DocumentCode
2892814
Title
Anfis Applied to a Ship Autopilot Design
Author
Zhang, Xian-ku ; Jin, Yi-cheng ; Guo, Ge
Author_Institution
Lab. of Marine Simulation & Control, Dalian Maritime Univ.
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
2233
Lastpage
2236
Abstract
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
Keywords
PD control; closed loop systems; control engineering computing; control system synthesis; fuzzy control; fuzzy reasoning; fuzzy systems; learning (artificial intelligence); neurocontrollers; remotely operated vehicles; ships; ANFIS; Matlab; PD controller; PD course control system; adaptive neuro-fuzzy inference system; batch learning scheme; least square algorithm; nonlinear ship model; oil tanker; ship autopilot design; Control system synthesis; Fuzzy systems; Least squares methods; Marine vehicles; Mathematical model; Nonlinear control systems; PD control; Petroleum; Robust control; Training data; ANFIS; Autopilot for ships; Simulation;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258664
Filename
4028435
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