN and posture
Angle, i=1,2,3,4,5,6；For the first derivative of the auxiliary variable of i-th of freedom degree, eiIt is OBFN under fixed coordinate system
Position and attitude error,For eiFirst derivative,For the first derivative of i-th of freedom degree performance function；It is i-th
The second dervative of freedom degree performance function；
Take error variance s ∈ R6For following form
In formula, ε=[ε1,ε2,ε3,ε4,ε5,ε6]T, λ=diag [λ1,λ2,λ3,λ4,λ5,λ6] > 0 be parameter to be designed；
For the first derivative of ε；T is transposition；R is real number field；εiFor mapping fault, λiFor the parameter to be designed of i-th of freedom degree, i=1,
2,3,4,5,6；
NoteD=-F
OBFN kinetic simulation pattern (3) is abbreviated as following formula:
In formula, A, B, D are intermediate variable；
And then have:
In formula,For the first derivative of s,For the first derivative of ε,For the second dervative of ε；L, R are intermediate variable.
Other steps and parameter are identical as one of specific embodiment one to five.
Specific embodiment 7: unlike one of present embodiment and specific embodiment one to six: described
L=[l1,l2,l3,l4,l5,l6]T,
I=1,2,3,4,5,6,
R=diag [r1,r2,r3,r4,r5,r6]
In formula, liFor the intermediate variable of i-th of freedom degree,For the auxiliary variable r of i-th of freedom degreeiFirst derivative.
Other steps and parameter are identical as one of specific embodiment one to six.
Specific embodiment 8: unlike one of present embodiment and specific embodiment one to seven: the step 5
Middle controller chooses radial basis function neural network parameter；Detailed process are as follows:
There are uncertain nonlinearities D in simplified OBFN kinetic simulation pattern (14), using RBF neural to not
Determine that nonlinear terms D is approached, process are as follows:
Neural network input is taken asThen the estimation of uncertain nonlinearities D can be written as
In formula,For the estimated value of weight matrix,
H (x) is radial basis function, h (x)=[h1(x),h2(x),...,hj(x),...hm(x)T]∈Rm, m is RBF nerve
Network the number of hidden nodes；hj(x) it is the radial basis function of jth dimension, the form of Gaussian bases can be used；1≤j≤m；
E=[e1,e2,e3,e4,e5,e6], eiThe position for being OBFN under fixed coordinate system and attitude error, i=1,2,
3,4,5,6,Reciprocal for the single order of e, T is transposition.
Other steps and parameter are identical as one of specific embodiment one to seven.
Specific embodiment 9: unlike one of present embodiment and specific embodiment one to eight: the RBF nerve
Network the number of hidden nodes m >=3.
Other steps and parameter are identical as one of specific embodiment one to eight.
Specific embodiment 10: unlike one of present embodiment and specific embodiment one to nine: the step 6
In adaptive contrail tracker designed based on step 4 and step 5；Detailed process are as follows:
In view of the upper bound μ * of approximate error is unknown, the present invention proposes following adaptive contrail tracker:
In formula,It indicates to approximate error upper bound μ*Estimation (formula 19 obtains),ForSingle order it is reciprocal, | | s | | be
Two norms of error variance s, K > 0, σ > 0 are control parameter to be designed；τwi> 0, β > 0, τμ> 0, γ > 0 is adaptive
Gain；S=[s1,s2,s3,s4,s5,s6], siFor some value in error variance s, i=1,2,3,4,5,6.
Formula (17) is controller, and formula (18) and formula (19) are the attached adaptive laws of controller.
Other steps and parameter are identical as one of specific embodiment one to nine.
Theoretical basis
The kinetic model of OBFN
The kinetics equation of OBFN, which can be used, indicates [1] (Fossen based on Fossen outline six degree of freedom nonlinear model
T I.Handbook of Marine Craft Hydrodynamics and Motion Control [M] .2011.):
In formula, Mη=MJ-1；CAη=CA(vr)J-1；Dη=D (vr)J-1；Gh=g
(η)；vr=v-vc；M is mass inertia matrix, η=[x, y, z, φ, θ, ψ]TIndicate OBFN in inertial coodinate system
Under six-degree of freedom position and posture, v=[u, v, w, p, q, r]TIndicate that speed and angular speed, J under kinetic coordinate system are used
Transition matrix between property coordinate system and kinetic coordinate system；CRBIt is the coriolis force and centripetal force matrix of rigid body, CAIt is additional mass
Coriolis force and centripetal force matrix；D is hydrodynamic damping matrix, gηThe power and torque vector generated for gravity and buoyancy, τ are to push away
The control force and torque generated into system, vrIt is speed of the OBFN relative to ocean current, vcIt is the speed of ocean current under kinetic coordinate system.
