REFERENCES
1. Goheen KR, Jefferys ER. Multivariable self-tuning autopilots for autonomous and remotely operated underwater vehicles. IEEE J Oceanic Eng 1990;15:144-51.
2. García-Valdovinos LG, Salgado-Jiménez T, Bandala-Sánchez M, Nava-Balanzar L, Hernández-Alvarado R, Cruz-ledesma JA. Modelling, Design and robust control of a remotely operated underwater vehicle. Int J Adv Robot Syst 2014;11:1.
3. Reisenbichler KR, Chaffey MR, Cazenave F, et al., Automating MBARI's midwater time-series video surveys: The transition from ROV to AUV., In: OCEANS 2016 MTS/IEEE Monterey; 2016. pp. 1–9.
4. Petillot YR, Antonelli G, Casalino G, Ferreira F. underwater robots: from remotely operated vehicles to intervention-autonomous underwater vehicles. IEEE Robot Automat Mag 2019;26:94-101.
5. Wynn RB, Huvenne VAI, Le Bas TP, et al. Autonomous underwater vehicles (AUVs): their past, present and future contributions to the advancement of marine geoscience. Marine Geology 2014;352:451-68.
6. Shi Y, Shen C, Fang H, Li H. Advanced control in marine mechatronic systems: a survey. IEEE/ASME Trans Mechatron 2017;22:1121-31.
7. Li J, Xu Z, Zhu D, et al. Bio-inspired intelligence with applications to robotics: a survey. Intell Robot 2021;1:58-83.
8. Fossen TI., Guidance and control of ocean vehicles., University of Trondheim, Norway, Printed by John Wiley & Sons, Chichester, England, ISBN: 0 471 94113 1, Doctors Thesis 1999.
9. Bogue R. Underwater robots: a review of technologies and applications. Indus Robot 2015;42:186-91.
10. Gafurov SA, Klochkov EV. Autonomous unmanned underwater vehicles development tendencies. Procedia Engineering 2015;106:141-48.
12. Alam K, Ray T, Anavatti SG. A brief taxonomy of autonomous underwater vehicle design literature. Ocean Engineering 2014;88:627-30.
13. Sousa J, Cruz N, Matos A, Pereira FL., Multiple AUVs for coastal oceanography., In: Oceans' 97. MTS/IEEE Conference Proceedings. vol. 1. IEEE; 1997. pp. 409–14.
14. Singh H, Catipovic J, Eastwood R, et al., An integrated approach to multiple AUV communications, navigation and docking., In: OCEANS 96 MTS/IEEE Conference Proceedings. The Coastal Ocean-Prospects for the 21st Century. vol. 1. IEEE; 1996. pp. 59–64.
15. Sotzing CC, Evans J, Lane DM., A multi-agent architecture to increase coordination efficiency in multi-auv operations., In: OCEANS 2007-Europe. IEEE; 2007. pp. 1–6.
16. Yu W, Chen G, Cao M. Distributed leader–follower flocking control for multi-agent dynamical systems with time-varying velocities. Syst Contr Letters 2010;59:543-52.
17. Yamaguchi H. A cooperative hunting behavior by mobile-robot troops. Int J Robot Res 1999;18:931-40.
18. Vidal R, Shakernia O, Kim HJ, Shim DH, Sastry S. Probabilistic pursuit-evasion games: theory, implementation, and experimental evaluation. IEEE Trans Robot Automat 2002;18:662-9.
19. Chung TH, Hollinger GA, Isler V. Search and pursuit-evasion in mobile robotics. Auton Robot 2011;31:299-316.
20. Hespanha JP, Kim HJ, Sastry S., Multiple-agent probabilistic pursuit-evasion games., In: Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No. 99CH36304). vol. 3. IEEE; 1999. pp. 2432–37.
21. Guler S, Fidan B, Gazi V., Adaptive swarm coordination and formation control., In: Tan Y, editor. Handbook of Research on Design, Control, and Modeling of Swarm Robotics. IGI Global; 2016. pp. 175-206.
22. Yan J, Yang X, Luo X, Chen C. Energy-efficient data collection over AUV-assisted underwater acoustic sensor network. IEEE Syst J 2018;12:3519-30.
