Iterative learning double closed-loop structure for modeling and controller design of output stochastic distribution control systems

Zhou, Jinglin and Yue, Hong and Zhang, Jinfang and Wang, Hong (2014) Iterative learning double closed-loop structure for modeling and controller design of output stochastic distribution control systems. IEEE Transactions on Control Systems Technology, 22 (6). pp. 2261-2276. ISSN 1063-6536 (https://doi.org/10.1109/TCST.2014.2306452)

[thumbnail of Zhou-etal-TCST2014-iterative-learning-double-closed-loop-structure-for-modeling]
Preview
 Text. Filename: Zhou_etal_TCST2014_iterative_learning_double_closed_loop_structure_for_modeling.pdf
Accepted Author Manuscript
Download (1MB)| Preview

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.

ORCID iDs

Zhou, Jinglin, Yue, Hong ORCID logoORCID: https://orcid.org/0000-0003-2072-6223, Zhang, Jinfang and Wang, Hong;
  • Item type:Article
    ID code:48418
    Dates:
    Date
    Event
    1 November 2014
    Published
    3 March 2014
    Published Online
    Notes:(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:04 Jun 2014 12:50
    Last modified:11 Nov 2024 10:43
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/48418
CORE (COnnecting REpositories)