Propeller is the important component of OBFN and the main source of failure problems.The failure influence of propeller can
It is indicated in the form of using thrust allocation matrix, is defined as Δ B.Therefore, actual control force and torque can be rewritten as τ+Δ τ:
τ+Δ τ=(B0- KB) u=(B0+ΔB)u (21)
In formula, B0It is the nominal value of thrust allocation matrix, u is the control action of propeller, and K is a diagonal matrix,
Element kii∈ [0,1] indicates corresponding propeller fault degree.Therefore, equation (20) can transform to:
In formula, subscript 0 indicates nominal value；F indicates the overall uncertainty of system, and expression formula is as follows:
In formula,It indicates to influence caused by ocean current disturbance；Δ indicates uncertain value.
Control target of the invention can be stated are as follows: design controller u make OBFN there are systematic uncertainty and promote
In the case where device failure, position and attitude vectors η still are able to tracking desired value ηd, and make tracking error e=η-ηdWith pre-
First given dynamic property and steady-state response situation.
In conjunction with Practical Project background it is proposed that 3 hypothesis:
Assuming that 1 position and attitude vectors η and its first derivativeIt can survey.
Assuming that 2 desired positions and attitude angle ηdThe known and bounded with its single order, second dervative.
Assuming that the overall uncertainty F bounded of 3 systems, i.e., | | F | |≤χ, wherein χ is unknown normal number.
Default capabilities control method
A kind of common performance function [2] (Bechlioulis C P, Rovithakis G A.Robust as follows
Adaptive Control of Feedback Linearizable MIMO Nonlinear Systems With
Prescribed Performance[J].IEEE Transactions on Automatic Control,2008,53(9):
2090-2099.):
ρ (t)=(ρ0-ρ∞)e-kt+ρ∞ (24)
In formula, ρ0、ρ∞It is previously given normal number with k.It meets following condition:
(1) ρ (t) monotone decreasing and perseverance is positive；
(2)Then ρ (t) is referred to as a performance function.
Tracking error can be expressed as by utility function
In formula, ei(t), i=1,2,3,4,5,6 be the position OBFN and attitude error, 0≤δi≤1.According to performance function
(24) and the form of formula (25) it is found that if tracking error initial value meet 0≤| | ei(0)||≤ρi(0), then parameter kiIt limits
The minimum rate of convergence of tracking error, and ρi∞Given the upper bound of the steady track error of permission, the overshoot of simultaneity factor response
δ is not exceedediρi(t).Therefore, performance function ρ appropriate is designedi(t) and δiIt can be obtained desired systematic error response.
To solve the default capabilities control problem that is indicated by formula (25), using a kind of error transform mode will under constraint with
Track control problem is converted into unconfined stable control.Defined function Si(εi), it has the property that
(1)Si(εi) smooth and strictly monotone increasing；
(2)
(3)
In formula, εi∈ (- ∞ ,+∞) is known as mapping fault.Meet a function S of above-mentioned conditioni(εi) it is given by:
According to Si(εi) characteristic, formula (25) can be expressed equivalently as
ei(t)=ρi(t)Si(εi) (27)
Because of Si(εi) it is strictly monotone increasing, so there are inverse functions
If ε can be controllediBounded can then guarantee that formula (25) are set up, into performance function ρi(t) make under constraint with
Track error reaches expectation target.The tracking control problem of system (22) is just converted into ε at this timeiFor variable closed-loop system it is steady
Determine control problem.
Consider Si(εi) form that takes equation (26), then have
In formula, zi=ei(t)/ρi(t)
Enable εiSingle order and second dervative are asked to time t respectively:
In formula,It can be calculated and be obtained by formula (29)It respectively indicates
The actual position OBFN and attitude angle and desired position and attitude angle.Due toAnd ρi(t) r known to > 0iPerseverance is greater than
Zero, as long as and error ei(t) track is strictly limited in the range of formula (25), then riBounded meetsWithFor
Normal number.
Take error variance s ∈ R6For following form
In formula, ε=[ε1,ε2,ε3,ε4,ε5,ε6]T, λ=diag [λ1,λ2,λ3,λ4,λ5,λ6] > 0 be parameter to be designed.