23. Huang M, Zhang K, Zeng Z, Wang T, Liu Y. An AUV-assisted data gathering scheme based on clustering and matrix completion for smart ocean. IEEE Internet Things J 2020;7:9904-18.
24. Duan R, Du J, Ren J, et al., VoI based information collection for AUV assisted underwater acoustic sensor networks., In: ICC 2020-2020 IEEE International Conference on Communications (ICC). IEEE; 2020. pp. 1–6.
25. Tan HP, Diamant R, Seah WK, Waldmeyer M. A survey of techniques and challenges in underwater localization. Ocean Engineering 2011;38:1663-76.
26. Kinsey JC, Eustice RM, Whitcomb LL., A survey of underwater vehicle navigation: Recent advances and new challenges., In: IFAC conference of manoeuvering and control of marine craft. vol. 88. Lisbon; 2006. pp. 1–12.
27. Yang Y, Xiao Y, Li T. A survey of autonomous underwater vehicle formation: Performance, formation control, and communication capability. IEEE Commun Surv Tutorials 2021;23:815-41.
28. Fossen TI., Handbook of marine craft hydrodynamics and motion control., Chichester: John Wiley & Sons, Ltd; 2011. pp. 343-415.
29. Das B, Subudhi B, Pati BB. Cooperative formation control of autonomous underwater vehicles: an overview. Int J Autom Comput 2016;13:199-225.
30. Wei X, Wang X, Bai X, Bai S, Liu J. Autonomous underwater vehicles localisation in mobile underwater networks. IJSNET 2017;23:61.
31. Chen YQ, Wang Z., Formation control: a review and a new consideration., In: 2005 IEEE/RSJ International conference on intelligent robots and systems. IEEE; 2005. pp. 3181–86.
33. Zhang Y, Mehrjerdi H., A survey on multiple unmanned vehicles formation control and coordination: Normal and fault situations., In: 2013 International conference on unmanned aircraft systems (ICUAS). IEEE; 2013. pp. 1087–96.
34. Do HT, Hua HT, Nguyen MT, et al. Formation control algorithms for multiple-uavs: a comprehensive survey. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2021;8:e3-e3.
35. Ziquan Y, Zhang Y, Jiang B, Jun F, Ying J. A review on fault-tolerant cooperative control of multiple unmanned aerial vehicles. Chinese J Aeronaut 2022;35:1-18.
36. Cai G, Dias J, Seneviratne L. A survey of small-scale unmanned aerial vehicles: recent advances and future development trends. Un Sys 2014;02:175-99.
37. Dong X, Yu B, Shi Z, Zhong Y. Time-varying formation control for unmanned aerial vehicles: Theories and applications. IEEE Trans Contr Syst Technol 2015;23:340-8.
38. Scharf DP, Hadaegh FY, Ploen SR., A survey of spacecraft formation flying guidance and control. part ii: control., In: Proceedings of the 2004 American control conference. vol. 4. Ieee; 2004. pp. 2976–85.
40. Yuh J. Design and control of autonomous underwater robots: a survey. Autonomous Robots 2000;8:7-24.
41. Li X, Zhu D, Qian Y. A survey on formation control algorithms for multi-AUV system. Un Sys 2014;02:351-9.
42. Hadi B, Khosravi A, Sarhadi P. A review of the path planning and formation control for multiple autonomous underwater vehicles. J Intell Robot Syst 2021;101.
43. Wang X, Zerr B, Thomas H, Clement B, Xie Z. Pattern formation of multi-AUV systems with the optical sensor based on displacement-based formation control. Int J Syst Sci 2020;51:348-67.
44. Edwards D, Bean T, Odell D, Anderson M., A leader-follower algorithm for multiple AUV formations., In: 2004 IEEE/OES Autonomous Underwater Vehicles (IEEE Cat. No. 04CH37578). IEEE; 2004. pp. 40–46.
45. Ren W, Sorensen N. Distributed coordination architecture for multi-robot formation control. Robot Auton Syst 2008;56:324-33.
46. Cui R, Ge SS, How BVE, Choo YS. Leader–follower formation control of underactuated autonomous underwater vehicles. Ocean Engineering 2010;37:1491-502.
