Autonomous Underwater Vehicles: Localization, Tracking, and Formation

ISBN-13: 9789811660955 / Angielski / Twarda / 2021 / 228 str.

Jing Yan; Xian Yang;Haiyan Zhao

Opis

Autonomous underwater vehicles (AUVs) are emerging as a promising solution to help us explore and understand the ocean. The global market for AUVs is predicted to grow from 638 million dollars in 2020 to 1,638 million dollars by 2025 – a compound annual growth rate of 20.8 percent. To make AUVs suitable for a wider range of application-specific missions, it is necessary to deploy multiple AUVs to cooperatively perform the localization, tracking and formation tasks. However, weak underwater acoustic communication and the model uncertainty of AUVs make achieving this challenging. 

This book presents cutting-edge results regarding localization, tracking and formation for AUVs, highlighting the latest research on commonly encountered AUV systems. It also showcases several joint localization and tracking solutions for AUVs. Lastly, it discusses future research directions and provides guidance on the design of future localization, tracking and formation schemes for AUVs. 

Representing a substantial contribution to nonlinear system theory, robotic control theory, and underwater acoustic communication system, this book will appeal to university researchers, scientists, engineers, and graduate students in control theory and control engineering who wish to learn about the core principles, methods, algorithms, and applications of AUVs. Moreover, the practical localization, tracking and formation schemes presented provide guidance on exploring the ocean. The book is intended for those with an understanding of nonlinear system theory, robotic control theory, and underwater acoustic communication systems.

Spis treści

1.      Introduction

1.1   Dynamical Model of Autonomous Underwater Vehicles

1.2   Weak Communication Characteristic of Underwater Acoustic Communication

1.3   Existing Literatures on the Localization, Tracking and Formation

   

2.      Persistent Localization of Autonomous Underwater Vehicles without Velocity Measurements

2.1   Introduction

2.2   System Model and Problem Formulation

2.3   Main Results

2.3.1 Persistent Localization Design

2.3.1.1 Observer-Based Motion Prediction Strategy Design

2.3.1.2 Graph-Based Persistent Localization Strategy Design

2.3.2 Performance Analysis

2.3.2.1 Stability Analysis

2.3.2.2 Convergence Analysis

2.3.2.3 Computational Overhead

2.3.2.4 Communication Overhead

2.4   Numerical Simulation

2.4.1 Simulation of Observer-based Motion Prediction

2.4.2 Simulation of Persistent Localization

2.5   Summary

References

 

3.      Joint Localization and Tracking of Autonomous Underwater Vehicle with State Disturbances

3.1   Introduction

3.2   System Model and Problem Formulation

3.3   Main Results

3.3.1 Joint Localization and Tracking Design

3.3.1.1 Self-Localization Method Design

3.3.1.2 Model-Free Tracking Controller Design

3.3.2 Performance Analysis

3.3.2.1 Convergence Analysis of Localization Method

3.3.2.2 Cramer-Rao Lower Bound of Localization Method

3.3.2.3 Boundness Analysis of Time-Delay Estimation Error

3.3.2.4 Convergence Analysis of Tracking Controller

3.4   Numerical Simulations and Experiments

3.4.1 Simulation of Self-Localization Method

3.4.2 Simulation of Tracking Controller

3.4.3 Experimental Results

3.5   Summary

Reference

 

4.      Joint Localization and Tracking of Autonomous Underwater Vehicle with Model Uncertainty

4.1   Introduction

4.2   System Model and Problem Formulation

4.3   Main Results

4.3.1 Joint Localization and Tracking Design

4.3.1.1 Self-Localization Algorithm Design

4.3.1.2 Reinforcement Learning Based Tracking Controller Design

4.3.2 Convergence Analysis of Tracking Controller

4.4   Numerical Simulation

4.4.1 Simulation of Self-Localization Method

4.4.2 Simulation of Tracking Controller

4.5   Summary

Reference

 

5.      Tracking Control of Autonomous Underwater Vehicle with time Delay and Actuator Saturation

5.1   Introduction

5.2   System Model and Problem Formulation

5.3   Main Results

5.3.1 Tracking Controller Design

5.3.2 Stability Condition and DOA Estimation

5.4   Numerical Simulations and Experiments

5.4.1 Simulation Results

5.4.2 Experimental Results

5.5   Summary

Reference

 

