CN109189103B - Under-actuated AUV trajectory tracking control method with transient performance constraint - Google Patents

Under-actuated AUV trajectory tracking control method with transient performance constraint Download PDF

Info

Publication number
CN109189103B
CN109189103B CN201811333785.9A CN201811333785A CN109189103B CN 109189103 B CN109189103 B CN 109189103B CN 201811333785 A CN201811333785 A CN 201811333785A CN 109189103 B CN109189103 B CN 109189103B
Authority
CN
China
Prior art keywords
auv
under
actuated
function
neural network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811333785.9A
Other languages
Chinese (zh)
Other versions
CN109189103A (en
Inventor
崔荣鑫
严卫生
陈乐鹏
张福斌
高剑
黄凯
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northwestern Polytechnical University
Original Assignee
Northwestern Polytechnical University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northwestern Polytechnical University filed Critical Northwestern Polytechnical University
Priority to CN201811333785.9A priority Critical patent/CN109189103B/en
Publication of CN109189103A publication Critical patent/CN109189103A/en
Application granted granted Critical
Publication of CN109189103B publication Critical patent/CN109189103B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/12—Target-seeking control

Landscapes

  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention provides an under-actuated AUV trajectory tracking control method with transient performance constraint, which establishes an AUV horizontal plane model, constructs an error conversion function with transient constraint performance and provides adaptive inversion control aiming at the under-actuated characteristic of an underwater vehicle. Meanwhile, the uncertain system parameters and the external time-varying interference are estimated in real time by using the neural network, an observer based on the neural network is designed for estimating unmeasurable speed, and the effectiveness of the algorithm is verified through simulation.

