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
Link To Document :
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