Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control

Jeen Shing Wang, C. S.George Lee, Junku Yuh

Research output: Contribution to journalConference articlepeer-review

20 Citations (Scopus)

Abstract

This paper presents a systematic approach for developing a concise self-adaptive neuro-fuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning the control knowledge for autonomous underwater vehicles (AUV). The multi-layered structure of SANFIS incorporates fuzzy basis functions for better function approximations. Based on the need of different applications, we investigate three SANFIS structures with three different types of fuzzy IF-THEN-rule-based models and cast the rule formation problem as a clustering problem. A recursive least squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the learning process. Thus, incorporating an on-line clustering technique, a fast hybrid learning procedure and rule examination, the SANFIS is capable of self-organizing and self-adapting its internal structure for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.

Original languageEnglish
Pages (from-to)3861-3866
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume4
Publication statusPublished - 2000 Dec 3
EventICRA 2000: IEEE International Conference on Robotics and Automation - San Francisco, CA, USA
Duration: 2000 Apr 242000 Apr 28

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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