Browsing by Author "Soydemir, M. Ugur"

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    Searching Optimal Values of Identification and Controller Design Horizon Lengths and Regularization Parameters in NARMA Based Online Learning Controller Design
    (Institute of Electrical and Electronics Engineers Inc., 2019) Tugce Toprak; Savaş Şahin; Mehmet Uğur Soydemir; Parvin Bulucu; Aykut Kocaoǧlu; Cüneyt Güzeliş; Toprak, Tugce; Bulucu, Parvin; Kocaoglu, Aykut; Soydemir, M. Ugur; Guzelis, Cuneyt; Sahin, Savas
    This paper presents an analysis on searching the optimal values of the system identification and tracking window lengths and regularization parameter for the online learning NARMA controller algorithm. Both window lengths and regularization parameter are generally determined with exhaustive searches by researchers. Although the estimation of plant and controller parameters plays the essential role in online learning control algorithms using non-optimal values of the window lengths and regularization parameter may deteriorate badly the estimation and so the performance of the controller. In the paper the effects of the window lengths and the regularization parameter on the tracking performance of the NARMA based online learning controller are analyzed with a search method. The considered NARMA based online learning control method is performed on a rotary inverted pendulum model. While the effect of the regularization parameter is examined in the batch mode the effects of identification and tracking error window lengths are studied for the online mode of the controller learning algorithm. The developed search method can provide the optimum values of the plant identification and tracking horizon lengths and regularization parameter when a sufficiently large class of possible input output and reference signals are taken into account in the search. The presented study may be extended as future research in the direction of developing intelligent control systems by determining the horizon window lengths and regularization parameter in an automatic way with efficient learning algorithms. © 2020 Elsevier B.V. All rights reserved.