Description

Under-actuated AUV trajectory tracking control method with transient performance constraint
Technical Field
The invention relates to the technical field of underwater vehicle horizontal plane track tracking control, in particular to an under-actuated adaptive inversion control method with transient performance constraint and saturation resistance.
Background
The Autonomous Underwater Vehicle (AUV) can be widely used for exploration of submarine biological resources, sampling of mineral resources, submarine topography exploration, salvage of sediments, earthquake geothermal activity monitoring, marine environment monitoring, marine engineering maintenance and the like.
The AUV has strong nonlinear characteristics, and the navigation environment is very complex, so that the traditional PID control algorithm is hard to be sufficient. The fluid parameters of the control system can change in the process of sailing, so that the model parameters have uncertainty and can be randomly disturbed by sea waves and ocean currents, and the control system has certain robustness and self-adaptive capacity.
Under-actuated AUV is a common type of AUV that features a number of control inputs that is less than the number of degrees of freedom. Inversion control is an important tool for designing a nonlinear system, and can decompose a complex nonlinear system into a plurality of subsystems, then design intermediate virtual control quantities for the subsystems respectively, and continue reverse deduction until the design of the whole control system is completed. In recent years, inversion control methods have been successfully applied in the fields of motor control, robots, space vehicles, and the like. In particular, inversion control is an important method for solving the under-actuated system control, and therefore, attention is paid.
In addition, the problem of trajectory tracking control of an under-actuated AUV system is widely concerned, but many existing works only meet the condition that a tracking error converges to a residual set, and the tracking accuracy and the upper and lower state bounds of the system error are not required, namely the transient characteristic is not required. For example, for the tracking error of the system, constraints such as convergence speed, maximum overshoot, and steady-state error range can be proposed, which are effective bases for considering the tracking performance of the system. However, in a real marine environment, various dangerous obstacles may exist, such as reefs and the like, and in order to avoid the dangerous obstacles, it is necessary to consider the problem of under-actuated AUV trajectory tracking control under transient performance constraints.
In summary, the current under-actuated AUV trajectory tracking control still has the following three problems: 1. the under-AUV model parameters have uncertainty; 2. external random interferences such as ocean currents and the like can cause certain influence on the trajectory tracking control of the AUV; 3. in order to avoid various dangerous obstacles in the real marine environment, the problem of under-actuated AUV trajectory tracking control under transient performance constraint needs to be considered.
Disclosure of Invention
The invention provides an under-actuated AUV trajectory tracking control method with transient performance constraint aiming at the problems of uncertain mathematical model and unknown external interference in under-actuated AUV horizontal trajectory tracking control.
The technical scheme of the invention is as follows:
the under-actuated AUV trajectory tracking control method with transient performance constraint is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a horizontal plane motion model of an under-actuated underwater vehicle:
Figure BDA0001860691070000021
wherein x and y are horizontal plane position coordinates of the AUV under the world coordinate system, and psi is a course angle; u, v and r are the forward, lateral and course speeds of the AUV under a body coordinate system; parameter miiFor the ith diagonal entry in the AUV quality matrix, parameter diiAs a known linear term of the hydrodynamic parameter of the AUV, DiAn unknown nonlinear term representing the AUV hydrodynamic parameter and external unknown interference; χ is a rudder effect function related to the state quantities u, v; longitudinal thrust T and straight rudder angle with saturation characteristicrFunction is as
Figure BDA0001860691070000022
Wherein, TmaxAndr,maxthe corresponding amplitudes of the longitudinal thrust and the straight rudder angle;
step 2: and (2) controlling the underwater vehicle horizontal plane motion model in the step (1) by adopting the following control law and self-adaptive law:
the control law is as follows:
Figure BDA0001860691070000031
Figure BDA0001860691070000032
wherein, muu，μrAnd l3Is a set normal number;
Figure BDA0001860691070000033
Figure BDA0001860691070000034
is a weight estimate of the neural network, ΘiIs a neural network basis function, Z ═ x, y, ψ, xd,yd,ψd,u,v,r]T；s1,s2And s3Is the difference between the virtual speed and the actual speed, which is defined as
Figure BDA0001860691070000035
α1,α2And alpha3Is a constant greater than zero, uv,vvAnd rvIs a virtual speed, which is defined as
Figure BDA0001860691070000036
l1And l2Is a constant greater than zero, xd,ydAnd psidIs the expected value of the position coordinate and the course angle; e.g. of the typex,eyAnd eψA logarithmic form of the tracking error transfer function defined as
