Unmanned Underwater Vehicles Fault Identification and Fault-Tolerant Control Method Based on FCA-CMAC Neural Networks, Applied on an Actuated Vehicle

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• 作者：Qian Liu (1)
  Daqi Zhu (12) zdq367@yahoo.com.cn
  Simon X. Yang (2) syang@uoguelph.ca
• 关键词：Fault diagnosis &#8211 ; Fault ; tolerant control &#8211 ; Abrupt faults &#8211 ; Underwater vehicles &#8211 ; Thruster
• 刊名：Journal of Intelligent and Robotic Systems
• 出版年：2012
• 出版时间：June 2012
• 年：2012
• 卷：66
• 期：4
• 页码：463-475
• 全文大小：1,016.3 KB
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• 作者单位：1. Laboratory of Underwater Vehicles and Intelligent Systems, Shanghai Maritime University, Pudong Avenue 1550, Shanghai, 200135 China2. Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G2W1, Canada
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Electronic and Computer Engineering
  Artificial Intelligence and Robotics
  Mechanical Engineering
  
• 出版者：Springer Netherlands
• ISSN：1573-0409

文摘

A novel fault diagnosis and accommodation method for unmanned underwater vehicles thruster is presented in this paper. FCA-CMAC (Credit Assignment-based Fuzzy Cerebellar Model Articulation Controllers) neural network is used to realize the fault identification for thruster continuous and uncertain jammed fault situation. A reconstruction algorithm based on weighted pseudo-inverse is used to find the available solution of the control allocation problem. To illustrate effective of the proposed method, two simulation examples of multi-uncertain abrupt faults are given in the paper.