caa a22 4500 606159975 CHVBK 20210128100627.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1839-6 doi (NATIONALLICENCE)springer-10.1007/s00521-015-1839-6 A bioinspired neural dynamics-based approach to tracking control of autonomous surface vehicles subject to unknown ocean currents [Elektronische Daten] [Changzhong Pan, Xuzhi Lai, Simon Yang, Min Wu] This paper addresses the trajectory tracking control problem of an autonomous surface vehicle (ASV) subject to unknown ocean currents, where smooth and continuous velocity commands are desirable for safe and effective operation. A novel bioinspired approach is proposed by integrating three neural dynamics models into the conventional Lyapunov synthesis. The tracking controller is derived from the error dynamics analysis of the ASV and the stability analysis of the control system. A simple observer is proposed to estimate the unknown ocean currents, which only requires the position of the ASV. The overall control system under the controller and observer is rigorously proved to be asymptotically stable by a Lyapunov stability theory for cascaded systems. The most contribution is that the proposed tracking controller is capable of eliminating the sharp velocity jumps due to sudden tracking error changes and generating smooth and continuous control signals. In addition, it can deals with the situation with unknown ocean currents. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies. The Natural Computing Applications Forum, 2015 Neural dynamics nationallicence Tracking control nationallicence Autonomous surface vehicles nationallicence Unknown ocean currents nationallicence Pan Changzhong School of Information and Electrical Engineering, Hunan University of Science and Technology, 411201, Xiangtan, Hunan, China aut Lai Xuzhi School of Automation, China University of Geosciences, 430074, Wuhan, Hubei, China aut Yang Simon School of Engineering, University of Guelph, N1G 2W1, Guelph, ON, Canada aut Wu Min School of Automation, China University of Geosciences, 430074, Wuhan, Hubei, China aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1929-1938 0941-0643 26:8<1929 2015 26 521 https://doi.org/10.1007/s00521-015-1839-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1839-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Pan Changzhong School of Information and Electrical Engineering, Hunan University of Science and Technology, 411201, Xiangtan, Hunan, China aut NATIONALLICENCE 700 1- Lai Xuzhi School of Automation, China University of Geosciences, 430074, Wuhan, Hubei, China aut NATIONALLICENCE 700 1- Yang Simon School of Engineering, University of Guelph, N1G 2W1, Guelph, ON, Canada aut NATIONALLICENCE 700 1- Wu Min School of Automation, China University of Geosciences, 430074, Wuhan, Hubei, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1929-1938 0941-0643 26:8<1929 2015 26 521