Yazar "Coban, Ramazan" seçeneğine göre listele

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    Identification of Disk Drive Systems using the Multifeedback-Layer Neural Network and the Particle Swarm Optimization Algorithm
    (IEEE, 2013) Aksu, Inayet Ozge; Coban, Ramazan
    In this study, the closed loop identification of the reader head position of a disk drive system is proposed by using the Multifeedback-Layer Neural Network. To identify the system, the connection weights of the Multifeedback-Layer Neural Network (MFLNN) are trained by the Particle Swarm Optimization (PSO) algorithm. Simulation results show the effectiveness of the proposed method.
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    Identification of disk drive systems using the Multifeedback-Layer Neural Network and the Particle Swarm Optimization algorithm
    (2013) Aksu, Inayet Ozge; Coban, Ramazan
    In this study, the closed loop identification of the reader head position of a disk drive system is proposed by using the Multifeedback-Layer Neural Network. To identify the system, the connection weights of the Multifeedback-Layer Neural Network (MFLNN) are trained by the Particle Swarm Optimization (PSO) algorithm. Simulation results show the effectiveness of the proposed method. © 2013 IEEE.
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    Neuro-Controller Design by Using the Multifeedback Layer Neural Network and the Particle Swarm Optimization
    (Univ Osijek, Tech Fac, 2018) Coban, Ramazan; Aksu, Inayet Ozge
    In the present study, a novel neuro-controller is suggested for hard disk drive (HDD) systems in addition to nonlinear dynamic systems using the Multifeedback-Layer Neural Network (MFLNN) proposed in recent years. In neuro-controller design problems, since the derivative based train methods such as the back-propagation and Levenberg-Marquart (LM) methods necessitate the reference values of the neural network's output or Jacobian of the dynamic system for the duration of the train, the connection weights of the MFLNN employed in the present work are updated using the Particle Swarm Optimization (PSO) algorithm that does not need such information. The PSO method is improved by some alterations to augment the performance of the standard PSO. First of all, this MFLNN-PSO controller is applied to different nonlinear dynamical systems. Afterwards, the proposed method is applied to a HDD as a real system. Simulation results demonstrate the effectiveness of the proposed controller on the control of dynamic and HDD systems.
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    Prediction of an Electromechanical System Parameters using the Particle Swarm Optimization Algorithm
    (IEEE, 2016) Aksu, Inayet Ozge; Coban, Ramazan
    In this study, the effect of the mechanical parts consisting of Tachometer/Gearbox, Digital Encoder, Input and Output Potentiometers, a brake disc, transmission belt, and some couplings which are directly connected to the shaft of a DC servo motor is investigated. The appropriate model of the DC servo motor can be achieved by modeling the effect of the mechanical parts correctly. The contribution of these mechanical parts is added to the mathematical model of the DC servo motor as new parameters. These parameters are estimated by using the Particle Swarm Optimization (PSO) algorithm since the convergence speed of the PSO is high. The result of the PSO algorithm is confirmed with experimental results. According to the experimental results, the obtained mathematical model including the effect of mechanical parts represents the real system more accurate.
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    Prediction of an electromechanical system parameters using the Particle Swarm Optimization algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2016) Aksu, Inayet Ozge; Coban, Ramazan
    In this study, the effect of the mechanical parts consisting of Tachometer/Gearbox, Digital Encoder, Input and Output Potentiometers, a brake disc, transmission belt, and some couplings which are directly connected to the shaft of a DC servo motor is investigated. The appropriate model of the DC servo motor can be achieved by modeling the effect of the mechanical parts correctly. The contribution of these mechanical parts is added to the mathematical model of the DC servo motor as new parameters. These parameters are estimated by using the Particle Swarm Optimization (PSO) algorithm since the convergence speed of the PSO is high. The result of the PSO algorithm is confirmed with experimental results. According to the experimental results, the obtained mathematical model including the effect of mechanical parts represents the real system more accurate. © 2016 IEEE.
