Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle

Jeen Shing Wang, C. S.George Lee

研究成果: Conference article同行評審

16 引文 斯高帕斯(Scopus)

摘要

This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy control as a feedforward control as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then utilized as a feedforward controller to compute the nominal torque of the AUV along a desired trajectory. The PD feedback controller computes the error torque to minimize the system error along the desired trajectory. This error torque also provides an error signal for online updating the parameters in the recurrent neuro-fuzzy control to adapt in a changing environment. A systematic self-adaptive learning algorithm, consisting of a mapping-constrained agglomerative clustering algorithm for the structure learning and a recursive recurrent learning algorithm for the parameter learning, has been developed to construct the recurrent neuro-fuzzy system to model the inverse dynamics of an AUV with fast learning convergence. Computer simulations of the proposed recurrent neuro-fuzzy control scheme and its performance comparison with an adaptive controller have been conducted to validate the effectiveness of the proposed approach.

原文English
頁(從 - 到)1095-1100
頁數6
期刊Proceedings - IEEE International Conference on Robotics and Automation
2
出版狀態Published - 2002 一月 1
事件2002 IEEE International Conference on Robotics and Automation - Washington, DC, United States
持續時間: 2002 五月 112002 五月 15

All Science Journal Classification (ASJC) codes

  • 軟體
  • 控制與系統工程
  • 人工智慧
  • 電氣與電子工程

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