47. Zheng J, Huang Y, Xiao Y. The effect of leaders on the consistency of group behaviour. IJSNET 2012;11:126-35.
48. Cao X, Guo L. A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments. Int J Adv Robot Syst 2019;16:1729881419870664.
49. Shi H, Wang L, Chu T. Virtual leader approach to coordinated control of multiple mobile agents with asymmetric interactions. Physica D: Nonlinear Phenomena 2006;213:51-65.
50. Droge G., Distributed virtual leader moving formation control using behavior-based MPC., In: 2015 American Control Conference (ACC). IEEE; 2015. pp. 2323–28.
51. Zheping Y, Yibo L, Jiajia Z, Gengshi Z., Moving target following control of multi-auvs formation based on rigid virtual leader-follower under ocean current., In: 2015 34th Chinese control conference (CCC). IEEE; 2015. pp. 5901–6.
52. Li J, Du X., Underactuated multi-AUV robust formation control based on virtual leader., In: 2018 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE; 2018. pp. 1568–73.
53. Tan KH, Lewis MA., Virtual structures for high-precision cooperative mobile robotic control., In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS'96. vol. 1. IEEE; 1996. pp. 132–39.
54. Lewis MA, Tan KH. High precision formation control of mobile robots using virtual structures. Auton Robot 1997;4:387-403.
55. Ren W, Beard RW. Decentralized scheme for spacecraft formation flying via the virtual structure approach. Journal of Guidance, Control, and Dynamics 2004;27:73-82.
56. Yuan J, Tang GY., Formation control for mobile multiple robots based on hierarchical virtual structures., In: IEEE ICCA 2010. IEEE; 2010. pp. 393–98.
57. Zhang Lc, Wang J, Wang T, Liu M, Gao J., Optimal formation of multiple AUVs cooperative localization based on virtual structure., In: OCEANS 2016 MTS/IEEE Monterey. IEEE; 2016. pp. 1–6.
58. Zhen Q, Wan L, Li Y, Jiang D. Formation control of a multi-AUVs system based on virtual structure and artificial potential field on SE (3). Ocean Engineering 2022;253:111148.
59. Yuan C, Licht S, He H. Formation learning control of multiple autonomous underwater vehicles with heterogeneous nonlinear uncertain dynamics. IEEE Trans Cybern 2018;48:2920-34.
60. Balch T, Arkin RC. Behavior-based formation control for multirobot teams. IEEE Trans Robot Automat 1998;14:926-39.
61. Monteiro S, Bicho E., A dynamical systems approach to behavior-based formation control., In: Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292). vol. 3. IEEE; 2002. pp. 2606–11.
62. Xiaomin M, Yang D, Xing L, Sentang W., Behavior-based formation control of multi-missiles., In: 2009 Chinese Control and Decision Conference. IEEE; 2009. pp. 5019–23.
63. Xu D, Zhang X, Zhu Z, Chen C, Yang P. Behavior-based formation control of swarm robots. Mathematical Problems in Engineering 2014;2014:1-13.
64. Hacene N, Mendil B. Behavior-based autonomous navigation and formation control of mobile robots in unknown cluttered dynamic environments with dynamic target tracking. Int J Autom Comput 2021;18:766-86.
65. Khatib O., Real-time obstacle avoidance for manipulators and mobile robots., In: Cox IJ, Wilfong GT, editors. Autonomous Robot Vehicles. New York: Springer; 1990. pp. 396-404.
66. Gazi V. Swarm aggregations using artificial potentials and sliding-mode control. IEEE Trans Robot 2005;21:1208-14.
67. Fiorelli E, Leonard NE, Bhatta P, Paley DA, Bachmayer R, et al. Multi-AUV control and adaptive sampling in Monterey Bay. IEEE J Oceanic Eng 2006;31:935-48.
68. Barnes L, Fields M, Valavanis K., Unmanned ground vehicle swarm formation control using potential fields., In: 2007 Mediterranean Conference on Control & Automation. IEEE; 2007. pp. 1–8.
69. Pereira AR, Hsu L. Adaptive formation control using artificial potentials for Euler-Lagrange agents. IFAC Proceedings Volumes 2008;41:10788-93.
70. Sabattini L, Secchi C, Fantuzzi C. Arbitrarily shaped formations of mobile robots: artificial potential fields and coordinate transformation. Auton Robot 2011;30:385-97.
