A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 1 Issue 1
Jan.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Xiaoming Sun and Shuzhi Sam Ge, "Adaptive Neural Region Tracking Control of Multi-fully Actuated Ocean Surface Vessels," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 77-83, 2014.
Citation: Xiaoming Sun and Shuzhi Sam Ge, "Adaptive Neural Region Tracking Control of Multi-fully Actuated Ocean Surface Vessels," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 77-83, 2014.

Adaptive Neural Region Tracking Control of Multi-fully Actuated Ocean Surface Vessels

Funds:

This work was supported by National Basic Research Program of China (973 Program) (2011CB707005).

  • In this paper, adaptive neural network region tracking control is designed to force a group of fully actuated ocean vessels with limited sensing range to track a common moving target region, in the presence of uncertainties and unknown disturbances. In this control concept, the desired objective is specified as a moving region instead of a stationary point, region or a path. The controllers guarantee the connectivity preservation of the dynamic interaction network, and no collisions happen between any ocean vessels in the group. The tracking control design is based on the artificial potential functions, approximation-based backstepping design technique, and Lyapunov's method. It is proved that under the adaptive neural network control law, the tracking error of each ocean vessel converges to an adjustable neighborhood of the origin, although some of them do not access the desired target region directly. Simulation results are presented to illustrate the performance of the proposed approach.

     

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