Anfis Applied to a Ship Autopilot Design

Using a batch learning scheme and a hybrid learning rule, i.e. BP algorithm is applied to the learning of premise parameters, while least square algorithm to the learning of consequent parameters, an ANFIS system for ship autopilot with two inputs and one output, three fuzzy zones, nine fuzzy rules...

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Bibliographic Details
Published in2006 International Conference on Machine Learning and Cybernetics pp. 2233 - 2236
Main Authors Xian-Ku Zhang, Yi-Cheng Jin, Ge Guo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2006
Subjects
ANFIS
Autopilot for ships
Control system synthesis
Fuzzy systems
Least squares methods
Marine vehicles
Mathematical model
Nonlinear control systems
PD control
Petroleum
Robust control
Simulation
Training data
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Summary:Using a batch learning scheme and a hybrid learning rule, i.e. BP algorithm is applied to the learning of premise parameters, while least square algorithm to the learning of consequent parameters, an ANFIS system for ship autopilot with two inputs and one output, three fuzzy zones, nine fuzzy rules is trained. Training data come from a PD course control system, then the trained ANFIS autopilot controls an oil tanker that is described by a nonlinear ship model. The simulating results by Matlab indicate that the performance of ANFIS controller is similar to that of the training PD controller with good robustness
ISBN:1424400619
9781424400614
ISSN:2160-133X
DOI:10.1109/ICMLC.2006.258664