CN113419426A - AUV communication delay active compensation method based on data driving state predictor - Google Patents
AUV communication delay active compensation method based on data driving state predictor Download PDFInfo
- Publication number
- CN113419426A CN113419426A CN202110790129.7A CN202110790129A CN113419426A CN 113419426 A CN113419426 A CN 113419426A CN 202110790129 A CN202110790129 A CN 202110790129A CN 113419426 A CN113419426 A CN 113419426A
- Authority
- CN
- China
- Prior art keywords
- auv
- communication delay
- active compensation
- jth
- design
- 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
- 238000004891 communication Methods 0.000 title claims abstract description 42
- 238000000034 method Methods 0.000 title claims abstract description 29
- 230000015572 biosynthetic process Effects 0.000 claims abstract description 24
- 238000013461 design Methods 0.000 claims abstract description 21
- 239000013598 vector Substances 0.000 claims abstract description 11
- 150000001875 compounds Chemical class 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 230000003111 delayed effect Effects 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 abstract description 7
- 238000005516 engineering process Methods 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
Images
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)
Abstract
The invention discloses a multi-AUV communication delay active compensation method based on a data-driven predictor, which comprises the following steps of: establishing an autoregressive model; determining the undetermined order number of the autoregressive model; designing an online updating law of the parameter vectors; designing a data driving state predictor of a jth AUV; and active compensation of communication delay is realized. The method can realize the active compensation of the AUV communication delay by designing the state predictor to predict the current actual motion state of the neighboring AUV in real time on line and applying the predicted value to the design of the formation control law. The active compensation method for communication delay among multiple AUVs, provided by the invention, has no limitation on the design method of AUV formation control law, and can be flexibly applied to various advanced formation control theory methods. The active compensation method for communication delay among multiple AUVs, provided by the invention, is data-driven and independent of an AUV motion mathematical model, so that the prior knowledge of parameters and structure of the AUV motion mathematical model is not required.
Description
Technical Field
The invention relates to the field of Underwater robots, in particular to a multi-Autonomous Underwater robot (AUV) communication delay active compensation method based on a data-driven state predictor.
Background
AUV plays an important role in civil and military fields such as ocean resource development and utilization, submarine topography mapping, offshore defense and military reconnaissance. For some complex marine operation tasks, such as large-scale submarine landform survey, underwater multi-target striking and the like, a single AUV is difficult to meet the operation requirements due to the limits in the aspects of carrying load, range, speed and the like, multiple AUVs are applied to carry different devices, and a formation motion control mechanism is established, so that the multiple AUVs are cooperatively used, and the tasks can be completed with higher efficiency and high quality. AUV information interaction is the premise of AUV formation, underwater AUV generally depends on underwater acoustic communication for information transmission, communication delay exists between AUVs due to low propagation speed of the underwater acoustic communication, and if the problem of communication delay is not processed in AUV formation control, the AUV formation control precision is reduced, and even AUV formation cannot be realized. Therefore, the method for researching the communication delay in the AUV formation control has important practical significance, and can provide guarantee for the multi-AUV cooperative marine operation.
The distributed underwater vehicle fleet control with communication constraint is published in the university of science and technology book of Huazhong, Yanbo et al of Huazhong science and technology in 2009, 2, an AUV nonlinear motion mathematical model is linearized by applying a feedback linearization method, an AUV fleet control law under communication delay is designed by combining a consistency algorithm, and a range of AUV fleet control law design parameters for stabilizing an AUV fleet closed-loop control system under communication delay is given based on a Lyapunov-Krosovski function and a linear matrix inequality, so that AUV fleet control under communication delay is realized. A paper entitled "formation control of under-actuated autonomous underwater vehicle under communication delay" is published by Yan Wei et al, northwest industrial university in firepower command control 2011 at 6 th, the problem of multi-AUV formation is converted into the problem of path tracking of each AUV, a multi-AUV synchronous control law based on path parameters considering communication delay is designed by applying a consistency algorithm, and a design parameter range enabling the multi-AUV synchronous control law is given based on a distributed consistency algorithm stability theory, so that formation of multiple AUVs under communication delay is realized. However, in the above document, the designed AUV formation control law only provides sufficient conditions for implementing AUV formation under communication delay through theoretical analysis, and all uses the motion state information of AUV delay, without dealing with the problem of communication delay, and only adapts to the situation of communication delay; in addition, due to the application of a consistency algorithm, the structure of the AUV formation control law is fixed, and the AUV formation control law cannot be further combined with other advanced control theory methods to improve the control effect.
