Browsing by Author "Bulucu, Parvin"

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Citation - Scopus: 3
Data Dependent Stable Robust Adaptive Controller Design for Altitude Control of Quadrotor Model
(Institute of Electrical and Electronics Engineers Inc., 2018) Mehmet Uğur Soydemir; Ishak Alkus; Parvin Bulucu; Aykut Kocaoǧlu; Cüneyt Güzeliş; Savaş Şahin; Bulucu, Parvin; Kocaoglu, Aykut; Soydemir, Mehmet Ugur; Guzelis, Cuneyt; Alkus, Ishak; Sahin, Savas; D. Maga , A. Stefek , T. Brezina
This paper presents Nonlinear Auto Regressive Moving Average (NARMA) based stable robust adaptive controller design. Both the plant and the closed-loop controller systems are modelled by the proposed NARMA based input-output models. During online supervised learning for the system identification and the controller design phases input-output data obtained from the simulated plant are evaluated in suitable parameter regions providing Schur stability for the overall closed-loop system. At the same time ϵ-insentive loss function and ℓ1 norm are used for providing robustness for proposed system identification and adaptive controller parameters. The proposed controller design method is performed on quadrotor model which is an unmanned air vehicle benchmark plant. The performance results are compared against proportional derivative controller. © 2023 Elsevier B.V. All rights reserved.
Citation - Scopus: 3
Online learning of stable robust adaptive controllers design based on data-dependent feedback linearization with application to rotary inverted pendulum
(Springer Science and Business Media Deutschland GmbH, 2024) Mehmet Uğur Soydemir; Savaş Şahin; Aykut Kocaoǧlu; Parvin Bulucu; Cüneyt Güzeliş; Kocaoğlu, Aykut; Bulucu, Parvin; Soydemir, Mehmet Uğur; Güzeliş, Cüneyt; Şahin, Savaş
This study introduces an online (supervised) learning method to design nonlinear auto-regressive moving average (NARMA) controllers for feedback-linearized nonlinear single-input single-output (SISO) systems. The algorithm ensures Schur stability of the overall closed-loop system and provides adaptiveness and robustness for the NARMA controllers. The first stage of the method derives in a data-dependent way a feedback-linearized model of the nonlinear plant by using its input and output sample pairs. The method’s second stage which constitutes the novel part of the presented study builds up an online learning scheme for the linear auto-regressive moving average (ARMA) controller based on an already learned feedback-linearized model of the nonlinear plant. During online supervised learning ARMA parameters of the feedback-linearized SISO plant model and the closed-loop ARMA model are computed by minimizing the plant identification and the closed-loop system tracking errors. Both errors are defined as ℓ1ε namely ε-insensitive loss functions that provide NARMA controller the robustness against noise and outliers. The proposed online learning control algorithm is applied to a rotary inverted pendulum model and to a real rotary inverted pendulum setup. The tracking performance of the developed controller is compared with those of the linear quadratic regulator and coupled sliding mode controller in terms of mean square error. © 2024 Elsevier B.V. All rights reserved.
Performance analysis of stable adaptive NARMA controller scheme for furuta pendulum
(Institute of Electrical and Electronics Engineers Inc., 2019) Parvin Bulucu; Mehmet Uğur Soydemir; Savaş Şahin; Aykut Kocaoǧlu; Cüneyt Güzeliş; Bulucu, Parvin; Kocaoglu, Aykut; Soydemir, Mehmet Ugur; Guzelis, Cuneyt; Sahin, Sava; R.-E. Precup
This paper presents a novel stable adaptive controller scheme for Furuta Pendulum via nonlinear auto-regressive moving-average based plant identification. During online learning for the developed controller input-output data obtained from the rotary inverted pendulum model used to update the parameters of the NARMA controller while ensuring Schur stability for the overall closed-loop control system. The parameters of the plant model and the introduced controller are computed by minimizing the identification and output tracking errors respectively both of them are absolute loss functions modified with a regularization parameter. The proposed adaptive controller is tested on Furuta pendulum model and its performance is compared with the performances of proportional integral derivative controller and model reference adaptive controller. © 2020 Elsevier B.V. All rights reserved.
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.