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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:GUO Bing-jie,LI Yue-ming,Wan Lei.T-S norm FNN controller based on hybrid learning algorithm[J].Journal of Harbin Institute Of Technology(New Series),2011,18(3):27-32.DOI:10.11916/j.issn.1005-9113.2011.03.006.
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T-S norm FNN controller based on hybrid learning algorithm
Author NameAffiliation
GUO Bing-jie College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China 
LI Yue-ming College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China 
Wan Lei College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China 
Abstract:
Aiming at the problems that fuzzy neural network controller has heavy computation and lag,a T-S norm Fuzzy Neural Network Control based on hybrid learning algorithm was proposed.Immune genetic algorithm (IGA) was used to optimize the parameters of membership functions (MFs) off line,and the neural network was used to adjust the parameters of MFs on line to enhance the response of the controller.Moreover,the latter network was used to adjust the fuzzy rules automatically to reduce the computation of the neural network and improve the robustness and adaptability of the controller,so that the controller can work well ever when the underwater vehicle works in hostile ocean environment.Finally,experiments were carried on " XX" mini autonomous underwater vehicle (min-AUV) in tank.The results showed that this controller has great improvement in response and overshoot,compared with the traditional controllers.
Key words:  T-S norm fuzzy neural network  Underwater vehicles  Immune genetic algorithm  Hybrid learning algorithm
DOI：10.11916/j.issn.1005-9113.2011.03.006
Clc Number:TP273
Fund:

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