71. Nair RR, Behera L, Kumar V, Jamshidi M. Multisatellite formation control for remote sensing applications using artificial potential field and adaptive fuzzy sliding mode control. IEEE Syst J 2014;9:508-18.
72. Ying Z, Xu L., Leader-follower formation control and obstacle avoidance of multi-robot based on artificial potential field., In: The 27th Chinese Control and Decision Conference (2015 CCDC). IEEE; 2015. pp. 4355–60.
73. Ihle IA, Skjetne R, Fossen TI., Nonlinear formation control of marine craft with experimental results., In: 2004 43rd IEEE Conference on Decision and Control (CDC)(IEEE Cat. No. 04CH37601). vol. 1. IEEE; 2004. pp. 680–85.
74. Cui R, Xu D, Yan W., Formation control of autonomous underwater vehicles under fixed topology., In: 2007 IEEE International Conference on Control and Automation. IEEE; 2007. pp. 2913–18.
75. Yu W, Wen G, Chen G, Cao J., Distributed cooperative control of multi-agent systems., John Wiley & Sons; 2017.
76. Fink A, Kosecoff J, Chassin M, Brook RH. Consensus methods: characteristics and guidelines for use. Am J Public Health 1984;74:979-83.
77. Ren W, Beard RW, Atkins EM., A survey of consensus problems in multi-agent coordination., In: Proceedings of the 2005, American Control Conference, 2005. IEEE; 2005. pp. 1859–64.
78. Ren W, Beard RW, McLain TW., Coordination variables and consensus building in multiple vehicle systems., In: Kumar V, Leonard N, Morse AS, editors. Cooperative Control. Berlin: Springer Berlin Heidelberg; 2005. pp. 171-88.
79. Olfati-Saber R, Fax JA, Murray RM. Consensus and cooperation in networked multi-agent systems. Proc IEEE 2007;95:215-33.
80. Anderson BD, Yu C, Fidan B, Hendrickx JM. Rigid graph control architectures for autonomous formations. IEEE Control Syst 2008;28:48-63.
81. Mesbahi M, Egerstedt M., Graph theoretic methods in multiagent networks., In: Graph Theoretic Methods in Multiagent Networks. Princeton University Press; 2010.
82. Ren W., Consensus based formation control strategies for multi-vehicle systems., In: 2006 American Control Conference. IEEE; 2006. pp. 6–pp.
83. Porfiri M, Roberson DG, Stilwell DJ. Tracking and formation control of multiple autonomous agents: a two-level consensus approach. Automatica 2007;43:1318-28.
84. Luo X, Han N, Guan X. Leader-following consensus protocols for formation control of multi-agent network. J Syst Eng Electron 2011;22:991-7.
85. Dong R, Geng Z. Consensus based formation control laws for systems on Lie groups. Syst Contr Letters 2013;62:104-11.
86. Dong R, Geng Z. Consensus for formation control of multi-agent systems. Int J Robust Nonlinear Control 2015;25:2481-501.
87. Falconi R, Sabattini L, Secchi C, Fantuzzi C, Melchiorri C. Edge-weighted consensus-based formation control strategy with collision avoidance. Robotica 2015;33:332-47.
88. Listmann KD, Masalawala MV, Adamy J., Consensus for formation control of nonholonomic mobile robots., In: 2009 IEEE international conference on robotics and automation. IEEE; 2009. pp. 3886–91.
89. Wang W, Huang J, Wen C, Fan H. Distributed adaptive control for consensus tracking with application to formation control of nonholonomic mobile robots. Automatica 2014;50:1254-63.
90. Peng Z, Wen G, Rahmani A, Yu Y. Distributed consensus-based formation control for multiple nonholonomic mobile robots with a specified reference trajectory. Int J Syst Sci 2015;46:1447-57.
91. Peng Z, Wen G, Yang S, Rahmani A. Distributed consensus-based formation control for nonholonomic wheeled mobile robots using adaptive neural network. Nonlinear Dynamics 2016;86:605-22.
92. Cheng Y, Jia R, Du H, Wen G, Zhu W. Robust finite-time consensus formation control for multiple nonholonomic wheeled mobile robots via output feedback. Int J Robust Nonlinear Control 2018;28:2082-96.
