NDLI: An identification and adaptive control scheme using fuzzy parameterized linear filters

Content Provider	IEEE Xplore Digital Library
Author	Papp, Z.
Copyright Year	1998
Description	Author affiliation: TNO Inst. of Appl. Phys., Delft, Netherlands (Papp, Z.)
Abstract	Presents a nonlinear (fuzzy) control structure enhanced with supervised learning and/or adaption. Availability of at least a partial process model is assumed. A nonlinear process identification procedure is used to complete the partial model. Based on the identification model the system sensitivity model is derived which guides the training process to keep the training time (the number of observations) at minimum. The identification model introduced consists of a linear dynamical section (filter) and a nonlinear zero-memory section (implemented by a fuzzy mapping). Only the filter section is on the primary signal path. The nonlinear mapping delivers the filter parameters (depending on the system input and state). An adaption procedure is introduced, which tunes the nonlinear mapping (e.g. membership function parameters) to minimize identification error. The process identification and the controller tuning can run parallel, in this way the online adaption of the controller can be realized in a straightforward way. The scheme proposed can incorporate a priori knowledge on two levels (system topology and fuzzy rule set). The most distinctive features of the scheme are that it directly supports controller design and/or (online) tuning in various ways and results in a dynamical system, which is relatively easy to analyze using well established traditional analysis methods. The identification and control scheme can readily incorporate other types of sufficiently smooth parameterized nonlinearities (e.g. neural networks or domain specific specialized nonlinear mappings).
Starting Page	468
Ending Page	473
File Size	630964
Page Count	6
File Format	PDF
ISBN	078034863X
ISSN	10987584
DOI	10.1109/FUZZY.1998.687531
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-05-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive control Fuzzy control Supervised learning Nonlinear filters Topology Fuzzy sets Fuzzy systems Control systems Control nonlinearities Neural networks
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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