Figure BDA0001860691070000037
In, xe,yeAnd psieFor tracking error, defined as xe＝x-xd,ye＝y-yd,ψe＝ψ-ψd；
The logarithmic transfer function is defined as
Figure BDA0001860691070000038
ρiIs a predefined world function;
the adaptive law is as follows:
Figure BDA0001860691070000041
wherein the content of the first and second substances,iand kappaiAll constants greater than zero, σ (T) ═ T- γ (T), σ: (c)r)＝r-γ(r)
Figure BDA0001860691070000042
Advantageous effects
The method establishes an AUV horizontal plane model, constructs an error conversion function with transient constraint performance, and provides adaptive inversion control aiming at the under-actuated characteristic of the underwater vehicle. Meanwhile, the neural network is used for estimating uncertain system parameters and external time-varying interference in real time, an observer based on the neural network is designed for estimating unmeasurable speed, the effectiveness of the algorithm is verified in a simulation mode, and the control system can be guaranteed to have good performance.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic block diagram of an under-actuated AUV trajectory tracking control with transient performance constraints.
FIG. 2 is a trace-tracking graph employing PID, adaptive inversion control with transient constraints, respectively.
FIGS. 3 and 4 show the tracking error x under two control lawse,yeGraph is shown.
Figure 5 is a two-norm neural network.
Detailed Description
The following detailed description of embodiments of the invention is intended to be illustrative, and not to be construed as limiting the invention.
The embodiment provides an under-actuated AUV trajectory tracking control method with transient performance constraint, which is an under-actuated AUV adaptive inversion control method with transient performance constraint and mainly comprises the following steps:
and establishing an AUV horizontal plane mathematical model.
And designing a proper boundary function, and constructing an error conversion variable in a logarithmic form.
The virtual speed is constructed on the basis of ensuring the stability of a kinematic layer, and meanwhile, in order to solve the problems of input saturation and underactuation, beta is designed1,β2And beta3And (4) self-adaptation law.
And respectively carrying out derivation on the converted error variable and the error between the virtual speed and the real speed to obtain derivatives of the error variable and the error.
And (3) adopting the RBF neural network to estimate model uncertainty and external unknown interference, and designing a self-adaptive inversion control method.
And constructing a Lyapunov function, proving the stability of the Lyapunov function, and performing simulation verification on the Lyapunov function.
The method combines a transient performance constraint technology, an input saturation resisting technology and an adaptive inversion control technology to realize the horizontal trajectory tracking of the under-actuated AUV, and has good adaptive capacity and certain engineering value.
The present invention will be described in further detail below.
Firstly, establishing a horizontal plane motion model of an under-actuated underwater vehicle:
Figure BDA0001860691070000051
wherein x and y are horizontal plane position coordinates of the AUV under the world coordinate system, and psi is a course angle; u, v and r are the forward, lateral and course speeds of the AUV under a body coordinate system; parameter miiFor the ith diagonal entry in the AUV quality matrix, parameter diiAs a known linear term of the hydrodynamic parameter of the AUV, DiAn unknown nonlinear term representing the AUV hydrodynamic parameter and external unknown interference; χ is a rudder effect function related to the state quantities u, v; longitudinal thrust T and straight rudder angle with saturation characteristicrFunction is as
Figure BDA0001860691070000061
Wherein, TmaxAndr,maxthe corresponding amplitudes of the longitudinal thrust and the straight rudder angle.
Secondly, designing a boundary function, and constructing error conversion variables in a logarithmic form:
definition of xe＝x-xd,ye＝y-yd,ψe＝ψ-ψdFor tracking errors, wherein xd,ydAnd psidThe expected values for the position coordinates and the heading angle,
Figure BDA0001860691070000062
and assume a reference velocity ud，vd，rdAnd its first derivative is bounded.
To ensure the transient performance of the tracking error, let the tracking error xe,yeAnd psieSatisfy the following constraints
Figure BDA0001860691070000063
Where ρ isiIs a predefined world function and can be defined as
Figure BDA0001860691070000067
ρi,0，ρi,∞And alphaiIs a constant greater than zero, pi,0And ρi,∞Respectively, the maximum and steady-state values of the bound function, while alphaiThe convergence speed of the boundary function is determined.
ex,eyAnd eψA logarithmic form of the tracking error transfer function defined as
Figure BDA0001860691070000064
Wherein the logarithmic transfer function is defined as
Figure BDA0001860691070000065
Then, a virtual speed is constructed on the basis of ensuring the stability of the kinematic layer, and meanwhile, in order to solve the problems of input saturation and underactuation, beta is designed1,β2And beta3And (4) self-adaptation law.
s1,s2And s3Is the difference between the virtual speed and the actual speed, which is defined as
