NDLI: The application of fuzzy neural network in ship course control system

Content Provider	IEEE Xplore Digital Library
Author	Jin Zhao Huajun Zhang
Copyright Year	2010
Description	Author affiliation: Department of Technology Center, Dongfeng Motor Corporation, Wuhan, China (Huajun Zhang) \|\| Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China (Jin Zhao)
Abstract	when ship docks at harbor or other sea area, both the robustness and dynamic properties of course control system are required strictly. Because the model parameters are relative to the speed and load of the ship, it is difficult to design a good controller based on the ship model. This paper combines internal model control with fuzzy neural network to design a course system. First, it designs a zero steady-state error controller which ensures robustness of the course control system relay on inner model. For the zero steady-state error controller, it is necessary to reduce the dynamic properties of the control system to ensure the robustness. Thus, this paper uses fuzzy neural network to adjust the pole sites of the closed control system based on the ship course and course change rate. It designs a new structure of fuzzy neural network which uses neural network to represent the fuzzy rules. According to expert experiences, it also gives out the weights computation method of fuzzy neural adjuster. At last, the hybrid course control system is applied in a actual ship and the course response curves indicate that the course control system possess good robustness and dynamic properties.
Starting Page	752
Ending Page	756
File Size	282436
Page Count	5
File Format	PDF
ISBN	9781424459315
e-ISBN	9781424459346
DOI	10.1109/FSKD.2010.5569361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control fuzzy neural network Computational modeling Control systems ship Robustness Mathematical model internal model control Marine vehicles Equations course control
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

The application of fuzzy neural network in ship course control system

Model-based controller for ship track-keeping using neural network

A fuzzy variable structure controller and its application on ship course nonlinear system

Nonlinear control of hydrofoil catamaran course in three DOF

Research on fault diagnosis for ship course control system

Adaptive RBF Neural Network Sliding Mode Control for Ship Course Control System

Design of Ship Controller and Ship Model Based on Neural Network Identification Structures

Dynamic Fuzzy Neural Intelligent Control for ship course tracking

Decentralized variable structure control research of ship course/roll

The application of fuzzy neural network in ship course control system

Similar Documents

The application of fuzzy neural network in ship course control system

Model-based controller for ship track-keeping using neural network

A fuzzy variable structure controller and its application on ship course nonlinear system

Nonlinear control of hydrofoil catamaran course in three DOF

Research on fault diagnosis for ship course control system

Adaptive RBF Neural Network Sliding Mode Control for Ship Course Control System

Design of Ship Controller and Ship Model Based on Neural Network Identification Structures

Dynamic Fuzzy Neural Intelligent Control for ship course tracking

Decentralized variable structure control research of ship course/roll

The application of fuzzy neural network in ship course control system