Kinetic model (22) according to OBFN:
NoteD=-F, model (22) can be write a Chinese character in simplified form
Such as following formula:
And then have:
In formula, L=[l1,l2,l3,l4,l5,l6]T, R=diag [r1,r2,r3,r4,
r5,r6].If design controller u makes s bounded, ε can be obtained according to formula (34)iWithBounded.
Adaptive Attitude Tracking controller design
There are uncertain nonlinearities D in system (33), approached using RBF neural, i.e.,
D=W*Th(x)+μ (35)
In formula,For neural network input vector, h (x)=[h1(x),h2(x),...,hj(x),...hm
(x)]T∈Rm, m is network the number of hidden nodes.hj(x) form for generalling use Gaussian bases, has
In formula, cjFor the center vector of j-th of node in network, cj=[cj1,cj2,...,cjq]TFor the sound stage width of node j
Value.It is the ideal weight battle array of network, μ ∈ R6For approximate error, and meet | |
μ||≤μ*,μ*For unknown normal number.For weight matrix W ∈ Rm+3, the W of ideal situation*It is defined as
Neural network input is taken asThen the estimation of indeterminate D can be written as
In formula,For weight matrix W*Estimation.
In summary analytic process, and consider the upper bound μ of approximate error*It is unknown, propose following adaptive control laws
In formula,It indicates to approximate error upper bound μ*Estimation, K > 0, σ > 0, τwi> 0, β > 0, τμ> 0, γ > 0 is
Control parameter and adaptive gain to be designed.It can be seen that working as OBFN kinetic model (22), changed by error transform (28)
For error system (34), if controller u is designed as the form of formula (39) and using the adaptive law of formula (40) and (41),
Mapping fault εiUniform ultimate bounded, and tracking error eiMeet default capabilities constraint formula (25).
It proves: because of matrix R symmetric positive definite and riBounded, can choose Lyapunov function is
In formula,For corresponding evaluated error, Γw=diag [τw1,τw2,τw3,τw4,τw5,
τw6].To V derivation and substitutes into formula (34), controller (39) and adaptive law (40) and (41) and can obtain
Have using Young inequality
According to adaptive law (41)So having
Further abbreviation formula (43)
It enablesWork as known to thenOrOrWhen, it is availableTherefore variable s, evaluated error matrixAnd evaluated errorUniform ultimate bounded,
And set is converged on respectively:
In formula, λmin(K) minimal eigenvalue of representing matrix K.And then there is mapping fault εiUniform ultimate bounded, and restrain
In
Further according to function Si(εi) property 2, can obtain constraint formula (25), i.e. the tracking error of OBFN kinetic model (22)
eiPreassigned dynamic property and steady-state response are obtained, card is finished.
Beneficial effects of the present invention are verified using following embodiment:
Embodiment one:
A kind of Trajectory Tracking Control method of seabed flight node of the present embodiment is specifically to be prepared according to the following steps:
Compared with prior art
If realizing seabed flight node-locus under the influence of ocean current disturbance, model uncertainty and propeller failure
The control requirement of tracking, the schemes such as scheme, adaptive neural network other than inventive algorithm also based on fault detection, with
Both schemes are simply introduced down, and they are compared with inventive algorithm.
Scheme based on fault detection
Document [4] (Sun B, Zhu D, Yang S X.A Novel Tracking Controller for
Autonomous Underwater Vehicles with Thruster Fault Accommodation[J].Journal
Of Navigation, 2016,69 (3): 593-612.) a kind of tracking control unit adjusted based on Actuator failure is proposed, it should
After propeller failure is divided into partial fault and complete failure by method, new thrust allocation matrix is generated using weighted pseudo-inverse.When
After normal propeller reaches the thrust limit, introduction volume sub-line uses failure propeller for conditional for particle group optimizing,
And the solution of control redistribution problem is found in limitation range.Document [5] (the underwater machine of Zhang Mingjun, Chu Zhen loyalty autonomous
The fault detection of device people's propeller, separation and reconstruct [J] Nanjing Aero-Space University journal, 2011 (s1): 142-146.) it is directed to
Threshold residual value is not easy the problem of choosing in fault diagnosis of underwater robots residual error method, it is proposed that a kind of underwater machine based on observer
Device people's propeller fault detection and separation method carry out propeller failure and residual signals by building fault-detecting-observer
Decoupling, make propeller break down after only cause relative residual error in be monotonically changed, thus may be selected biggish threshold value into
Row fault detection is to improve the reliability of diagnostic system.But compared with inventive algorithm, the program is that propeller failure is independent
A set of diagnosis Fault-tolerant Model is designed, without considering that ocean current disturbance, model uncertainty etc. influence the factor of OBFN control.
