Minimum time ship maneuvering method using neural network and nonlinear model predictive compensator |
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| Affiliation: | 1. Nagoya Institute of Technology, Gokiso-cho, Shouwa-ku, Nagoya 466-8555, Japan;2. National Maritime Research Institute, 6-38-1,Shinkawa, Mitaka, Tokyo, Japan;3. Tokyo University of Marine Science and Technology, 2-6-1, Etchujima, Koto-ku, Tokyo, Japan;1. Dipartimento di Ingegneria Informatica, Gestionale e dell''Automazione, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy;2. Industrial Control Centre, Department of Electric and Electronic Engineering, University of Strathclyde, Glasgow, UK;3. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK |
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| Abstract: | In this paper, a new minimum time ship maneuvering method using neural network (NN) and nonlinear model predictive compensator is proposed. In this proposed method the NN is used for interpolating the precomputed minimum time solution for real time situations and the nonlinear dynamical model of a ship is used for compensating the control error caused by some modeling errors, disturbances and so on. The introduction of the nonlinear model into the online control system is inspired by the idea that since the nonlinear dynamical model of a ship has been constructed for the off-line numerical computation of the optimal solutions, it could also be used to enhance online control performance. In order to investigate this method, simulation studies and actual sea tests were carried out using a training ship Shioji Maru.The results showed that the system gives approximate solutions in a short computing time and good tracking performance in the actual sea trials. |
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