NDLI: A discrete-time multivariable neuro-adaptive control for nonlinear unknown dynamic systems

Content Provider	IEEE Xplore Digital Library
Author	Chih-Lyang Hwang Ye-Hwa Chen Ching-Hung Lin
Copyright Year	1999
Description	Author affiliation: Dept. of Mech. Eng., Tatung Inst. of Technol., Taipei, Taiwan (Chih-Lyang Hwang)
Abstract	The controlled system is assumed to be a nonlinear matrix gain before a linear discrete-time multivariable dynamic system. A forward control based on a nominal system is employed to cancel the system nonlinear matrix gain and to track the desired trajectory. Because the remaining uncertainties are dynamic, a recurrent neural network with a compensation of the upper bound of its residue is applied to model the remaining uncertainties in a compact subset /spl Omega/. The linearly parameterized connection weight for the function approximation error of the proposed network is given. An e-modification updating law with projection for the weight matrix is employed to guarantee its boundedness and the stability of the network without the requirement of persistent excitation. Then a discrete-time multivariable neuro-adaptive variable structure control is designed to improve the system performances.
Starting Page	1102
Ending Page	1107
File Size	563952
Page Count	6
File Format	PDF
ISBN	0780357310
ISSN	1062922X
DOI	10.1109/ICSMC.1999.812564
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-10-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Control systems Nonlinear control systems Nonlinear dynamical systems Uncertainty Gain Trajectory Recurrent neural networks Upper bound Function approximation Stability
Content Type	Text
Resource Type	Article

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