Browsing by Author "Dumlu, A."

Now showing 1 - 11 of 11

Design and Control of Intelligent Cooling System for IC Engine
(Institute of Electrical and Electronics Engineers Inc., 2017) Kaleli, A.; Kaltakkıran, G.; Dumlu, A.; Ayten, K.K.
The engine cooling systems in the commercial vehicles have not developed as the other control systems despite the technological improvements. Additionally, researchers in the automotive industry and the universities have focused on the power production and the combustion. Therefore, the thermal management of internal combustion engines, which has the same importance, does not taken into consideration sufficiently. However, optimum management of heat transfer system in the engines has an important effect on many parameters such as the engine performance, lubrication quality, exhaust emissions and fuel efficiency. It is necessary to define the engine cooling fluid temperature and flow rate profiles, and to follow them sensitively by a controller system to be designed. In this paper, it is aimed to design and to produce a thermal management system for internal combustion engines in order to ensure optimal distribution of the heat transfer. The electrically driven coolant pump and DC motor controlled valve is considered as control inputs. Additionally, robust control approach based on lumped parameter control-oriented model of engine cooling system is presented. Finally, the proposed and traditional engine cooling systems are compared by means of exhaust emission values. © 2017 IEEE.
Model-Based Sliding Mode Control Technique for the Stewart Platform Mechanism
(Institute of Electrical and Electronics Engineers Inc., 2017) Dumlu, A.; Kaleli, A.; Erentürk, K.; Ayten, K.K.
In this study; design, analysis and real time trajectory tracking control of a six-degree of freedom revolute spherical-spherical (RSS) type parallel manipulator, actuated by six electrical motors, has been studied. A nonlinear sliding mode controller method has been utilized to take dynamic approximation model of the manipulator into account and to improve tracking performance of the manipulator. Real-Time experimental results for the control technique have been verified. Finally, according to the results, the nonlinear sliding mode controller method has improved the tracking performance of the designed manipulator. © 2017 IEEE.
Nonlinear Consensus-Based Synchronizing Tracking Control of Networked DC Motors
(Institute of Electrical and Electronics Engineers Inc., 2018) Guzey, M.; Dumlu, A.
In this study, consensus-based synchronizing control for DC motors is presented. Nonlinear sliding mode based dynamical control of DC motors is extended for a group of networked DC motors and a novel consensus control is developed to make the motors reach the same state (position or speed). One of the motors in the network is assigned as the group leader. The leader is controlled through sliding mode controller to track a given desired trajectory. While the rest of the motors in the network are controlled through the novel synchronizing controller developed in this work. As long as the communication network is connected, it is shown that the motors eventually track the desired trajectory given to the leader without having any knowledge of the desired trajectory. Experimental results are provided at the end of the paper to verify our proposed theoretical claims. © 2018 IEEE.
Optimal Synchronizing Speed Control of Multiple DC Motors
(Institute of Electrical and Electronics Engineers Inc., 2018) Guzey, M.; Dumlu, A.; Guzey, N.; Alpay, A.
In this paper, optimal synchronization speed control of multiple DC motors is developed by using leader-follower based approach. One of the DC motor is considered as a leader motor and it tracks its desired speed trajectory. The other DC motor is controlled through a leader-follower based optimal formation controller developed in this work. If the leader DC motor slows down or speeds up for some reason, the follower DC motor synchronize its speed according to the leader without having any knowledge about the desired trajectory. By doing so, the multiple DC motors will be able to work together in a synchronized manner. The simulation results are given at the end of the paper to verify our theoretical claims. © 2018 IEEE.
Real Time Parameter Estimation and Experimental Validation of Adaptive Self-Tuning Regulator for the Liquid Level Control Process
(Institute of Electrical and Electronics Engineers Inc., 2017) Ayten, K.K.; Dumlu, A.; Kaleli, A.; Kaplan, N.H.
This paper presents self-tuning control design problem for a coupled tank liquid level system that often used in industry. An auto regressive with exogenous (ARX) model has been used as the liquid process model with the self-tuning control implementation due to the flow between tank problem and varying system dynamics. The parameters of designing model have been calculated by using on-line recursive least square method with the forgetting factor algorithm. Additionally, to verify the effectiveness of the proposed model and control algorithm, detailed experimental was realized. © 2017 IEEE.
Real-Time Dehazing via Multiscale Products for Vision Control
(Institute of Electrical and Electronics Engineers Inc., 2017) Kaplan, N.H.; Ayten, K.K.; Dumlu, A.
