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

Jeen Shing Wang, C. S.George Lee

Research output: Contribution to journalConference article

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1095-1100
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Volume2
Publication statusPublished - 2002 Jan 1
Event2002 IEEE International Conference on Robotics and Automation - Washington, DC, United States
Duration: 2002 May 112002 May 15

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Autonomous underwater vehicles
Fuzzy control
Controllers
Torque
Fuzzy systems
Learning algorithms
Trajectories
Derivatives
Feedback
Feedforward control
Adaptive algorithms
Clustering algorithms
Computer simulation

All Science Journal Classification (ASJC) codes

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

Cite this

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Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle. / Wang, Jeen Shing; Lee, C. S.George.

In: Proceedings - IEEE International Conference on Robotics and Automation, Vol. 2, 01.01.2002, p. 1095-1100.

Research output: Contribution to journalConference article

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