Figure BDA0001860691070000066
Wherein alpha is1,α2And alpha3Is a constant greater than zero, uv,vvAnd rvIs a virtual speed, which is defined as
Figure BDA0001860691070000071
Wherein l1And l2Is a constant greater than zero.
And beta is1,β2And beta3The adaptive law is:
Figure BDA0001860691070000072
wherein the content of the first and second substances,iand kappaiAll constants greater than zero, σ (T) ═ T- γ (T), σ: (c)r)＝r-γ(r)。
It is noted that the virtual speed uv,vvAnd rvThe function of (a) is to ensure the stability of the kinematic level, and at the same time, beta1And beta3For compensating the effect of input saturation, beta2To solve the under-actuated control problem.
And finally, respectively carrying out derivation on the converted error variable and the error between the virtual speed and the real speed to obtain derivatives of the error variable and the error.
Combining equations (4) and (5) for tracking error xe,yeAnd psieDerivative to obtain
Figure BDA0001860691070000073
By deriving from formula (3), the result is obtained
Figure BDA0001860691070000074
Wherein
Figure BDA0001860691070000077
Figure BDA0001860691070000076
Figure BDA0001860691070000081
It is worth noting that:
i)|tanh(·)|≤1，|sin(·)|≤1，|cos(·)|≤1；
ii) known from assumptions
Figure BDA0001860691070000082
Is bounded;
iii) according to the definition of the error transformation function, | zi|≤1，
Figure BDA0001860691070000083
Thus, there is a constant greater than zero
Figure BDA0001860691070000084
So that the inequality
Figure BDA0001860691070000085
This is true.
Combining the pairs s of (1) and (6)1,s2And s3Derivative to obtain
Figure BDA0001860691070000086
Wherein the content of the first and second substances,
Figure BDA0001860691070000087
is an ideal value of the RBF weight.
And finally, designing a self-adaptive inversion control method.
To ensure s1，s2And s3The following control law is designed for the stability of (1):
Figure BDA0001860691070000088
wherein, κi＞0,i＝1,2,3。
The Lyapunov function is constructed below, the stability is proved, and simulation verification is carried out on the Lyapunov function.
Defining a candidate Lyapunov function as
Figure BDA0001860691070000089
Substituting the control law (10) into (9) can obtain
Figure BDA0001860691070000091
Wherein the content of the first and second substances,
Figure BDA0001860691070000092
the derivatives of V can be obtained by combining the formulae (8), (10) and (12)
Figure BDA0001860691070000093
According to the characteristics of RBF neural network and Young's inequality
Figure BDA0001860691070000094
Wherein ξi，
Figure BDA0001860691070000095
Is a normal number, and
Figure BDA0001860691070000096
according to the Young's inequality, (13) can be rewritten as
Figure BDA0001860691070000097
Wherein the content of the first and second substances,
Figure BDA0001860691070000101
Figure BDA0001860691070000102
λmax(. -) represents the maximum eigenvalue of. To ensure that μ is a positive number, the gain in the control law, l1，l2And l3The following conditions should be satisfied:
Figure BDA0001860691070000103
(15) the left and right sides multiply e simultaneouslyμtIs obtained by
Figure BDA0001860691070000104
According to the above inequality, the conversion error e can be guaranteedx,ey,
Figure BDA0001860691070000105
s1,s2And s3And weight estimation error of neural network
Figure BDA0001860691070000106
And
Figure BDA0001860691070000107
is bounded. According to the boundedness of the conversion error, the tracking error can be guaranteed not to violate the constraint, namely: | xe(t)|＜ρ1(t)，|ye(t)|＜ρ2(t)，|ψe(t)|＜ρ3(t) of (d). The certification is over.
Simulation experiment and verification:
the following illustrates the validity of verifying the design of an under-actuated AUV trajectory tracking controller.
And carrying out simulation according to model parameters of certain model AUV.
The parameters of the controller are selected as follows: l1＝1，l2＝2，l3＝1，α1＝30，α2＝50，α3＝20，μu＝5，μr＝5，β1(0)＝0，β2(0)＝0，β3(0)＝0，κ1＝3，κ2＝1，κ3＝6。
The present embodiment employs RBF neural networks to estimate unknown external interference and model uncertainty. Each thetaiThere are 9 neurons and the initial weight
Figure BDA0001860691070000109
Is set to zero. Meanwhile, a weight matrix in the Lyapunov function is defined asi＝15I9×9And the variance is σi＝8。
To simulate a real marine environment, the time-varying ambient disturbances in the world coordinate system are defined as follows:
Figure BDA0001860691070000108
in the body coordinate system, D ═ M-1JT(ψ)w(t)，D＝[D1 D2 D3]T，
Figure BDA0001860691070000111
The desired trajectory may be defined as:
1)0≤t＜100:ud＝0.5,vd＝0,rd＝0；
2)100≤t＜300:ud＝0.5,vd＝0,rd＝-0.005sin(π(t-100)/400)；
3)300≤t≤700:ud＝0.5,vd＝0,rd＝-0.01/2。
the reference trajectory and the initial state of the AUV are: etad(0)＝[0m,0m,π/4rad]T，η(0)＝[4m,-6m,0rad]T。
The predetermined world function is defined as:
ρ1(t)＝(20-2)e-0.05t+2，
ρ2(t)＝(20-2)e-0.05t+2，
ρ3(t)＝(3-π/9)e-0.05t+π/9。
fig. 2 is a track tracking diagram respectively adopting PID and adaptive inversion control, and it can be seen that the unmanned ship can better track an expected track. FIGS. 3 and 4 show the tracking error x under two control lawse,yeCurve lineThe figure shows that the tracking error does not exceed the constraint at all times. FIG. 5 is a two-norm of a neural network, and it can be seen that the norm of the weights of the neural network is bounded.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.

