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http://dx.doi.org/10.5370/JEET.2017.12.6.2365

Design of Adaptive Neural Tracking Controller for Pod Propulsion Unmanned Vessel Subject to Unknown Dynamics  

Mu, Dong-Dong (School of Information Science and Technology, Dalian Maritime University)
Wang, Guo-Feng (School of Information Science and Technology, Dalian Maritime University)
Fan, Yun-Sheng (School of Information Science and Technology, Dalian Maritime University)
Publication Information
Journal of Electrical Engineering and Technology / v.12, no.6, 2017 , pp. 2365-2377 More about this Journal
Abstract
This paper addresses two interrelated problems concerning the tracking control of pod propulsion unmanned surface vessel (USV), namely, the modeling of pod propulsion USV, and tracking controller design. First, based on MMG modeling theory, the model of pod propulsion USV is derived. Furthermore, a practical adaptive neural tracking controller is proposed by backstepping technique, neural network approximation and adaptive method. Meanwhile, unlike some existing tracking methods for surface vessel whose control algorithms suffer from "explosion of complexity", a novel neural shunting model is introduced to solve the problem. Using a Lyapunov functional, it is proven that all error signals in the system are uniformly ultimately bounded. The advantages of the paper are that first, the underactuated characteristic of pod propulsion USV is proved; second, the neural shunting model is used to solve the problem of "explosion of complexity", and this is a combination of knowledge in the field of biology and engineering; third, the developed controller is able to capture the uncertainties without the exact information of hydrodynamic damping structure and the sea disturbances. Numerical examples have been given to illustrate the performance and effectiveness of the proposed scheme.
Keywords
Pod; USV; Modeling; Tracking control; Adaptive; Robot and automation;
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Times Cited By KSCI : 1  (Citation Analysis)
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