An Internal/Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for dynamic system identification

2010 ◽  
Author(s):  
Yang-Yin Lin ◽  
Jyh-Yeong Chang ◽  
Chin-Teng Lin
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Dynamic System ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Recurrent Type

scholarly journals Autonomous underwater vehicle precise motion control for target following with model uncertainty

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141771980 ◽  
Author(s):  
Huang Hai ◽  
Zhang Guocheng ◽  
Qing Hongde ◽  
Zhou Zexing
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
External Disturbance ◽  
Underwater Vehicle ◽  
Parameter Uncertainties ◽  
Fuzzy Neural ◽  
Recurrent Type

Target following plays an important role in oceanic detection and target capturing for autonomous underwater vehicles. Due to the model nonlinearity and external disturbance, the dynamic model of a portable autonomous underwater vehicle was usually established with parameter uncertainties. In this article, a petri-based recurrent type 2 fuzzy neural network has been built to approximate the unknown autonomous underwater vehicle dynamics. The type 2 fuzzy logic system has been applied to the network to improve the approximation accuracy for systematic nonlinearity, and the petri layer in the network can improve estimation speed and reduce energy consumption. A petri-based recurrent type 2 fuzzy neural network–based adaptive robust controller has been proposed for target tracking. In the offshore experiments, the proposed controller has not only realized stable position and pose control but also successfully followed mobile target on the surface. In the tank underwater experiments, the pipeline target has been successfully followed to further verify the controller performance.

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