Claims (1)

1. An under-actuated AUV trajectory tracking control method with transient performance constraint is characterized in that: the method comprises the following steps:
step 1: establishing a horizontal plane motion model of an under-actuated underwater vehicle:
Figure FDA0002700380280000011
wherein x and y are horizontal plane position coordinates of the AUV under the world coordinate system, and psi is a course angle; u, v and r are the forward, lateral and course speeds of the AUV under a body coordinate system; parameter miiFor the ith diagonal entry in the AUV quality matrix, parameter diiAs a known linear term of the hydrodynamic parameter of the AUV, DiAn unknown nonlinear term representing AUV hydrodynamic parameters and external unknown interference, i is 1,2 and 3; χ is a rudder effect function related to the state quantities u, v; longitudinal thrust T and straight rudder angle with saturation characteristicrFunction is as
Figure FDA0002700380280000012
Wherein, TmaxAndr,maxthe corresponding amplitudes of the longitudinal thrust and the straight rudder angle;
step 2: and (2) controlling the underwater vehicle horizontal plane motion model in the step (1) by adopting the following control law and self-adaptive law:
the control law is as follows:
Figure FDA0002700380280000013
Figure FDA0002700380280000014
wherein, muu，μrAnd l3Is a set normal number;
Figure FDA0002700380280000015
Figure FDA0002700380280000021
is a weight estimate of the neural network, ΘiIs a neural network basis function, Z ═ x, y, ψ, xd,yd,ψd,u,v,r]T；s1,s2And s3Is the difference between the virtual speed and the actual speed, which is defined as
Figure FDA0002700380280000022
α1,α2And alpha3Is a constant greater than zero, uv,vvAnd rvIs a virtual speed, which is defined as
Figure FDA0002700380280000023
l1And l2Is a constant greater than zero, xd,ydAnd psidIs the expected value of the position coordinate and the course angle; e.g. of the typex,eyAnd eψA logarithmic form of the tracking error transfer function defined as
Figure FDA0002700380280000024
In, xe,yeAnd psieFor tracking error, defined as xe＝x-xd,ye＝y-yd,ψe＝ψ-ψd；
The logarithmic transfer function is defined as
Figure FDA0002700380280000025
ρiIs a predefined world function;
the adaptive law is as follows:
Figure FDA0002700380280000026
wherein the content of the first and second substances,iand kappaiAll constants greater than zero, σ (T) ═ T- γ (T), σ: (c)r)＝r-γ(r)
Figure FDA0002700380280000027
CN201811333785.9A 2018-11-09 2018-11-09 Under-actuated AUV trajectory tracking control method with transient performance constraint Active CN109189103B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811333785.9A CN109189103B (en) 2018-11-09 2018-11-09 Under-actuated AUV trajectory tracking control method with transient performance constraint

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811333785.9A CN109189103B (en) 2018-11-09 2018-11-09 Under-actuated AUV trajectory tracking control method with transient performance constraint

Publications (2)

Publication Number Publication Date
CN109189103A CN109189103A (en) 2019-01-11
CN109189103B true CN109189103B (en) 2020-12-08

Family

ID=64938984

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811333785.9A Active CN109189103B (en) 2018-11-09 2018-11-09 Under-actuated AUV trajectory tracking control method with transient performance constraint

Country Status (1)

Country Link
CN (1) CN109189103B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109521798B (en) * 2019-01-24 2021-07-27 大连海事大学 AUV motion control method based on finite time extended state observer
US11526724B2 (en) * 2019-08-21 2022-12-13 GM Global Technology Operations LLC Virtual sensor for estimating online unmeasurable variables via successive time derivatives
CN110647161B (en) * 2019-10-15 2022-07-15 哈尔滨工程大学 Under-actuated UUV horizontal plane trajectory tracking control method based on state prediction compensation
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
CN112130557B (en) * 2020-08-19 2023-10-20 鹏城实验室 Multi-underwater vehicle tracking control method, terminal and storage medium
CN112818819B (en) * 2021-01-28 2022-08-26 青岛澎湃海洋探索技术有限公司 AUV state monitoring method based on dynamic model and complex network theory

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102385316A (en) * 2011-09-16 2012-03-21 哈尔滨工程大学 Deepening controlling method of underactuated automatic underwater vehicle based on neural network back stepping method
CN104133375A (en) * 2014-08-14 2014-11-05 大连海事大学 Multi-AUV synchronous controller structure and design method
CN105843224A (en) * 2016-03-25 2016-08-10 哈尔滨工程大学 AUV horizontal planar path tracking control method based on neural dynamic model and backstepping method
CN106227223A (en) * 2016-09-27 2016-12-14 哈尔滨工程大学 A kind of UUV trace tracking method based on dynamic sliding mode control
CN107544256A (en) * 2017-10-17 2018-01-05 西北工业大学 Underwater robot sliding-mode control based on adaptive Backstepping
CN108427414A (en) * 2018-03-31 2018-08-21 西北工业大学 A kind of horizontal surface self-adaption Trajectory Tracking Control method of Autonomous Underwater Vehicle

