NDLI: Generalized predictive control method for a class of nonlinear systems using ANFIS and multiple models

Content Provider	IEEE Xplore Digital Library
Author	Yajun Zhang Tianyou Chai Hong Wang Jun Fu
Copyright Year	2010
Description	Author affiliation: Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, China (Yajun Zhang; Tianyou Chai; Hong Wang; Jun Fu)
Abstract	This paper develops a generalized predictive control method using adaptive-network-based fuzzy inference system (ANFIS) and multiple models for a class of uncertain discrete-time nonlinear systems with unstable zero-dynamics. The proposed method is composed of a linear robust generalized predictive controller, an ANFIS-based nonlinear generalized predictive controller, and a switching mechanism using multiple models technique. The method in this paper has the following three features compared with the results available in the literature. First, this method relaxes the global boundedness assumption of the unmodelled dynamics in the literature, and thus widens its ranges of applications. Secondly, the ANFIS is used to estimate and compensate for the unmodeled dynamics adaptively in the nonlinear generalized predictive controller design, which successfully tackles the relatively low convergence rate of neural networks and avoids the possibility that the network becomes trapped in local minima. Thirdly, to guarantee the universal approximation property of ANFIS, a “one-to-one mapping” is adapted. A simulation example is exploited to illustrate the effectiveness of the proposed method.
Starting Page	4600
Ending Page	4605
File Size	338382
Page Count	6
File Format	PDF
ISBN	9781424477456
ISSN	07431546
e-ISBN	9781424477463
DOI	10.1109/CDC.2010.5717046
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Switches Artificial neural networks Approximation methods Predictive models Nonlinear dynamical systems
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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