Therefore inventive algorithm is improved on its basis, by disturbing ocean current, model uncertainty and propulsion
Device failure is considered as the whole uncertain of system, approaches the uncertainty using radial basis function neural network, and introduce adaptive
The upper bound of strategy estimation approximate error is answered, so that several factors that will affect OBFN control precision are contained in the design of controller
In, closer to actual engineering demand.
Scheme neural network based
Neural network is chiefly used in approaching the model uncertainty of AUV or unknown external disturbance problem, by Actuator failure
Consider together with model uncertainty and external disturbance as general uncertain part, estimates above-mentioned disturb using neural network
It is dynamic, it, can also be with by using some common control methods, such as PID control, sliding formwork control, Reverse Step Control, self adaptive control
Relatively good control program is obtained, as [6]-[8] ([6] Jia Heming, Zhang Lijun, Qi Xue waits neural network based underwater
Robot three-dimensional Track In Track controls [J] control theory and application, 2012,29 (7): 56-62. [7] Chu Zhenzhong, the big surprise base of Zhu
In autonomous type underwater robot faults-tolerant control [J] journal of Shandong university of adaptive region tracking: engineering version, 2017,47 (5):
57-63. [8] Zhang Mingjun, Chu Zhen loyalty autonomous type underwater robot adaptive region tracing control [J] mechanical engineering journal,
2014,50 (19): 50-57.).
But compared with inventive algorithm, above scheme do not account for control overshoot problem, and control precision height according to
Rely the selection in model parameter.Therefore inventive algorithm improved on its basis, by introduce default capabilities method with
Error transform makes convergence rate, overshoot and tracking error obtain preset performance, is relaxed to a certain extent to control
The requirement that parameter is chosen.
Emulation prepares
For the validity of control method designed by the verifying present invention, applies it in a kind of OBFN model and carry out emulation and test
Card, and consider influence caused by model uncertainty, ocean current disturbance, propeller failure.The corresponding hydrodynamic force system of OBFN model
Number, inertia coeffeicent and position and posture initial value difference are as shown in table 1-3.
1 OBFN hydrodynamic force coefficient of table
2 OBFN inertia coeffeicent of table
3 position OBFN of table and Attitude Simulation initial value table
Model uncertainty
For the ease of simulation analysis, the present invention is by model uncertainty quantification treatment.Consider the 20% of model nominal value
Emulation module is incorporated to as modeling error, and as a part of disturbance.
Ocean current disturbance
Single order Gauss-Markov process is introduced into the simulation process for being applied to ocean current disturbance, and expression formula is as follows:
In formula, VcIt is the size of ocean current under terrestrial coordinate system, ω is mean value and variance is 1 white Gaussian noise；μ=3.
Present invention assumes that the direction of ocean current be it is constant, it is identical as X-axis positive direction under terrestrial coordinate system.
Propeller failure
It is respectively essentially identical to arranging since the propeller arrangement of OBFN uses full drive mode, as shown in Figure 2.Therefore exist
In emulation, only considers that the propeller of a certain fixation breaks down, the fault condition of any propeller can be represented.Present invention assumes that
No. 1 propeller is failure propeller, shown in fault mode such as formula (51),
Controller parameter
It is required that systematic steady state control precision reaches 0.0035.Consider tracing control performance expected from the position OBFN and posture
Design are as follows: (1) steady track error is no more than 0.0035；(2) minimum convergence rate is limited to e-0.15t；(3) system response is without super
It adjusts.Performance function ρ can be determined accordinglyi(t) and δiValue, as shown in table 4.
4 default capabilities parameter value of table
Controller parameter is chosen for λ=diag [0.125,0.125,0.125,0.125,0.125,0.125], K=diag
[0.6,0.6,0.6,0.6,0.6,0.6], σ=0.01；Adaptive gain is chosen for τwi=τμ=0.5, β=γ=0.01；It will
The node number of RBF neural hidden layer is taken as j=7, and the center of Gaussian bases is expressed as c=[c1,...,c7], value
As shown in formula (52), sound stage width bj=0.09.
Simulation analysis
Consider that the desired track tracked is complex, most of situation can be covered to representative.Therefore, originally
For the ship trajectory that invention selects one kind spirally to decline as desired trajectory, expression is as follows:
xd=2sin (0.1t), yd=2cos (0.1t)+2, zd=-0.5144t
φd=0, θd=0, ψd=0
ηd=[xd；yd；zd；φd；θd；ψd]
In simulation analysis, propeller fault mode is based on equation (51), and considers that model uncertainty and ocean current disturb
Influence to OBFN.Fig. 3-Fig. 8 gives the 6DOF track following error curve of OBFN.Wherein, block curve represents application
This paper default capabilities adaptive tracing device (39)-(41) position and attitude angle tracking error curve, dashed curve represent default
Performance bounds.