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    Second Order Sliding Mode Control of MIMO Nonlinear Coupled Tank System
    (IEEE, 2018) Aksu, Inayet Ozge; Coban, Ramazan
    The aim of present work is to control the coupled tank system with Multi Input Multi Output (MIMO) structure by using the Sliding Mode Control (SMC) method. The most important disadvantage of the conventional SMC is chattering problem. The purpose of the presented study is to decrease the chattering effect. Two different controllers are proposed: Firstly, conventional SMC is designed. In the second step; to decrease the chattering problem in the conventional SMC, the same system is controlled by the second order SMC (SOSMC). The designed controllers have been applied to the coupled tank system, which has an important place in the industry. At the end of the study, by comparing the experimental results, it has been observed that the chattering in the control signal is significantly decreased.
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    Second order sliding mode control of MIMO nonlinear coupled tank system
    (Institute of Electrical and Electronics Engineers Inc., 2018) Aksu, Inayet Ozge; Coban, Ramazan
    The aim of present work is to control the coupled tank system with Multi Input Multi Output (MIMO) structure by using the Sliding Mode Control (SMC) method. The most important disadvantage of the conventional SMC is chattering problem. The purpose of the presented study is to decrease the chattering effect. Two different controllers are proposed: Firstly, conventional SMC is designed. In the second step; to decrease the chattering problem in the conventional SMC, the same system is controlled by the second order SMC (SOSMC). The designed controllers have been applied to the coupled tank system, which has an important place in the industry. At the end of the study, by comparing the experimental results, it has been observed that the chattering in the control signal is significantly decreased. © 2018 IEEE.
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    Sliding mode PI control with backstepping approach for MIMO nonlinear cross-coupled tank systems
    (Wiley, 2019) Aksu, Inayet Ozge; Coban, Ramazan
    The control of tank systems in industrial applications is an important issue for monitoring the chemical processes involved in the manufacture and delivery of product. The most important reason to control the tank systems is to keep the liquid level in the tanks constant and at the desired level for a specified period of time. In this study, the sliding mode control (SMC) with a repetitive approach called backstepping that is insensitive to uncertainties in system parameters and input disturbances is proposed and experimentally applied to a quadruple, cross-coupled, uncertain, nonlinear, and multiple-input/multiple-output tank system. A proportional-integral (PI) control is used to reduce the steady-state error caused by the parameter variations and external noises. The traditional way of introducing PI usually leads to sliding surfaces. In this paper, the PI action is introduced to the control signal. The proposed backstepping sliding mode PI control (BSMPIC) is applied to such a complex tank system for the first time. The experimental results are compared with those of the SMC, sliding mode PI control, and backstepping sliding mode control to see the effect of the proposed BSMPIC on the system. As a result of the comparison, it is observed that less overshoot and tracking error, better tracking performance, and faster rise time in the transient regime is obtained by the BSMPIC.
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    Sliding mode PI control with backstepping approach for MIMO nonlinear cross-coupled tank systems (vol 29, pg 1854, 2019)
    (Wiley, 2019) Aksu, Inayet Ozge; Coban, Ramazan
    [Abstract Not Available]
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    TRAINING THE MULTIFEEDBACK-LAYER NEURAL NETWORK USING THE PARTICLE SWARM OPTIMIZATION ALGORITHM
    (IEEE, 2013) Aksu, Inayet Ozge; Coban, Ramazan
    In this study, the Multifeedback-Layer Neural Network (MFLNN) weights are trained by the Particle Swarm Optimization (PSO). This method (MFLNN-PSO) is applied to two different problems to prove accomplishment of the study. Firstly, a chaotic time series prediction problem is used to test the MFLNN-PSO. Also, the method is used for identification of a non-linear dynamic system. This study shows that the MFLNN-PSO can be used for dynamic system identification as well as controller design.
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    Training the multifeedback-layer neural network using the Particle Swarm Optimization algorithm
    (IEEE Computer Society, 2013) Aksu, Inayet Ozge; Coban, Ramazan
    In this study, the Multifeedback-Layer Neural Network (MFLNN) weights are trained by the Particle Swarm Optimization (PSO). This method (MFLNN-PSO) is applied to two different problems to prove accomplishment of the study. Firstly, a chaotic time series prediction problem is used to test the MFLNN-PSO. Also, the method is used for identification of a non-linear dynamic system. This study shows that the MFLNN-PSO can be used for dynamic system identification as well as controller design. © 2013 IEEE.