The Chinese invention patent CN108594845A discloses a multi-AUV formation method based on prediction control under communication limitation, which firstly designs an AUV cooperative path tracking control law based on a backward-pushing method, and then introduces a cooperative tracking error prediction module to process the influence of AUV communication delay. However, this patent requires that the AUV motion mathematical model parameters be accurately known.
Disclosure of Invention
In order to solve the problems in the prior art, the invention designs a multi-AUV communication delay active compensation method based on a data-driven state predictor, which fundamentally solves the problem of communication delay among multiple AUVs under the condition that the parameters of an AUV motion mathematical model are unknown, thereby realizing AUV formation control under the condition that communication delay exists.
In order to achieve the purpose, the technical scheme of the invention is as follows: a multi-AUV communication delay active compensation method based on a data drive predictor is disclosed, wherein the multi-AUV comprises N AUVs, and the current actual motion state of the jth AUV is defined as Xj(t); said communication delay is denoted TijAt the current moment, the delayed motion state of a certain neighbor AUV (AUV) received by the ith AUV, namely the jth AUV, is Xj(t-Tij) 1,2, …, N, j 1,2, …, N; the active compensation method comprises the following steps:
A. establishing Xj(t-Tij) Is a model of autoregressive
Establishing Xj(t-Tij) The autoregressive model of (c) is as follows:
in the formula, λijThe undetermined order of the autoregressive model;
λ representing jth AUVijA historical motion state vector for which there is a delay,is λijDimension unknown parameter vector, εij(t) represents a modeling error.
B. Determining undetermined order number lambda of autoregressive modelijValue of (A)
Determining λ according to Akaike information content criterionijThe value of (c):
first, is Xj(t-Tij) The autoregressive model (1) of (A) defines Akaike information content criterion function AIC (lambda)ij) The following were used:
wherein ln (·) represents a natural logarithm,the variance of the modeling error is represented as,is composed ofM 1,2, λijN is a positive integer; assuming that the maximum order of the autoregressive model (1) isCalculate in turnDetermining the order of the autoregressive model (1) as
in the formula (I), the compound is shown in the specification,is composed ofEstimated value of (P)ijIs λij×λijThe aided design matrix of the dimension(s),andare respectively asAnd PijDerivative of (a), k1,ijAnd k2,ijIs a positive design constant.
D. Design the data driving state predictor of the jth AUV
Designing a data driving state predictor of the jth AUV as follows:
in the formula (I), the compound is shown in the specification,andrespectively representAndan estimate of (2). When l isij=λijThen, the ith AUV obtains the current actual motion state X of the jth AUV adjacent to the ith AUVj(t) estimated value
E. Active compensation for communication delay
In the formation control law design of the ith AUV, the current actual motion state X of the jth AUV adjacent to the ith AUV obtained on the basis of a state predictor (7) is appliedj(t) estimated valueAnd active compensation of communication delay is realized.
Compared with the prior art, the invention has the following beneficial effects:
1. the method can realize the active compensation of the AUV communication delay by designing the state predictor to predict the current actual motion state of the neighboring AUV in real time on line and applying the predicted value to the design of the formation control law.
2. The active compensation method for communication delay among multiple AUVs, provided by the invention, has no limitation on the design method of AUV formation control law, and can be flexibly applied to various advanced formation control theory methods.
3. The active compensation method for communication delay among multiple AUVs, provided by the invention, is data-driven and independent of an AUV motion mathematical model, so that the prior knowledge of parameters and structure of the AUV motion mathematical model is not required.
Drawings
Fig. 1 is a schematic diagram of an active compensation method for multi-AUV communication delay based on a data-driven state predictor.