93. Kuriki Y, Namerikawa T., Consensus-based cooperative formation control with collision avoidance for a multi-UAV system., In: 2014 American Control Conference. IEee; 2014. pp. 2077–82.
94. Kuriki Y, Namerikawa T. Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control. SICE Journal of Control, Measurement, and System Integration 2015;8:285-94.
95. Mu B, Li H, Ding J, Shi Y. Consensus in second-order multiple flying vehicles with random delays governed by a Markov chain. Journal of the Franklin Institute 2015;352:3628-44.
96. Du H, Zhu W, Wen G, Duan Z, Lü J. Distributed formation control of multiple quadrotor aircraft based on nonsmooth consensus algorithms. IEEE Trans Cybern 2019;49:342-53.
97. Kuo CW, Tsai CC, Lee CT. Intelligent leader-following consensus formation control using recurrent neural networks for small-size unmanned helicopters. IEEE Trans Syst Man Cybern, Syst 2021;51:1288-301.
98. Wu Y, Gou J, Hu X, Huang Y. A new consensus theory-based method for formation control and obstacle avoidance of UAVs. Aerospace Science and Technology 2020;107:106332.
99. Ren W., Distributed attitude consensus among multiple networked spacecraft., In: 2006 American control conference. IEEE; 2006. pp. 6–pp.
100. Ren W. Distributed attitude alignment in spacecraft formation flying. Int J Adapt Control Signal Process 2007;21:95-113.
101. Guiming L, Liangdong L. Coordinated multiple spacecraft attitude control with communication time delays and uncertainties. Chinese J Aeronaut 2012;25:698-708.
102. Nazari M, Butcher EA, Yucelen T, Sanyal AK. Decentralized consensus control of a rigid-body spacecraft formation with communication delay. Journal of Guidance, Control, and Dynamics 2016;39:838-51.
103. Silvestre C, Pascoal A. Control of the INFANTE AUV using gain scheduled static output feedback. Control Engineering Practice 2004;12:1501-9.
104. Kaminer I, Pascoal AM, Khargonekar PP, Coleman EE. A velocity algorithm for the implementation of gain-scheduled controllers. Automatica 1995;31:1185-91.
105. Mohamed SA, Osman AA, Attia SA, Maged SA., Dynamic model and control of an autonomous underwater vehicle., In: 2020 International Conference on Innovative Trends in Communication and Computer Engineering (ITCE). IEEE; 2020. pp. 182–90.
106. Nag A, Patel SS, Kishore K, Akbar S., A robust H-infinity based depth control of an autonomous underwater vehicle., In: 2013 International Conference on Advanced Electronic Systems (ICAES). IEEE; 2013. pp. 68–73.
107. Zhang Y, Liu X, Luo M, Yang C. MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments. Ocean Engineering 2019;189:106309.
108. Naeem W, Sutton R, Ahmad S. LQG/LTR control of an autonomous underwater vehicle using a hybrid guidance law. IFAC Proceedings Volumes 2003;36:31-36.
109. Long C, Qin X, Bian Y, Hu M. Trajectory tracking control of ROVs considering external disturbances and measurement noises using ESKF-based MPC. Ocean Engineering 2021;241:109991.
110. Chellabi A, Nahon M., Feedback linearization control of undersea vehicles., In: Proceedings of OCEANS '93; 1993. pp. I410–ol. 1.
111. Shen C, Shi Y, Buckham B. Trajectory tracking control of an autonomous underwater vehicle using Lyapunov-based model predictive control. IEEE Trans Ind Electron 2018;65:5796-805.
112. Shen C, Shi Y, Buckham B., Nonlinear model predictive control for trajectory tracking of an AUV: A distributed implementation., In: 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE; 2016. pp. 5998–6003.
113. Li H, Xie P, Yan W. Receding horizon formation tracking control of constrained underactuated autonomous underwater vehicles. IEEE Trans Ind Electron 2017;64:5004-13.
114. Wei H, Shen C, Shi Y. Distributed Lyapunov-based model predictive formation tracking control for autonomous underwater vehicles subject to disturbances. IEEE Trans Syst Man Cybern, Syst 2019;51:5198-208.