From Fig. 3-Fig. 8 can be seen that method proposed by the invention be held in posture angle tracking error in position it is pre-
In the boundary determined by performance function first set, and obtain good dynamic process and desired steady state controling precision.
The present invention can also have other various embodiments, without deviating from the spirit and substance of the present invention, this field
Technical staff makes various corresponding changes and modifications in accordance with the present invention, but these corresponding changes and modifications all should belong to
The protection scope of the appended claims of the present invention.
Claims (10)
1. a kind of Trajectory Tracking Control method of seabed flight node, it is characterised in that: the method detailed process are as follows:
Step 1: the kinetic model based on Fossen outline six degree of freedom Building Nonlinear Model OBFN；Detailed process are as follows:
The kinetic model of OBFN is used to be indicated based on Fossen outline six degree of freedom nonlinear model:
The OBFN is seabed flight node；
In formula, MηFor the induced variable of M, M is the mass inertia matrix of OBFN；CRBηFor CRBInduced variable, CRBFor the rigid of OBFN
The coriolis force and centripetal force matrix of body；CAηFor CAInduced variable, CAFor the coriolis force and centripetal force matrix of OBFN additional mass；
DηFor the induced variable of D, D is hydrodynamic damping matrix；gηThe power and torque vector generated for OBFN gravity and buoyancy, η OBFN
Six-degree of freedom position and attitude vectors under fixed coordinate system；For the first derivative of η；For the second dervative of η；For
First derivative of the OBFN under fixed coordinate system relative to the motion vector of ocean current；τ is the actual control of the propeller of OBFN
Power；
Step 2: converting to the kinetic model for the OBFN that step 1 is established, the kinetic simulation of transformed OBFN is obtained
Type；
Step 3: defining performance function；
Step 4: the kinetic model for the transformed OBFN that the performance function defined according to step 3 obtains step 2 carries out
Error transform；
Step 5: choosing radial basis function neural network parameter；
Step 6: designing adaptive contrail tracker based on step 4 and step 5.
2. a kind of Trajectory Tracking Control method of seabed flight node according to claim 1, it is characterised in that: the step
In one
The induced variable M of Mη=MJ-1, transition matrix of the J between fixed coordinate system and kinetic coordinate system；
CRBInduced variable For the first derivative of J, v is OBFN under kinetic coordinate system
Speed and angular speed, v=[u ', a, w, p, q, r]T,
In formula, u ' is OBFN surging speed under kinetic coordinate system, and a is OBFN swaying speed under kinetic coordinate system, w OBFN
The heaving speed under kinetic coordinate system, p are OBFN heel angular speed under kinetic coordinate system, and q is OBFN under kinetic coordinate system
Pitch velocity, r are that OBFN shakes first angular speed under kinetic coordinate system, and T is transposition；
CAInduced variable CAη=CA(vr)J-1, vrSpeed for OBFN relative to ocean current；
The induced variable D of Dη=D (vr)J-1；
Six-degree of freedom position of the OBFN under fixed coordinate system and attitude vectors η=[x, y, z, φ, θ, ψ]T
In formula, x is that x-axis direction is displaced under OBFN fixed coordinate system, and y is that the y-axis direction under fixed coordinate system OBFN is displaced, and z is
The z-axis direction under fixed coordinate system OBFN is displaced, and φ is OBFN angle of heel under fixed coordinate system, and θ is that OBFN is sat fixed
Mark is lower trim angle, and ψ is that OBFN shakes first angle under fixed coordinate system；
First derivative of the OBFN under fixed coordinate system relative to the motion vector of ocean current
vr=v-vc, vcFor the speed of ocean current under kinetic coordinate system.
3. a kind of Trajectory Tracking Control method of seabed flight node according to claim 2, it is characterised in that: the step
The kinetic model of the OBFN established in two to step 1 converts, and obtains the kinetic model of transformed OBFN；Specifically
Process are as follows:
The failure of the propeller of OBFN influences to be defined as Δ B using the expression of thrust allocation matrix form；
The actual control force and torque of the propeller of OBFN are rewritten as τ+Δ τ:
τ+Δ τ=(B0- KB) u=(B0+ΔB)u (2)
In formula, B0It is the nominal value of thrust allocation matrix B, u is the control input of the propeller of OBFN, and B is thrust allocation matrix, K
It is a diagonal matrix, element kii∈[0,1]；
Formula (1) is transformed to OBFN kinetic model:
In formula, Mη0For M induced variable MηNominal value, CRBη0For CRBInduced variable CRBηNominal value, CAη0For CAInduced variable
CAηNominal value, Dη0For D induced variable DηNominal value, gη0For gηNominal value；The Trajectory Tracking Control System of F expression OBFN
Overall uncertainty.
