NDLI: A neurofuzzy approach for the anticipatory control of complex systems

Content Provider	IEEE Xplore Digital Library
Author	Xinqing, L. Tsoukalas, L.H. Uhrig, R.E.
Copyright Year	1996
Description	Author affiliation: Purdue Univ., West Lafayette, IN, USA (Xinqing, L.)
Abstract	Anticipatory control refers to system regulation based on information about anticipated future states. A preview of the future is typically obtained via predictive models and decisions about changes of state are made at the present taking into account the output of such models. Significant improvements in soft computing methodologies support the development of anticipatory control that integrates planning and control sequencing functions with feedback control algorithms. We present a neurofuzzy approach for anticipatory control using radial basis neural models and fuzzy rules and demonstrate it through nuclear reactor regulation. The control method does not require knowledge of plant parameters or structure. It is model-independent, and thus may be applied to other nonlinear time-varying dynamic systems. Simulation results show that the neurofuzzy anticipatory control approach improves tracking performance and smoothness and may be quite insensitive to noise. The results suggest that it is a flexible and powerful approach that can easily be extended to other problems, and it is compatible with other techniques. The relevance of the approach to the control of large complex systems is also discussed.
Starting Page	587
Ending Page	593
File Size	595063
Page Count	7
File Format	PDF
ISBN	0780336453
DOI	10.1109/FUZZY.1996.551806
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-09-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Control systems Power system modeling Predictive models Nuclear facility regulation Safety Power generation Inductors Laboratories Feedback control Fuzzy control
Content Type	Text
Resource Type	Article

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