Detailed Description
The invention is further described below with reference to the accompanying drawings. As shown in fig. 1, a principle of an active compensation method for multiple AUV communication delays based on a data-driven state predictor is as follows: motion state X with time delay by using jth AUVj(t-Tij) And λijHistorical motion state vector X with time delayj,past(t-Tij(t)), establishing Xj(t-Tij) And designing the parameter vector of the autoregressive modelAn online update law of; based on the above, an AUV data driving state predictor is designed to give the current actual motion state X of the jth AUVj(t) estimated valueWill be provided withThe method is applied to the design of the formation control law of the ith AUV, so that the active compensation of communication delay existing among multiple AUVs is realized.
The present invention is not limited to the embodiment, and any equivalent idea or change within the technical scope of the present invention is to be regarded as the protection scope of the present invention.
Claims (1)
1. Multi-AUV communication delay active compensation method based on data drive predictor, and computer thereofThe multiple AUVs include N AUVs, and the current actual motion state of the jth AUV is defined as Xj(t); said communication delay is denoted TijAt the current moment, the delayed motion state of a certain neighbor AUV (AUV) received by the ith AUV, namely the jth AUV, is Xj(t-Tij),i=1,2,…,N,j=1,2,…,N;
The method is characterized in that: the active compensation method comprises the following steps:
A. establishing Xj(t-Tij) Is a model of autoregressive
Establishing Xj(t-Tij) The autoregressive model of (c) is as follows:
in the formula, λijThe undetermined order of the autoregressive model;
λ representing jth AUVijA historical motion state vector for which there is a delay,is λijDimension unknown parameter vector, εij(t) represents a modeling error;
B. determining undetermined order number lambda of autoregressive modelijValue of (A)
Determining λ according to Akaike information content criterionijThe value of (c):
first, is Xj(t-Tij) The autoregressive model (1) of (A) defines Akaike information content criterion function AIC (lambda)ij) The following were used:
wherein ln (·) represents a natural logarithm,the variance of the modeling error is represented as,is composed ofM 1,2, λijN is a positive integer; assuming that the maximum order of the autoregressive model (1) is Calculate in turnDetermining the order of the autoregressive model (1) as
in the formula (I), the compound is shown in the specification,is composed ofEstimated value of (P)ijIs λij×λijThe aided design matrix of the dimension(s),andare respectively asAnd PijDerivative of (a), k1,ijAnd k2,ijA design constant that is positive;
D. design the data driving state predictor of the jth AUV
Designing a data driving state predictor of the jth AUV as follows:
in the formula (I), the compound is shown in the specification,andrespectively representAnda predicted value of (2); when l isij=λijThen, the ith AUV obtains the current actual motion state X of the jth AUV adjacent to the ith AUVj(t) estimated value
E. Active compensation for communication delay
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110790129.7A CN113419426B (en) | 2021-07-13 | 2021-07-13 | AUV communication delay active compensation method based on data driving state predictor |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110790129.7A CN113419426B (en) | 2021-07-13 | 2021-07-13 | AUV communication delay active compensation method based on data driving state predictor |
Publications (2)
Publication Number | Publication Date |
---|---|
CN113419426A true CN113419426A (en) | 2021-09-21 |
CN113419426B CN113419426B (en) | 2022-08-05 |
Family
ID=77720882
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110790129.7A Expired - Fee Related CN113419426B (en) | 2021-07-13 | 2021-07-13 | AUV communication delay active compensation method based on data driving state predictor |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113419426B (en) |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108594845A (en) * | 2018-03-23 | 2018-09-28 | 哈尔滨工程大学 | More AUV formation methods based on PREDICTIVE CONTROL under a kind of communication limitation |
CN109521798A (en) * | 2019-01-24 | 2019-03-26 | 大连海事大学 | AUV motion control method based on finite time extended state observer |
CN110422284A (en) * | 2019-07-24 | 2019-11-08 | 华中科技大学 | A kind of active compensation method and system based on Ship Motion forecast |