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140025613A1 (en) * 2012-07-20 2014-01-23 Filip Ponulak Apparatus and methods for reinforcement learning in large populations of artificial spiking neurons

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102385316A (en) * 2011-09-16 2012-03-21 哈尔滨工程大学 Deepening controlling method of underactuated automatic underwater vehicle based on neural network back stepping method
CN104133375A (en) * 2014-08-14 2014-11-05 大连海事大学 Multi-AUV synchronous controller structure and design method
CN105843224A (en) * 2016-03-25 2016-08-10 哈尔滨工程大学 AUV horizontal planar path tracking control method based on neural dynamic model and backstepping method
CN106227223A (en) * 2016-09-27 2016-12-14 哈尔滨工程大学 A kind of UUV trace tracking method based on dynamic sliding mode control
CN107544256A (en) * 2017-10-17 2018-01-05 西北工业大学 Underwater robot sliding-mode control based on adaptive Backstepping
CN108427414A (en) * 2018-03-31 2018-08-21 西北工业大学 A kind of horizontal surface self-adaption Trajectory Tracking Control method of Autonomous Underwater Vehicle

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
A novel adaptive second order sliding mode path following control for a portable AUV;Guo-cheng Zhang 等;《Ocean Engineering》;20180112;第82-92页 *
Adaptive Neural Network Control of Underactuated Surface Vessels With Guaranteed Transient Performance: Theory and Experimental Results;Lepeng Chen等;《IEEE》;20200530;第67卷(第5期);第4024-4035页 *
基于干扰观测器的欠驱动AUV自适应反演控制;陈巍 等;《中南大学学报（自然科学版）》;20170131;第48卷(第1期);第69-76页 *
基于模型参考的多自主水下航行器自适应覆盖控制;严卫生 等;《***工程与电子技术》;20151130;第37卷(第1期);第2574-2578页 *

Also Published As

Publication number Publication date
CN109189103A (en) 2019-01-11

Similar Documents

Publication Publication Date Title
CN109189103B (en) Under-actuated AUV trajectory tracking control method with transient performance constraint
CN108803321B (en) Autonomous underwater vehicle track tracking control method based on deep reinforcement learning
CN107168312B (en) Space trajectory tracking control method for compensating UUV kinematic and dynamic interference
CN109100939B (en) Input saturation considered water surface unmanned ship all-state constraint trajectory tracking control method
CN111650948B (en) Quick tracking control method for horizontal plane track of benthonic AUV
CN112612209B (en) Full-drive ship track tracking control method and system based on instruction filtering neural network controller
CN112965371B (en) Water surface unmanned ship track rapid tracking control method based on fixed time observer
CN111650832B (en) Method for tracking and controlling mechanical foot posture of underwater multi-foot walking robot
Batmani et al. Event-triggered H∞ depth control of remotely operated underwater vehicles
CN113238567B (en) Benthonic AUV weak buffeting integral sliding mode point stabilizing control method based on extended state observer
CN114442640B (en) Track tracking control method for unmanned surface vehicle
Shojaei Three-dimensional tracking control of autonomous underwater vehicles with limited torque and without velocity sensors
Sun et al. An integrated backstepping and sliding mode tracking control algorithm for unmanned underwater vehicles
CN111273677B (en) Autonomous underwater robot speed and heading control method based on reinforcement learning technology
Wang et al. Path-following optimal control of autonomous underwater vehicle based on deep reinforcement learning
CN113110512B (en) Benthonic AUV self-adaptive trajectory tracking control method for weakening unknown interference and buffeting influence
Ngongi et al. Linear fuzzy controller design for dynamic positioning system of surface ships
CN115951693B (en) Robust track tracking control method for under-actuated underwater robot
Faruq et al. Optimization of depth control for Unmanned Underwater Vehicle using surrogate modeling technique
Liu et al. Synchronisation control for ships in underway replenishment based on dynamic surface control
Wang et al. NN-backstepping for diving control of an underactuated AUV
CN112904719B (en) Annular area tracking control method suitable for underwater robot position
CN110703792B (en) Underwater robot attitude control method based on reinforcement learning
CN109062232B (en) Seabed seismic wave detection flight node distributed finite time anti-shake configuration inclusion control method
CN113093739A (en) Optimized controller for preventing collision of multiple unmanned boats in formation and structure and design method thereof

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