Page 88 - Read Online
P. 88

Page 83                             Li et al. Intell Robot 2021;1(1):58-83  I http://dx.doi.org/10.20517/ir.2021.08



               93.  Li H, Yang SX, Karray F. Optimization of a neural dynamics based controller for a nonholonomic mobile robot using genetic algorithms.
                   In: The Fourth International Conference on Control and Automation, 2003. ICCA; 2003 Jun 12­12; Montreal,Canada. IEEE; 2003. pp.
                   911–16.
               94.  Yang SX, Yang H, Meng MQH. Neural dynamics based full­state tracking control of a mobile robot. In: IEEE International Conference
                   on Robotics and Automation, 2004. Proceedings. ICRA '04; 2004 Apr 26­ May 1; New Orleans, USA. IEEE; 2004. pp. 4614–19.
               95.  Xu Z, Yang SX, Gadsden SA. Enhanced bioinspired backstepping control for a mobile robot with unscented kalman filter. IEEE
                   Magazines and Online Publications 2020;8:125899–908.
               96.  Pan CZ, Lai XZ, Yang SX, Wu M. A biologically inspired approach to tracking control of underactuated surface vessels subject to
                   unknown dynamics. Expert Syst Appl 2015;42:2153 – 61.
               97.  Mohd Shamsuddin BPNF, Bin Mansor MA. Motion cntrol algorithm for path following and trajectory tracking for unmanned surface
                   vehicle: a review paper. In: 2018 3rd International Conference on Control, Robotics and Cybernetics (CRC). Proceedings. Piscataway,
                   NJ, USA; 2018. pp. 73 – 77.
               98.  Pan CZ, Lai XZ, Yang SX, Wu M. Backstepping neurodynamics based position­tracking control of underactuated autonomous surface
                   vehicles. In: 2013 25th Chinese Control and Decision Conference (CCDC); 2013 May 25­27; Guiyang, China. IEEE; 2013. pp. 2845–50.
               99.  Pan C, Lai X, Yang SX, Wu M. A bioinspired neural dynamics­based approach to tracking control of autonomous surface vehicles subject
                   to unknown ocean currents. Neural Comput Appl 2015;26:1929–38.
               100. Li D, Wang P, Du L. Path planning technologies for autonomous underwater vehicles­a review. IEEE Access 2019;7:9745 – 9768.
               101. Burdinsky IN. Guidance algorithm for an autonomous unmanned underwater vehicle to a given target. Optoelectron Instrum Data
                   Process 2012;48:69 – 74.
               102. Karkoub M, Wu HM, Hwang CL. Nonlinear trajectory­tracking control of an autonomous underwater vehicle. Ocean Eng
                   2017;145:188–98.
               103. Zhu D, Hua X, Sun B. A neurodynamics control strategy for real­time tracking control of autonomous underwater vehicle. J Navig 2013
                   aug;67:113–27.
               104. Sun B, Zhu D, Ding F, Yang SX. A novel tracking control approach for unmanned underwater vehicles based on bio­inspired neurody­
                   namics. IJ Mar Sci Tech­japan 2012;18:63–74.
               105. Sun B, Zhu D, Yang SX. A bioinspired filtered backstepping tracking control of 7000­m manned submarine vehicle. IEEE Trans Ind
                   Electron 2014;61:3682–93.
               106. Jiang Y, Guo C, Yu H. Robust trajectory tracking control for an underactuated autonomous underwater vehicle based on bioinspired
                   neurodynamics. Int J Adv Robot Syst 2018;15:172988141880674.
               107. Peng Z, Wen G, Rahmani A, Yu Y. Leader–follower formation control of nonholonomic mobile robots based on a bioinspired neurody­
                   namic based approach. Robot Auton Syst 2013;61:988–96.
               108. Yi G, Mao J, Wang Y, Zhang H, Miao Z. Neurodynamics­based leader­follower formation tracking of multiple nonholonomic vehicles.
                   Assembly Autom 2018;38:548–57.
               109. He Y, Mou J, Chen L, et al. Survey on hydrodynamic effects on cooperative control of Maritime Autonomous Surface Ships. Ocean
                   Eng 2021;235.
               110. Peng Z, Wang J, Wang D, Han QL. An overview of recent advances in coordinated control of multiple autonomous surface vehicles.
                   IEEE Trans Industr Inform 2021;17:732-45.
               111. Wang D, Fu M. Adaptive formation control for waterjet USV with input and output constraints based on bioinspired neurodynamics.
                   IEEE Access 2019;7:165852–61.
               112. Wang D, Ge SS, Fu M, Li D. Bioinspired neurodynamics based formation control for unmanned surface vehicles with line­of­sight range
                   and angle constraints. Neurocomputing 2021;425:127–34.
               113. Yang Y, Xiao Y, Li T. A survey of autonomous underwater vehicle formation: performance, formation control, and communication
                   capability. IEEE Commun Surv Tutor 2021;23:815-41.
               114. Hadi B, Khosravi A, Sarhadi P. A review of the path planning and formation control for multiple autonomous underwater vehicles. J
                   Intel Robot Syst 2021;101.
               115. Ding G, Zhu D, Sun B. Formation control and obstacle avoidance of multi­AUV for 3­D underwater environment. In: Proceedings of
                   the 33rd Chinese Control Conference; 2014 Jul 28­30 ;Nanjing, China. IEEE; 2014. pp. 8347–52.
   83   84   85   86   87   88   89   90   91   92   93