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Zhu et al. Intell Robot 2022;2(3):200­222  I http://dx.doi.org/10.20517/ir.2022.13  Page 220


                   2008;56:1102­14. DOI
               70.  Zheng X, Koenig S. Robot coverage of terrain with non­uniform traversability. In: 2007 IEEE/RSJ International Conference on Intelligent
                   Robots and Systems. Piscataway, NJ, USA; 2007. pp. 3757­64. DOI
               71.  Kapanoglu M, Alikalfa M, Ozkan M, Yazici A, Parlaktuna O. A pattern­based genetic algorithm for multi­robot coverage path planning
                   minimizing completion time. J Intell Manuf 2012;23:1035­45. DOI
               72.  Yang SX, Luo C. A neural network approach to complete coverage path planning. IEEE Trans Syst Man Cybern B Cybern 2004;34:718–
                   24. DOI
               73.  Yao P, Zhao Z. Improved Glasius bio­inspired neural network for target search by multi­agents. Information Sci 2021;568:40­53. DOI
               74.  Cai W, Zhang M, Zheng YR. Task assignment and path planning for multiple autonomous underwater vehicles using 3D dubins curves.
                   Sensors 2017;17:1607­26. DOI
               75.  Yao P, Qiu L, Qi J, Yang R. AUV path planning for coverage search of static target in ocean environment. Ocean Eng 2021;241. DOI
               76.  Song D, Yao P. Search for static target in nonwide area by AUV: a prior data­driven strategy. IEEE Syst J 2021;15:3185­8. DOI
               77.  Yao P, Zhu Q, Zhao R. Gaussian mixture model and self­organizing map neural­network­based coverage for target search in curve­shape
                   area. IEEE Trans Cybern 2022;52:3971–83. DOI
               78.  Sun P, Boukerche A. Modeling and analysis of coverage degree and target detection for autonomous underwater vehicle­based system.
                   IEEE Trans Veh Technol 2018;67:9959­71. DOI
               79.  Bacha S, Saadi R, Ayad MY, Aboubou A, Bahri M. A review on vehicle modeling and control technics used for autonomous vehicle path
                   following. In: 2017 International Conference on Green Energy Conversion Systems (GECS). Piscataway, NJ, USA; 2017. pp. 1­6. DOI
               80.  Liu X, Zhang M, Rogers E. Trajectory tracking control for autonomous underwater vehicles based on fuzzy re­planning of a local desired
                   trajectory. IEEE Trans Veh Technol 2019;68:11657­67. DOI
               81.  Ray S, Bhowal R, Patel P, Panaiyappan AK. An overview of the design and development of a 6 dof remotely operated vehicle for
                   underwater structural inspection. In: 2021 International Conference on Communication, Control and Information Sciences (ICCISC).
                   Piscataway, NJ, USA; 2021. pp. 1­6. DOI
               82.  Shen C, Shi Y, Buckham B. Trajectory tracking control of an autonomous underwater vehicle using lyapunov­based model predictive
                   control. IEEE Trans Ind Electron 2018;65:5796­805. DOI
               83.  Li J, Xu Z, Zhu D, et al. Bio­inspired intelligence with applications to robotics: a survey. Intell Robot 2022;1:58–83. DOI
               84.  Zhu D, Sun B. The bio­inspired model based hybrid sliding­mode tracking control for unmanned underwater vehicles. Eng Appl Artif
                   Intell 2013;26:2260­9. DOI
               85.  Sun B, Zhang W, Song A, Zhu X, Zhu D. Trajectory tracking and obstacle avoidance control of unmanned underwater vehicles based
                   on MPC. In: IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS). Piscataway, NJ,
                   USA; 2018. pp. 1–6. DOI
               86.  Wan L, Sun N, Liao YL. Backstepping control method for the trajectory tracking for the underactuated autonomous underwater vehicle.
                   AMR2013;798­799:484­8. DOI
               87.  Karkoub M, Wu HM, Hwang CL. Nonlinear trajectory­tracking control of an autonomous underwater vehicle. Ocean Eng 2017;145:188­
                   98. DOI
               88.  Yang SX, Meng MQH. Real­time collision­free motion planning of a mobile robot using a Neural Dynamics­based approach. IEEE
                   Trans Neural Netw 2003;14:1541­52. DOI
               89.  Li T, Zhao R, Chen CLP, Fang L, Liu C. Finite­time formation control of under­actuated ships using nonlinear sliding mode control.
                   IEEE Trans Cybern 2018;48:3243­53. DOI
               90.  Qin J, Zhang G, Zheng WX, Kang Y. Adaptive sliding mode consensus tracking for second­order nonlinear multiagent systems with
                   actuator faults. IEEE Trans Cybern 2019;49:1605­15. DOI
               91.  Zaihidee FM, Mekhilef S, Mubin M. Robust speed control of PMSM using sliding mode control (SMC)­a review. Energies 2019;12:1669­
                   96. DOI
               92.  Dhanasekar R, Ganesh Kumar S, Rivera M. Sliding mode control of electric drives/review. In: 2016 IEEE International Conference on
                   Automatica (ICA­ACCA). Piscataway, NJ, USA; 2016. pp. 1–7. DOI
               93.  Liu H, Zhang T. Fuzzy sliding mode control of robotic manipulators with kinematic and dynamic uncertainties. J DYN SYST­T ASME
                   2012;134. DOI
               94.  Slotine JJE, Coetsee JA. Adaptive sliding controller synthesis for non­linear systems. Int J Control 1986;43:1631­51. DOI
               95.  Xu Z, X Yang S, Gadsden SA, Li J, Zhu D. Backstepping and sliding mode control for AUVs aided with bioinspired neurodynamics. In:
                   2021 IEEE International Conference on Robotics and Automation (ICRA). Xi’an, China; 2021. pp. 2113­9. DOI
               96.  Bai G, Meng Y, Liu L, Luo W, Gu Q. Review and comparison of path tracking based on model predictive control. Electronics 2019;8:1077
                   (32 pp.) . DOI
               97.  Dong L, Yan J, Yuan X, He H, Sun C. Functional nonlinear model predictive control based on adaptive dynamic programming. IEEE
                   Trans Cybern 2019;49:4206­18. DOI
               98.  Liu L, He Y, Han C. Review of model predictive control methods for time­delay systems. In: Proceedings of 2020 Chinese Intelligent
                   Systems Conference. Lecture Notes in Electrical Engineering (LNEE 705). vol. 1. Singapore; 2021. pp. 624–33. DOI
               99.  Gutierrez B, Kwak SS. Modular multilevel converters (MMCs) controlled by model predictive control with reduced calculation burden.
                   IEEE Trans Power Electron 2018;33:9176­87. DOI
               100. Na J, Huang Y, Wu X, Su S, Li G. Adaptive finite­time fuzzy control of nonlinear active suspension systems with input delay. IEEE
                   Trans Cybern 2020;50:2639­50. DOI
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