4. a kind of Trajectory Tracking Control method of seabed flight node according to claim 3, it is characterised in that: the OBFN
Trajectory Tracking Control System overall uncertainty F expression formula it is as follows:
In formula,It indicates to influence caused by ocean current disturbance；Δ indicates uncertain value, Δ MηFor MηUncertain value, Δ
CRBηFor CRBηUncertain value, Δ CAηFor CAηUncertain value, Δ DηFor DηUncertain value, Δ gηIt is generated for gravity and buoyancy
Power and torque vector uncertain value, ηrFor OBFN relative to the motion vector of ocean current under fixed coordinate system.
5. a kind of Trajectory Tracking Control method of seabed flight node according to claim 4, it is characterised in that: the step
Performance function is defined in three；Detailed process are as follows:
Define a performance function:
ρ (t)=(ρ0-ρ∞)e-kt+ρ∞ (5)
In formula, ρ0、ρ∞, k be normal number；ρ (t) is performance function, and t is the time；
Utility function ρ (t) is by position of the OBFN under fixed coordinate system and attitude error ei(t) it indicates are as follows:
In formula, eiIt (t) is position and attitude error of the OBFN under fixed coordinate system, i is variable, due to the position OBFN and appearance
State angle error includes 6 freedom degrees, therefore i=1, and 2,3,4,5,6；δiFor variable, 0≤δi≤1；ρiIt (t) is i-th of freedom degree
Performance function.
6. a kind of Trajectory Tracking Control method of seabed flight node according to claim 5, it is characterised in that: the step
The kinetic model of OBFN transformed in step 2 is carried out error transform by the performance function defined in four according to step 3；Tool
Body process are as follows:
OBFN kinetic simulation pattern (3) in step 2 is carried out error transform by the performance function defined according to step 3；
Define auxiliary function Si(εi):
Wherein εi∈ (- ∞ ,+∞) is known as mapping fault；
Auxiliary function Si(εi) have the property that
(1)Si(εi) smooth and strictly monotone increasing；
(2)
(3)
According to Si(εi) characteristic, formula (6) is expressed equivalently as
ei(t)=ρi(t)Si(εi) (8)
Because of Si(εi) there are inverse functions, obtain mapping fault εi:
Consider Si(εi) form that takes equation (7), then have
In formula, ziFor the auxiliary variable of i-th of freedom degree, zi=ei(t)/ρi(t)；
Enable εiSingle order and second dervative are sought to time t:
In formula, riFor the auxiliary variable of i-th of freedom degree, For riFirst derivative,It indicates
The actual position OBFN and attitude angle,The desired position expression OBFN and attitude angle, i=1,2,3,4,5,6；It is i-th
The first derivative of the auxiliary variable of freedom degree, eiThe position for being OBFN under fixed coordinate system and attitude error,For eiOne
Order derivative,For the first derivative of i-th of freedom degree performance function；For the second dervative of i-th of freedom degree performance function；
Take error variance s ∈ R6For following form
In formula, ε=[ε1,ε2,ε3,ε4,ε5,ε6]T, λ=diag [λ1,λ2,λ3,λ4,λ5,λ6] > 0 be parameter to be designed；For ε's
First derivative；T is transposition；R is real number field；εiFor mapping fault, λiFor the parameter to be designed of i-th of freedom degree, i=1,2,3,
4,5,6；
OBFN kinetic simulation pattern (3) is abbreviated as following formula:
In formula, A, B, D are intermediate variable；
And then have:
In formula,For the first derivative of s,For the first derivative of ε,For the second dervative of ε；L, R are intermediate variable.
7. a kind of Trajectory Tracking Control method of seabed flight node according to claim 6, it is characterised in that: the L=
[l1,l2,l3,l4,l5,l6]T,
R=diag [r1,r2,r3,r4,r5,r6]
In formula, liFor the intermediate variable of i-th of freedom degree,For the auxiliary variable r of i-th of freedom degreeiFirst derivative.
