NDLI: Iterative Learning Double Closed-Loop Structure for Modeling and Controller Design of Output StochAstic Distribution Control Systems

Content Provider	IEEE Xplore Digital Library
Author	Jinglin Zhou Hong Yue Jinfang Zhang Hong Wang
Copyright Year	1993
Abstract	Stochastic distribution control (SDC) systems are known to have the 2-D characteristics regarding time and probability space of a random variables, respectively. A double closed-loop structure, which includes iterative learning modeling (ILM) and iterative learning control (ILC), is proposed for non-Gaussian SDC systems. The ILM is arranged in the outer loop, which takes a longer period for each cycle termed as a BATCH. Each BATCH is divided into a modeling period and a number of control intervals, called batches, being arranged in the inner loop for ILC. The output probability density functions (PDFs) of the system are approximated by a radial basis function neural network (RBFNN) model, whose parameters are updated via ILM in each BATCH. Based on the RBFNN approximation of the output PDF, a state-space model is constructed by employing the subspace parameter estimation method. An IL optimal controller is then designed by decreasing the PDF tracking errors from batch to batch. Model simulations are carried out on a forth-order numerical example to examine the effectiveness of the proposed algorithm. To further assess its application feasibility, a flame shape distribution control simulation platform for a combustion process in a coal-fired gate boiler system is constructed by integrating WinCC interface, MATLAB simulation programs, and OPC communication together. The simulation study over this industrial simulation platform shows that the output PDF tracking performance can be efficiently achieved by this double closed-loop iterative learning strategy.
Sponsorship	IEEE Control Systems Society
Starting Page	2261
Ending Page	2276
Page Count	16
File Size	3590389
File Format	PDF
ISSN	10636536
Volume Number	22
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Probability density function Tuning Iterative methods Radial basis function networks temperature field distribution control. Iterative learning (IL) optimal tracking control probability density function (PDF) radial basis function neural network (RBFNN) stochastic distribution control (SDC) subspace identification temperature field distribution control
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Electrical and Electronic Engineering

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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