6.      Finite-Time Tracking Control of Autonomous Underwater Vehicle without Velocity Measurements

6.1   Introduction

6.2   System Model and Problem Formulation

6.3   Main Results

6.3.1 Finite-Time Tracking Controller Design

6.3.2 Performance Analysis

    6.3.2.1 Stability Condition of Velocity Observer

    6.3.2.1 Stability Analysis of Tracking Controller

6.4   Numerical Simulations and Experiments

6.4.1 Simulation Results

     6.4.1.1 Simulation results of Velocity Observer

     6.4.1.2 Simulation results of Tracking Controller

6.4.2 Experimental Results

6.5   Summary

Reference

 

7.      Formation Control of Autonomous Underwater Vehicles with Communication Delay

7.1   Introduction

7.2   System Model and Problem Formulation

7.3   Main Results

7.3.1 Tracking Control for Single-AUV System

7.3.2 Formation Control for Multi-AUV System

7.3.3 Performance Analysis

7.4   Numerical Simulations and Experiments

7.4.1 Simulation Results

7.4.2 Experimental Results

7.5   Summary

Reference

 

O autorze

Jing Yan received the B.Eng. degree in Automation from Henan University, Kaifeng, China, in 2008, and the Ph.D. degree in Control Theory and Control Engineering from Yanshan University, Qinhuangdao, China, in 2014. In 2014, he was Research Assistant with the Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiaotong University, Shanghai, China. From January 2016 to September 2016, he was Postdoc with University of North Texas, Denton, USA. From October 2016 to January 2017, he was Research Associate with University of Texas at Arlington, Arlington, USA. Currently, he is Associate Professor with Yanshan University, Qinhuangdao, China. Meanwhile, he is also Associate Editor for IEEE Access. His research interests cover in underwater acoustic sensor networks, networked teleoperation systems, and cyber-physical systems. He has published more than 80 peer-reviewed papers in leading academic journals and conferences. He has also received numerous awards, including the Excellence Paper Award from the National Doctoral Academic Forum of System Control and Information Processing in 2012, the Outstanding Doctorate Dissertation of Hebei Province in 2015, the Excellence Paper Award from the National Doctoral Academic Forum of System Control and Information Processing in 2012, the Youth Talent Support Program of Hebei Province in 2019, the Outstanding Young Foundation of Hebei Province in 2020, and the Excellence Adviser from Oceanology International Underwater Robot Competition in 2017.

Xian Yang received the B.S. degree in Automation and the Ph.D. degree in the Control Theory and Control Engineering from Yanshan University, Qinhuangdao, China, in 2010 and 2016, respectively. She was Academic Visitor of Imperial College London, UK, from June 2019 to June 2020. She is currently Associate Professor with Yanshan University. Her research interests include networked teleoperation systems, underwater autonomous systems, and nonlinear control. She has authored over 30 referred international journal and conference papers. Some of these papers were selected as Highly Cited Papers of ESI. Besides, she has published an English monograph. She was selected into the Youth Talent Support Program of Hebei Province in 2020.

Haiyan Zhao received the B.S. degree in Automation from Yanshan University, in 2017. Currently, she is pursuing the Ph.D. degree in Control Theory and Control Engineering at Yanshan University, Qinhuangdao, China. Her research interests cover in underwater acoustic sensor networks and autonomous underwater vehicle. She won the national scholarship in 2019 and presided over Postgraduate Innovation Fund Project of Hebei in 2019.

Xiaoyuan Luo received the M.Eng. degree and the Ph.D. degree from the Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China, in 2001 and 2004, respectively. He is currently Professor with Yanshan University. His research interests include intelligent transportation control systems, cyber-security for cyber-physical systems, and multi-agent and networked control systems. He has received Excellence Advisor from the Oceanology International Underwater Robot Competition in 2017.

Xinping Guan received the B.S. degree in applied mathematics from Harbin Normal University, Harbin, China, in 1986, and the M.S. degree in applied mathematics, and the Ph.D. degree in electrical engineering from the Harbin Institute of Technology, Harbin, in 1991 and 1999, respectively. He is currently Chair Professor with Shanghai Jiao Tong University, Shanghai, China. He has authored and/or co-authored four research monographs, more than 270 papers in IEEE and other peer-reviewed journals, and numerous conference papers. His current research interests include industrial cyber-physical systems, wireless networking and applications in smart city and smart factory, and underwater sensor networks. He was a recipient of the National Outstanding Youth Honoured by the NSF of China, the Changjiang Scholar by the Ministry of Education of China, and the State-Level Scholar of New Century Bai Qianwan Talent Program of China. He is Executive Committee Member of the Chinese Automation Association Council and the Chinese Artificial Intelligence Association Council.