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

Content Provider	IEEE Xplore Digital Library
Author	Jeen-Shing Wang Lee, C.S.G.
Copyright Year	2002
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA (Jeen-Shing Wang; Lee, C.S.G.)
Abstract	This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy 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 it 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, was 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 were conducted to validate the effectiveness of the proposed approach.
Sponsorship	IEEE Robotics & Autom. Soc
Starting Page	1095
Ending Page	1100
File Size	602490
Page Count	6
File Format	PDF
ISBN	0780372727
DOI	10.1109/ROBOT.2002.1014689
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proportional control Error correction Adaptive control Clustering algorithms PD control Fuzzy neural networks Inverse problems Vehicle dynamics Underwater vehicles Torque control
Content Type	Text
Resource Type	Article

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