8. a kind of Trajectory Tracking Control method of seabed flight node according to claim 7, it is characterised in that: the step
Radial basis function neural network parameter is chosen in five；Detailed process are as follows:
There are D in simplified OBFN kinetic simulation pattern (14), are approached using RBF neural D, process are as follows:
Neural network input is taken asThen the estimation of D is written as
In formula,For the estimated value of weight matrix,
H (x) is radial basis function, h (x)=[h1(x),h2(x),...,hj(x),...hm(x)T]∈Rm, m is RBF neural
The number of hidden nodes；hjIt (x) is the radial basis function of jth dimension；1≤j≤m；
E=[e1,e2,e3,e4,e5,e6], eiThe position for being OBFN under fixed coordinate system and attitude error, i=1,2,3,4,
5,6,Reciprocal for the single order of e, T is transposition.
9. a kind of Trajectory Tracking Control method of seabed flight node according to claim 8, it is characterised in that: the RBF
Neural network the number of hidden nodes m >=3.
10. a kind of Trajectory Tracking Control method of seabed flight node according to claim 9, it is characterised in that: the step
Adaptive contrail tracker is designed based on step 4 and step 5 in rapid six；Detailed process are as follows:
It is proposed following adaptive contrail tracker:
In formula,Indicate the estimation to approximate error upper bound μ *,ForSingle order it is reciprocal, | | s | | be two models of error variance s
Number, K > 0, σ > 0 are control parameter to be designed；τwi> 0, β > 0, τμ> 0, γ > 0 is adaptive gain；S=[s1,s2,
s3,s4,s5,s6], siFor some value in error variance s, i=1,2,3,4,5,6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811189823.8A CN109343347B (en) | 2018-10-12 | 2018-10-12 | Track tracking control method for submarine flight nodes |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811189823.8A CN109343347B (en) | 2018-10-12 | 2018-10-12 | Track tracking control method for submarine flight nodes |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109343347A true CN109343347A (en) | 2019-02-15 |
| CN109343347B CN109343347B (en) | 2021-10-29 |
Family
ID=65309401
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811189823.8A Active CN109343347B (en) | 2018-10-12 | 2018-10-12 | Track tracking control method for submarine flight nodes |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109343347B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109975788A (en) * | 2019-04-23 | 2019-07-05 | 中国科学技术大学 | A kind of self-adaptation control method of laser radar scanning mechanism |
| CN110532665A (en) * | 2019-08-26 | 2019-12-03 | 哈尔滨工程大学 | A kind of mobile object dynamic trajectory prediction technique under scheduled airline task |
| CN111650943A (en) * | 2020-06-19 | 2020-09-11 | 哈尔滨理工大学 | Track tracking preset performance control method for speed-limited still water dynamic positioning ship |
| CN112363538A (en) * | 2020-11-09 | 2021-02-12 | 哈尔滨工程大学 | AUV (autonomous underwater vehicle) area tracking control method under incomplete speed information |
| CN112882482A (en) * | 2021-01-27 | 2021-06-01 | 曲阜师范大学 | Fixed time trajectory tracking control method based on autonomous underwater robot with preset performance constraint |
| CN112947077A (en) * | 2021-01-29 | 2021-06-11 | 哈尔滨工程大学 | AUV robust trajectory tracking control method based on switching performance function technology |
| CN115952731A (en) * | 2022-12-20 | 2023-04-11 | 哈尔滨工业大学 | Active vibration control method, device and equipment for wind turbine blade |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106019394A (en) * | 2016-04-27 | 2016-10-12 | 中国地质科学院矿产资源研究所 | Three-dimensional parallel inversion method for nonlinear conjugate gradient of ocean magnetotelluric field |
| US9873496B2 (en) * | 2014-10-29 | 2018-01-23 | Seabed Geosolutions B.V. | Deployment and retrieval of seismic autonomous underwater vehicles |
-
2018
- 2018-10-12 CN CN201811189823.8A patent/CN109343347B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9873496B2 (en) * | 2014-10-29 | 2018-01-23 | Seabed Geosolutions B.V. | Deployment and retrieval of seismic autonomous underwater vehicles |
| CN106019394A (en) * | 2016-04-27 | 2016-10-12 | 中国地质科学院矿产资源研究所 | Three-dimensional parallel inversion method for nonlinear conjugate gradient of ocean magnetotelluric field |
Non-Patent Citations (3)
| Title |
|---|
| BIN XU 等: "Composite Learning Control of MIMO", 《 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》 * |
| CLIFFORD PONTBRIAND 等: "Wireless Data Harvesting Using the AUV Sentry and WHOI Optical Modem", 《OCEANS 2015 - MTS/IEEE WASHINGTON》 * |