CN112527017A (en) * | 2020-12-11 | 2021-03-19 | 中国科学院沈阳自动化研究所 | Ocean observation method based on multiple AUVs |
-
2021
- 2021-07-13 CN CN202110790129.7A patent/CN113419426B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108594845A (en) * | 2018-03-23 | 2018-09-28 | 哈尔滨工程大学 | More AUV formation methods based on PREDICTIVE CONTROL under a kind of communication limitation |
CN109521798A (en) * | 2019-01-24 | 2019-03-26 | 大连海事大学 | AUV motion control method based on finite time extended state observer |
CN110422284A (en) * | 2019-07-24 | 2019-11-08 | 华中科技大学 | A kind of active compensation method and system based on Ship Motion forecast |
CN112527017A (en) * | 2020-12-11 | 2021-03-19 | 中国科学院沈阳自动化研究所 | Ocean observation method based on multiple AUVs |
Non-Patent Citations (3)
Title |
---|
JIAN LI 等: "Robust adaptive formation control of underactuated autonomous underwater vehicles under input saturation", 《2018 CHINESE CONTROL AND DECISION CONFERENCE (CCDC)》 * |
JUNNAN LIU 等: "Composite learning tracking control for underactuated autonomous underwater vehicle with unknown dynamics and disturbances in three-dimension space", 《APPLIED OCEAN RESEARCH》 * |
包佳程: "侧壁式气垫船升沉运动控制研究", 《中国优秀博硕士学位论文全文数据库(硕士) 工程科技Ⅱ辑》 * |
Also Published As
Publication number | Publication date |
---|---|
CN113419426B (en) | 2022-08-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wu et al. | Depth control of model-free AUVs via reinforcement learning | |
Sun et al. | Leader-follower formation control of underactuated surface vehicles based on sliding mode control and parameter estimation | |
Gan et al. | QPSO-model predictive control-based approach to dynamic trajectory tracking control for unmanned underwater vehicles | |
Huang et al. | Robust practical fixed-time leader–follower formation control for underactuated autonomous surface vessels using event-triggered mechanism | |
Dong et al. | Autonomous cooperative formation control of underactuated USVs based on improved MPC in complex ocean environment | |
Zhang et al. | Anti-disturbance control for dynamic positioning system of ships with disturbances | |
CN109669345A (en) | Underwater robot fuzzy motion control method based on ESO | |
Yin et al. | Predictive trajectory tracking control of autonomous underwater vehicles based on variable fuzzy predictor | |
Su et al. | Fixed-time formation of AUVs with disturbance via event-triggered control | |
Wang et al. | Globally stable adaptive dynamic surface control for cooperative path following of multiple underactuated autonomous underwater vehicles | |
Gong et al. | Trajectory tracking control for autonomous underwater vehicles based on dual closed-loop of MPC with uncertain dynamics | |
Lv et al. | Robust state‐error port‐controlled Hamiltonian trajectory tracking control for unmanned surface vehicle with disturbance uncertainties | |
Zhang et al. | Nussbaum-type function based robust neural event-triggered control of unmanned surface vehicle subject to cyber and physical attacks | |
Gao et al. | Command filtered path tracking control of saturated ASVs based on time‐varying disturbance observer | |
Ajaweed et al. | Submarine control system using sliding mode controller with optimization algorithm | |
Pan | Exploration and mining learning robot of autonomous marine resources based on adaptive neural network controller | |
Rani et al. | A neural network based efficient leader–follower formation control approach for multiple autonomous underwater vehicles | |
Wang et al. | Quantized communications in containment maneuvering for output constrained marine surface vehicles: Theory and experiment | |
Fu et al. | Event‐triggered‐based finite‐time cooperative path following control for underactuated multiple marine surface vehicles | |
Mazare | Distributed adaptive fault tolerant formation control for multiple underwater vehicles: Free-will arbitrary time approach | |
Song et al. | Distributed dynamic edge-based event-triggered formation control for multiple underactuated unmanned surface vessels | |
CN113419426B (en) | AUV communication delay active compensation method based on data driving state predictor | |
Yang et al. | Trajectory tracking with external disturbance of bionic underwater robot based on CPG and robust model predictive control | |
Ihle et al. | Robust formation control of marine craft using lagrange multipliers | |
Liu et al. | Robust Adaptive Self‐Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels |
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 | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20220805 |