115. Khodayari MH, Balochian S. Modeling and control of autonomous underwater vehicle (AUV) in heading and depth attitude via self-adaptive fuzzy PID controller. J Mar Sci Technol 2015;20:559-78.
116. Liang X, Qu X, Wan L, Ma Q. Three-dimensional path following of an underactuated AUV based on fuzzy backstepping sliding mode control. Int J Fuzzy Systl 2018;20:640-9.
117. Nan D, Weng Y, Liu Y, Wang X., Accurate trajectory tracking control of unknown autonomous underwater vehicles: A data-driven predictive approach., In: 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS). IEEE; 2021. pp. 1241–45.
118. Wang D, He B, Shen Y, Li G, Chen G. A Modified ALOS Method of Path Tracking for AUVs with Reinforcement Learning Accelerated by Dynamic Data-Driven AUV Model. J Intell Robot Syst 2022;104:1-23.
119. Shojaei K. Neural network formation control of underactuated autonomous underwater vehicles with saturating actuators. Neurocomputing 2016;194:372-84.
120. Young KD, Utkin VI, Ozguner U. A control engineer's guide to sliding mode control. IEEE Trans Contr Syst Technol 1999;7:328-42.
122. Perruquetti W, Barbot JP., Sliding mode control in engineering. vol. 11., Marcel Dekker New York; 2002.
123. Yan Z, Wang M, Xu J. Robust adaptive sliding mode control of underactuated autonomous underwater vehicles with uncertain dynamics. Ocean Engineering 2019;173:802-9.
124. Guo Y, Qin H, Xu B, Han Y, Fan QY, et al. Composite learning adaptive sliding mode control for AUV target tracking. Neurocomputing 2019;351:180-86.
125. Huang B, Yang Q. Double-loop sliding mode controller with a novel switching term for the trajectory tracking of work-class ROVs. Ocean Engineering 2019;178:80-94.
126. Yan Y, Yu S. Sliding mode tracking control of autonomous underwater vehicles with the effect of quantization. Ocean Engineering 2018;151:322-28.
127. Lee PM, Hong SW, Lim YK, et al. Discrete-time quasi-sliding mode control of an autonomous underwater vehicle. IEEE J Oceanic Eng 1999;24:388-95.
128. Zhihong M, Yu XH. Terminal sliding mode control of MIMO linear systems. IEEE Trans Circuits Syst I 1997;44:1065-70.
129. Wang Y, Gu L, Gao M, Zhu K. Multivariable output feedback adaptive terminal sliding mode control for underwater vehicles. Asian J Contr 2016;18:247-65.
130. Elmokadem T, Zribi M, Youcef-Toumi K. Terminal sliding mode control for the trajectory tracking of underactuated Autonomous Underwater Vehicles. textitOcean Engineering 2017;129:613-25.
131. Qiao L, Zhang W. Adaptive non-singular integral terminal sliding mode tracking control for autonomous underwater vehicles. textitIET Control Theory & Applications 2017;11:1293-306.
132. Patre B, Londhe P, Waghmare L, Mohan S. Disturbance estimator based non-singular fast fuzzy terminal sliding mode control of an autonomous underwater vehicle. textitOcean Engineering 2018;159:372-87.
133. Rangel MAG, Manzanilla A, Suarez AEZ, Muñoz F, Salazar S, et al. Adaptive non-singular terminal sliding mode control for an unmanned underwater vehicle: Real-time experiments. textitInt J Control Autom Syst 2020;18:615-28.
134. Salgado-Jimenez T, Spiewak JM, Fraisse P, Jouvencel B., A robust control algorithm for AUV: based on a high order sliding mode., In: Oceans' 04 MTS/IEEE Techno-Ocean'04 (IEEE Cat. No. 04CH37600). vol. 1. IEEE; 2004. pp. 276–81.
135. Li X, Zhu D., Formation control of a group of AUVs using adaptive high order sliding mode controller., In: OCEANS 2016-Shanghai. IEEE; 2016. pp. 1–6.
136. Guerrero J, Antonio E, Manzanilla A, Torres J, Lozano R. Autonomous underwater vehicle robust path tracking: Auto-adjustable gain high order sliding mode controller. textitIFAC-PapersOnLine 2018;51:161-66.
