NDLI: Learning from direct adaptive neural control

Content Provider	IEEE Xplore Digital Library
Author	Cong Wang Hill, D.J.
Copyright Year	2004
Description	Author affiliation: Dept. of Electron. Eng., City Univ. of Hong Kong, China (Cong Wang; Hill, D.J.)
Abstract	This paper studies deterministic learning for nonlinear systems in the sense that an appropriately designed adaptive neural controller is shown to be capable of learning the unknown system dynamics while attempting to control the system. Following an earlier result for a simple class of systems, it is shown that this "deterministic learning" ability can still be implemented for direct adaptive neural control (ANC) of more general nonlinear systems. Specifically, for direct ANC of nonlinear systems in the strict-feedback form, accurate learning of system dynamics in certain desired control occur when all the NN inputs, including the system states and the intermediate variables, become periodic or periodic-like (recurrent) signals such that a partial persistence of excitation condition is satisfied. Further, it is also revealed that the direct ANC has advantages over the indirect ANC concerning the learning ability.
Starting Page	674
Ending Page	681
File Size	1442258
Page Count	8
File Format	PDF
ISBN	0780388739
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-20
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	ASCC
Subject Keyword	Learning systems Programmable control Radial basis function networks Nonlinear control systems Automatic control Control systems Nonlinear dynamical systems Adaptive control Nonlinear systems Convergence
Content Type	Text
Resource Type	Article

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