NDLI: Stability analysis of robot manipulators subject to feedforward neural network controllers

Content Provider	IEEE Xplore Digital Library
Author	Abdalla, A. Lilong Cai
Copyright Year	1994
Description	Author affiliation: Dept. of Mech. Eng., Columbia Univ., New York, NY, USA (Abdalla, A.)
Abstract	In this paper a new approach, based on Lyapunov's direct method, for the design of multilayer feedforward neural network (NN) controllers for uncertain robot manipulators is presented. Furthermore, two different feedforward NN controllers are proposed to stabilize uncertain robot manipulators. The first NN controller is shown to render the closed-loop system globally practically stable while the second NN controller guarantees global uniform asymptotic stability of the system. The new approach ensures stability of the control system without using any learning or adaptive algorithm. Moreover, using nonlinear control theory, the proposed approach to neural networks provides sufficient conditions for determining the number of hidden layers, the dimension of neurons, the architecture of the neural network and the weights among the layers in order to guarantee stability of the system. The theoretical results are illustrated by application to a two-link manipulator.
Starting Page	2588
Ending Page	2593
File Size	551056
Page Count	6
File Format	PDF
ISBN	0780321294
DOI	10.1109/ICSMC.1994.400261
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-10-02
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stability analysis Manipulators Neural networks Control systems Multi-layer neural network Design methodology Feedforward neural networks Robot control Asymptotic stability Adaptive algorithm
Content Type	Text
Resource Type	Article

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