137. Wang J, Wang C, Wei Y, Zhang C. Sliding mode based neural adaptive formation control of underactuated AUVs with leader-follower strategy. textitApplied Ocean Research 2020;94:101971.
138. Borlaug ILG, Pettersen KY, Gravdahl JT. Comparison of two second-order sliding mode control algorithms for an articulated intervention AUV: Theory and experimental results. textitOcean Engineering 2021;222:108480.
139. Yan T, Xu Z, Yang SX. Consensus Formation Control for Multiple AUVSystems Using Distributed Bioinspired Sliding Mode Control. textitIEEE Trans Intell Veh 2022:1-1.
140. Xu H, Zhang Gc, Cao J, Pang S, Sun Ys., Underactuated AUV nonlinear finite-time tracking control based on command filter and disturbance observer., textitSensors (Basel) 2019;19: 4987.Sensors(Basel)2019;19:498731731789.
141. Guerrero J, Torres J, Creuze V, Chemori A. Adaptive disturbance observer for trajectory tracking control of underwater vehicles. textitOcean Engineering 2020;200:107080.
142. Gao Z, Guo G. Fixed-time sliding mode formation control of AUVs based on a disturbance observer. textitIEEE/CAA J Autom Sinica 2020;7:539-45.
143. Su B, Wang Hb, Wang Y. Dynamic event-triggered formation control for AUVs with fixed-time integral sliding mode disturbance observer. textitOcean Engineering 2021;240:109893.
144. Wang H, Su B. Event-triggered formation control of AUVs with fixed-time RBF disturbance observer. textitApplied Ocean Research 2021;112:102638.
145. Li J, Du J, Chang WJ. Robust time-varying formation control for underactuated autonomous underwater vehicles with disturbances under input saturation. textitOcean Engineering 2019;179:180-88.
146. Gao Z, Guo G. Velocity free leader-follower formation control for autonomous underwater vehicles with line-of-sight range and angle constraints. textitInformation Sciences 2019;486:359-78.
147. Wang J, Wang C, Wei Y, Zhang C. Observer-Based Neural Formation Control of Leader–Follower AUVs With Input Saturation. textitIEEE Syst J 2021;15:2553-61.
148. Chen B, Hu J, Zhao Y, Ghosh BK. Finite-time velocity-free observer-based consensus tracking for heterogeneous uncertain AUVs via adaptive sliding mode control. textitOcean Engineering 2021;237:109565.
149. Yan Z, Zhang C, Tian W, Cai S, Zhao L. Distributed observer-based formation trajectory tracking method of leader-following multi-AUV system. textitOcean Engineering 2022;260:112019.
150. Filaretov V, Zhirabok A, Zuev A, Procenko A., The development of system of accommodation to faults of navigation sensors of underwater vehicles with resistance to disturbance., In: 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014); 2014. pp. 1548–53.
151. Xia Y, Xu K, Wang W, et al. Optimal robust trajectory tracking control of a X-rudder AUV with velocity sensor failures and uncertainties. textitOcean Engineering 2020;198:106949.
152. Liu Z, Yu X, Yuan C, Zhang Y., Leader-follower formation control of unmanned aerial vehicles with fault tolerant and collision avoidance capabilities., In: 2015 international conference on unmanned aircraft systems (ICUAS). IEEE; 2015. pp. 1025–30.
153. Wang X, Yadav V, Balakrishnan SN. Cooperative UAV Formation Flying With Obstacle/Collision Avoidance. textitIEEE Trans Contr Syst Technol 2007;15:672-79.
154. Chang K, Xia Y, Huang K. UAV formation control design with obstacle avoidance in dynamic three-dimensional environment. textitSpringerPlus 2016;5:1-16.
155. Shou Y, Xu B, Lu H, Zhang A, Mei T. Finite-time formation control and obstacle avoidance of multi-agent system with application. textitIntl J Robust & Nonlinear 2022;32:2883-901.
156. Lobo Pereira F, Borges de Sousa J, Gomes R, Calado P., A model predictive control approach to AUVs motion coordination., In: van Schuppen JH, Villa T, editors. Coordination Control of Distributed Systems. Cham: Springer International Publishing; 2015. pp. 9-18.
