NDLI: Adaptive data driven controller for nonlinear systems

Content Provider	IEEE Xplore Digital Library
Author	Dong Na
Copyright Year	2014
Description	Author affiliation: Tianjin Key Lab. of Process Meas. & Control, Tianjin Univ., Tianjin, China (Dong Na)
Abstract	A novel adaptive data driven control strategy is proposed for general discrete nonlinear systems. The parametric estimation is designed to be adaptive, which greatly improves the control ability. During the control process, the Simultaneous Perturbation Stochastic Approximation (SPSA) method is used to do the estimation of the control parameters, and the controller is fixed as a neural network here. In this paper, the proposed control strategy is applied to solve nonlinear tracking problems for discrete-time nonlinear systems, as well as nonlinear near-optimal control problems. The traditional model-free control strategy is introduced for comparison, and the feasibility and effectiveness of the proposed adaptive data driven control strategy is well demonstrated through simulation comparison results.
Starting Page	8806
Ending Page	8811
File Size	288043
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896481
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Adaptation models Adaptive data driven control Adaptive systems Neural networks Stochastic processes Simultaneous Perturbation Stochastic Approximation Electronic mail Approximation methods Nonlinear systems Discrete nonlinear systems
Content Type	Text
Resource Type	Article

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