Browsing by Author "Sahin, Savas"

Now showing 1 - 8 of 8

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 - WoS: 7
Citation - Scopus: 8
Design of microcontroller-based decentralized controller board to drive chiller systems using PID and fuzzy logic algorithms
(SAGE Publications Ltd, 2020) Yalcin Yalcin Isler; Savaş Şahin; Orhan Ekren; Cüneyt Güzeliş; Isler, Yalcin; Ekren, Orhan; Guzelis, Cuneyt; Sahin, Savas
This study deals with designing a decentralized multi-input multi-output controller board based on a low-cost microcontroller which drives both parts of variable-speed scroll compressor and electronic-type expansion valve simultaneously in a chiller system. This study aims to show the applicability of commercial low-cost microcontroller to increase the efficiency of the chiller system having variable-speed scroll compressor and electronic-type expansion valve with a new electronic card. Moreover the refrigerant system proposed in this study provides the compactness mobility and flexibility and also a decrease in the controller unit’s budget. The study was tested on a chiller system that consists of an air-cooled condenser a variable-speed scroll compressor and a stepper driven electronic-type expansion valve. The R134a was used as a refrigerant fluid and its flow was controlled by electronic-type expansion valve in this setup. Both variable-speed scroll compressor and electronic-type expansion valve were driven by the proposed hardware using either proportional integral derivative or fuzzy logic controller which defines four distinct controller modes. The experimental results show that fuzzy logic controlled electronic-type expansion valve and proportional integral derivative controlled variable-speed scroll compressor mode give more robustness by considering the response time. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
Effect of Chaotic Mixing on the Rheological Characterization of Mayonnaise
(IEEE, 2017) Seda Genc; Tolga Tugay Izmir; Ruhan Askin Uzel; Gokhan Demirkiran; Savas Sahin; Cuneyt Guzelis; Uzel, Ruhan Askin; Izmir, Tolga Tugay; Demirkiran, Gokhan; Guzelis, Cuneyt; Genc, Seda; Sahin, Savas
In this study a new mixing method for mayonnaise was developed. A chaotic hand mixer was designed. The speed of the mixer rotor was chaotically changed with the proposed method. Performance of the mixed mayonnaise was evaluated by a rheological characterization method. The results showed that the proposed chaotic mixing for mayonnaise has a better performance in terms of energy efficiency than conventional mixed ones indicating potential use in industry or as home appliance.
Investigation of Chaotic Mixing Performance on Characteristic Properties of Cake Batter
(IEEE, 2019) Ruhan Askin Uzel; Tolga Tugay Izmir; Gokhan Demirkiran; Savas Sahin; Cuneyt Guzelis; Uzel, Ruhan Askin; Izmir, Tolga Tugay; Demirkiran, Gokhan; Guzelis, Cuneyt; Tugay Izmir, Tolga; Sahin, Savas
Chaotification is the process of making an originally non chaotic system being chaotic by applying a suitable control input. The aim of the study was to create a chaotic mixing mechanism using a kitchen type mixer and to test its performance on the quality characteristics of cake batter material. A prototype mixer that works originally with conventional method has been realized with hardware and software changes on a commercial kitchen type mixer. The results obtained at the end of the study showed that the kitchen type mixer was able to successfully switch from the classical mixing mode to the chaotic mixing mode and this mixing mode positively affected the structural and sensory characteristics of the cake batter samples.
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.
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.
Citation - WoS: 13
Citation - Scopus: 14
Spatiotemporal chaotification of delta robot mixer for homogeneous graphene nanocomposite dispersing
(ELSEVIER, 2020) Savas Sahin; Ali Emre Kavur; Sibel Demiroglu Mustafov; Ozgur Seydibeyoglu; Ozgun Baser; Yalcin Isler; Cuneyt Guzelis; Seydibeyoglu, Ozgur; Kavur, Ali Emre; Baser, Ozgun; Guzelis, Cuneyt; Sahin, Savas; Isler, Yalcin; Demiroglu Mustafov, Sibel; Mustafov, Sibel Demiroglu
This paper presents the design implementation and polymer nanocomposite mixing application of a robust spatiotemporal chaotic delta robot. Blending fluids efficiently is a vital process for the preparation of graphene nanocomposite mixing. The most commonly used mixing materials are polymeric materials that need to be blended in non-Newtonian fluids. To achieve a superior blending performance over the conventional ones it is used two different chaotification mechanisms for the realization of the spatiotemporal chaotic delta robot mixer system. One of them is for the chaotification of the mixer propeller while the second one is for the chaotification of the three-dimensional position of the endpoint of the delta robot. The model-based robust chaotification scheme based on sliding mode control is applied to chaotify the speed of the delta robot-mixer via dynamical state-feedback chaotification method. The chaotification of 3D position of the mixer is realized in a feedforward way by producing chaotic input signals. The implemented robust chaotic delta robot mixer exploits the efficacy of chaotic mixing in obtaining homogeneity in the mixture with less operation time and hence reduced electrical energy consumption. In these performance evaluations energy consumption and material characterization which are measured by reliable material characterization methods such as X-ray diffraction Fourier-transform-infrared spectroscopy water contact angle dynamical mechanical analysis atomic force microscopy Raman and field emission-scanning electron microscope analyses are used as criteria. The obtained results show that for the delta robot the proposed chaotic-speed together with 3D chaotic-movement operation mode provides a better mixing performance than other mixing operation modes. (C) 2020 Elsevier B.V. All rights reserved.