157. Guo H, Shen C, Zhang H, Chen H, Jia R. Simultaneous trajectory planning and tracking using an MPC method for cyber-physical systems: A case study of obstacle avoidance for an intelligent vehicle. textitIEEE Trans Ind Inf 2018;14:4273-83.
158. Wang X, Yao X, Zhang L. Path planning under constraints and path following control of autonomous underwater vehicle with dynamical uncertainties and wave disturbances. textitJ Intell Robot Syst 2020;99:891-908.
159. Lindqvist B, Mansouri SS, Agha-mohammadi Aa, Nikolakopoulos G. Nonlinear MPC for collision avoidance and control of UAVs with dynamic obstacles. textitIEEE Robot Autom Lett 2020;5:6001-8.
160. Zhang Gl, Jia Hm., Global path planning of AUV based on improved ant colony optimization algorithm., In: 2012 IEEE International Conference on Automation and Logistics. IEEE; 2012. pp. 606–10.
161. Lin C, Wang H, Yuan J, Fu M. An online path planning method based on hybrid quantum ant colony optimization for AUV. textitInt J Robot Autom 2018;33:435-44.
162. Phung MD, Quach CH, Dinh TH, Ha Q. Enhanced discrete particle swarm optimization path planning for UAV vision-based surface inspection. textitAutom Construction 2017;81:25-33.
163. Wang D, Fan T, Han T, Pan J. A two-stage reinforcement learning approach for multi-UAV collision avoidance under imperfect sensing. textitIEEE Robot Autom Lett 2020;5:3098-105.
164. Yan Z, Yang Z, Yue L, et al. Discrete-time coordinated control of leader-following multiple AUVs under switching topologies and communication delays. textitOcean Engineering 2019;172:361-72.
165. Sørensen FF, von Benzon M, Liniger J, Pedersen S. A quantitative parametric study on output time delays for autonomous underwater cleaning operations. textitJMSE 2022;10:815.
166. Pedersen S, Liniger J, Sørensen FF, Schmidt K, von Benzon M, et al. Stabilization of a ROV in three-dimensional space using an underwater acoustic positioning system. textitIFAC-PapersOnLine 2019;52:117-22.
167. Millán P, Orihuela L, Jurado I, Rubio FR. Formation control of autonomous underwater vehicles subject to communication delays. textitIEEE Trans Contr Syst Technol 2013;22:770-77.
168. Yan Z, Pan X, Yang Z, Yue L. Formation control of leader-following multi-UUVs with uncertain factors and time-varying delays. textitIEEE Access 2019;7:118792-805.
169. Chen S, Ho DW. Consensus control for multiple AUVs under imperfect information caused by communication faults. textitInformation Sciences 2016;370-371:565-77.
170. Burlutskiy N, Touahmi Y, Lee BH. Power efficient formation configuration for centralized leader–follower AUVs control. textitJ Mar Sci Technol 2012;17:315-29.
171. Sharif BS, Neasham J, Hinton OR, Adams AE. A computationally efficient Doppler compensation system for underwater acoustic communications. textitIEEE J Oceanic Eng 2000;25:52-61.
172. Li B, Zhou S, Stojanovic M, Freitag L, Willett P. Multicarrier communication over underwater acoustic channels with nonuniform Doppler shifts. textitIEEE J Oceanic Eng 2008;33:198-209.
173. Yoshizawa S, Saito T, Mabuchi Y, Tsukui T, Sawada S. Parallel resampling of OFDM signals for fluctuating doppler shifts in underwater acoustic communication. textitJ Electr Compu Eng 2018;2018:1-11.
174. Hu Z, Ma C, Zhang L, et al. Formation control of impulsive networked autonomous underwater vehicles under fixed and switching topologies. textitNeurocomputing 2015;147:291-98.
175. Yan Z, Xu D, Chen T, Zhang W, Liu Y. Leader-follower formation control of UUVs with model uncertainties, current disturbances, and unstable communication. textitSensors 2018;18:662.
176. Seuret A, de Wit CC, et al., Contraction control of a fleet circular formation of AUVs under limited communication range., In: Proceedings of the 2010 American Control Conference. IEEE; 2010. pp. 5991–96.
