Article

Design of course-keeping controller for a ship based on backstepping and neural networks

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Due to the existence of uncertainties and the unknown time variant environmental disturbances for ship course nonlinear control system, the ship course adaptive neural network robust course-keeping controller is designed by combining the backstepping technique. The neural networks (NNs) are employed for the compensating of the nonlinear term of the nonlinear ship course-keeping control system. The designed adaptive laws are designed to estimate the weights of NNs and the bounds of unknown environmental disturbances. The first order commander are introduced to solve the problem of repeating differential operations in the traditional backstepping design method, which let the designed controller easier to implement in navigation practice and structure simplicity. Theoretically, it indicates that the proposed controller can track the setting course in arbitrary expected accuracy, while keeping all control signals in the ship course control closed-loop system are uniformly ultimately bounded. Finally, the training ship of Dalian Maritime University is taken for example; simulation results illustrated the effectiveness and the robustness of the proposed controller.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

ATC MR JIA d

Penerbit

Korea : E-navi., 2017

Deskripsi Fisik

8 p.

Bahasa

English

ISBN/ISSN

-

Klasifikasi

MR

Tipe Isi

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Tipe Media

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Tipe Pembawa

online resource

Edisi

-

Subjek

Ship course control
Adaptive neural network

Info Detail Spesifik

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Pernyataan Tanggungjawab

Na JIANG

Versi lain/terkait

Lampiran Berkas

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