| S. SHARIAT-PANAHI 等: "Small and robust ocean bottom seismometer (OBS) for long period active seismology", 《OCEANS 2011 IEEE - SPAIN》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109975788A (en) * | 2019-04-23 | 2019-07-05 | 中国科学技术大学 | A kind of self-adaptation control method of laser radar scanning mechanism |
| CN110532665A (en) * | 2019-08-26 | 2019-12-03 | 哈尔滨工程大学 | A kind of mobile object dynamic trajectory prediction technique under scheduled airline task |
| CN111650943A (en) * | 2020-06-19 | 2020-09-11 | 哈尔滨理工大学 | Track tracking preset performance control method for speed-limited still water dynamic positioning ship |
| CN111650943B (en) * | 2020-06-19 | 2022-06-24 | 哈尔滨理工大学 | Track tracking preset performance control method for speed-limited still water dynamic positioning ship |
| CN112363538A (en) * | 2020-11-09 | 2021-02-12 | 哈尔滨工程大学 | AUV (autonomous underwater vehicle) area tracking control method under incomplete speed information |
| CN112882482A (en) * | 2021-01-27 | 2021-06-01 | 曲阜师范大学 | Fixed time trajectory tracking control method based on autonomous underwater robot with preset performance constraint |
| CN112882482B (en) * | 2021-01-27 | 2022-07-22 | 曲阜师范大学 | Fixed time trajectory tracking control method based on autonomous underwater robot with predetermined performance constraint |
| CN112947077A (en) * | 2021-01-29 | 2021-06-11 | 哈尔滨工程大学 | AUV robust trajectory tracking control method based on switching performance function technology |
| CN115952731A (en) * | 2022-12-20 | 2023-04-11 | 哈尔滨工业大学 | Active vibration control method, device and equipment for wind turbine blade |
| CN115952731B (en) * | 2022-12-20 | 2024-01-16 | 哈尔滨工业大学 | Active vibration control method, device and equipment for wind turbine blade |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109343347B (en) | 2021-10-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109343347A (en) | A kind of Trajectory Tracking Control method of seabed flight node | |
| CN109283941A (en) | Default capabilities seabed flight node-locus tracking and controlling method based on disturbance observer | |
| CN111650948B (en) | Quick tracking control method for horizontal plane track of benthonic AUV | |
| Kadiyam et al. | Actuator fault-tolerant control study of an underwater robot with four rotatable thrusters | |
| Neto et al. | Attitude control for an hybrid unmanned aerial underwater vehicle: A robust switched strategy with global stability | |
| CN108803632A (en) | Unmanned surface vehicle total state constrained trajectory tracking and controlling method based on saturation compensation technology | |
| CN109634307A (en) | A kind of compound Track In Track control method of UAV navigation | |
| Vervoort | Modeling and control of an unmanned underwater vehicle | |
| Stephan et al. | Linear parameter-varying control for quadrotors in case of complete actuator loss | |
| CN114115262B (en) | Multi-AUV actuator saturation cooperative formation control system and method based on azimuth information | |
| Rokseth et al. | Modeling of generic offshore vessel in crane operations with focus on strong rigid body connections | |
| De Souza et al. | Intelligent UUVs: Some issues on ROV dynamic positioning | |
| CN114442640B (en) | Track tracking control method for unmanned surface vehicle | |
| CN108958294B (en) | Unmanned underwater vehicle formation control method and device | |
| Liang et al. | Design a software real-time operation platform for wave piercing catamarans motion control using linear quadratic regulator based genetic algorithm | |
| Del-Rio-Rivera et al. | Robust trajectory tracking control for fully actuated marine surface vehicle | |
| CN107894775A (en) | A kind of drive lacking UAV navigation Track Pick-up and control method | |
| Sun et al. | Adaptive nonlinear relative motion control of quadrotors in autonomous shipboard landings | |
| Liu et al. | Synchronisation control for ships in underway replenishment based on dynamic surface control | |
| Rober et al. | Three-dimensional path-following control for an underwater vehicle | |
| CN112904719B (en) | Annular area tracking control method suitable for underwater robot position | |
| Riedel | Seaway learning and motion compensation in shallow waters for small AUVs | |
| CN115047891A (en) | AUV propeller fault-tolerant control method based on finite time extended state observer | |
| Wang | Autonomous control of a differential thrust micro rov | |
| Mostafa et al. | Analysis and simulation of 3D trajectory with obstacle avoidance of an autonomous underwater vehicle for optimum performance |
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 |