In this study, a dehazing algorithm based on multiscale product (MSP) prior is presented. In this method, first, the observed hazy image is decomposed into its approximation and detail subbands by undecimated Laplacian decomposition. Then the MSPs of the approximation subbands for each band of the image is calculated to obtain the MSP prior. This prior keeps the significant information of the image, whereas it is capable of detecting the haze in the image. By the use of this prior and the hazy image model, a fast and robust dehazing algorithm is obtained. The proposed method is compared with commonly used methods. The results demonstrate that the proposed algorithm is better than the former methods. Being a fast and robust algorithm, the proposed method has also been applied to a real time robotic vision control system. © 2017 IEEE.
Real-Time Dehazing Via Multiscale Products for Vision Control
(IEEE, 2017) Kaplan, N. H.; Ayten, K. K.; Dumlu, A.
In this study, a dehazing algorithm based on multiscale product (MSP) prior is presented. In this method, first, the observed hazy image is decomposed into its approximation and detail subbands by undecimated Laplacian decomposition. Then the MSPs of the approximation subbands for each band of the image is calculated to obtain the MSP prior. This prior keeps the significant information of the image, whereas it is capable of detecting the haze in the image. By the use of this prior and the hazy image model, a fast and robust dehazing algorithm is obtained. The proposed method is compared with commonly used methods. The results demonstrate that the proposed algorithm is better than the former methods. Being a fast and robust algorithm, the proposed method has also been applied to a real time robotic vision control system.
Real-Time Implementation of Continuous Model Based Sliding Mode Control Technique for Trajectory Tracking Control of Mobile Robot
(2018) Yıldırım, M. R.; Dumlu, A.
In this study, real-time trajectory tracking control of an autonomous mobile robot, actuated by two DC motors, has been designed, analyzed and studied. Two different control approaches such as model based sliding mode (SMC) and the classical proportional–integral–derivative (PID) control are employed to increase the tracking performance of the mobile robot. A model based SMC technique has been presented in order to consider the complete dynamic model of the robot and in order to increase trajectory tracking performance of the system. The experimental outcomes strongly verified that the proposed controller gives a quite well trajectory tracking response and smaller magnitude overshot compared with the classical PID controller.
Real-Time Trajectory Tracking Control for Electric-Powered Wheelchairs Using Model-Based Multivariable Sliding Mode Control
(Institute of Electrical and Electronics Engineers Inc., 2017) Ayten, K.K.; Dumlu, A.; Kaleli, A.
In this paper, real-time trajectory tracking control of an electric-powered wheelchair (EPW), actuated by two DC motors, has been designed, analysed and studied. Two different control approaches such as nonlinear SMC and the classical proportional-integral-derivative (PID) are employed to increase the tracking performance of the electric-powered wheelchair. A nonlinear SMC technique has been presented in order to consider the complete dynamic model of the EPW and so as to increase trajectory tracking performance of the EPW. In this study, realtime experimental application of the two different control methods has been realized. Lastly, in terms of the results, the nonlinear SMC technique has improved the trajectory tracking performance of the designed EPW. © 2017 IEEE.
Sensorless Model Reference Adaptive System Control for Mobile Robot
(Institute of Electrical and Electronics Engineers Inc., 2017) Dumlu, A.; Ayten, K.K.
In this paper, a robust and an efficient tracking controller based on model reference adaptive system (MRAS) technique is performed for mobile robot in order to get the accurate trajectory tracking performance. The model reference adaptive system (MRAS) technique has been presented so as to achieve the estimated value of system's speed information. To estimate the rotor position and speed of the mobile robot system, the MRAS technique is one of the best methods which can be applied to the mobile robot system due to its performance in terms of decrease computational complexity, increase robustness, easy to implement to the systems. In addition, the control scheme of the mobile robot also performs the perfect speed tracking during the motion. All the simulations have verified that the proposed control algorithm is indeed feasible and effective. © 2017 IEEE.
Single Image Dehazing Based on Multiscale Product Prior and Application to Vision Control
(Springer London Ltd, 2017) Kaplan, N. H.; Ayten, K. K.; Dumlu, A.
In this paper, a novel dehazing algorithm based on multiscale product (MSP) prior is presented. First, the observed hazy image is decomposed into its approximation and detail subbands by undecimated Laplacian decomposition. Then the MSPs of the approximation subbands for each band of the image are calculated to obtain the MSP prior. This prior keeps the significant information of the image, whereas it is capable of detecting the haze in the image. By the use of this prior and the hazy image model, a fast and robust dehazing algorithm is obtained. The proposed method is compared with commonly used methods. The results demonstrate that the proposed algorithm is better than the former methods. Being a fast and robust algorithm, the proposed method has also been applied to a real-time robotic vision control system.