Browsing by Author "Kocaoglu, Aykut"

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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: 1
Learning Feedback Linearization Based Stable Robust Adaptive NARMA Controller Design for Rotary Inverted Pendulum
(Institute of Electrical and Electronics Engineers Inc., 2019) Mehmet Uğur Soydemir; Savaş Şahin; Parvin Bulucu; Aykut Kocaoǧlu; Cüneyt Güzeliş; Bulucu, Panin; Kocaoglu, Aykut; Soydemir, Mchmet Ugur; Guzelis, Cuneyt; Sailin, Savas; Sahin, Savas
This paper presents a Learning Feedback Linearization (LFL) based Nonlinear Auto-Regressive Moving Average (NARMA) controller design for a ROTary inverted PENdulum (ROTPEN) plant. The proposed NARMA controller comprises of a linear controller and an LFL block. The LFL block concatenated with the nonlinear plant constitutes a linear closed loop system so that linear control is applicable. An online learning algorithm is used for the data-dependent identification of the linearized plant and then for the data-dependent design of the linear part of the NARMA controller. The identification of the linearized plant starts with the determination of the LFL block in a supervised way by exploiting the input and the corresponding state data obtained from the nonlinear plant. The linearized plant is then identified as an ARMA model by the data generated with the combination of the already learned LFL block and the nonlinear plant. Robustness of the linearized system model is obtained by employing the ϵ-insensitive loss function ℓ1 ϵ(••) as the identification error of the linearized system. The Schur stability of the overall closed loop system is ensured by the linear inequality constraints imposed in the minimization of the ℓ1 ϵ(••) tracking error for determining the linear controller parameters. The proposed LFL based NARMA controller is tested on ROTPEN model and its performance is compared with the Proportional-Derivative controller and Hammerstein based NARMA adaptive controller. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 6
Citation - Scopus: 8
Learning Stable Robust Adaptive NARMA Controller for UAV and Its Application to Twin Rotor MIMO Systems
(SPRINGER, 2020) Parvin Bulucul; Mehmet Ugur Soydemir; Savas Sahin; Aykut Kocaoglu; Cuneyt Guzelis; Kocaoglu, Aykut; Bulucu, Parvın; Soydemir, Mehmet Ugur; Guzelis, Cuneyt; Bulucul, Parvm; Sahin, Savas
This study presents a nonlinear auto-regressive moving average (NARMA) based online learning controller algorithm providing adaptability robustness and the closed loop system stability. Both the controller and the plant are identified by the proposed NARMA based input-output models of Wiener and Hammerstein types respectively. In order to design the NARMA controller not only the plant but also the closed loop system identification data are obtained from the controlled plant during the online supervised learning mode. The overall closed loop model parameters are determined in suitable parameter regions to provide Schur stability. The identification and controller parameters are calculated by minimizing the einsensitive error functions. The proposed controller performances are not only tested on two simulated models such as the quadrotor and twin rotor MIMO system (TRMS) models but also applied to the real TRMS with having severe cross-coupling effect between pitch and yaw. The tracking error performances of the proposed controller are observed better compared to the conventional adaptive and proportional-integral-derivative controllers in terms of the mean squared error integral squared error and integral absolute error. The most noticeable superiority of the developed NARMA controller over its linear counterpart namely the adaptive auto-regressive moving average (ARMA) controller is observed on the TRMS such that the NARMA controller shows a good tracking performance not only for the simulated TRMS model but also the real TRMS. On the other hand it is seen that the adaptive ARMA is incapable of producing feasible control inputs for the real TRMS whereas it works well for the simulated TRMS model.
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.