Vision Based Control of Magnetic Levitation System

Gürkan Küçükyıldız

This work presents vision based control of a single-axis magnetic levitation system. The  system is the fundamental of the high speed maglev trains and magnetic bearings. A ferro magnetic object is levitated at a desired position in the air gap by applying electromagnetic  force against to gravity. The system consists of five main components: a position sensor and a camera, a coil, a controller, a driver and a ferro-magnetic object. Current on the coil, which causes electromagnetic force, was controlled according to position feedback. The mathematical model of the system was obtained based on Newton’s theory and verified by experimental data. The nonlinear relationships between force, current and air gap were found experimentally. The controller was designed using feedback linearization technique based on the nonlinear relationships. In the literature, light based sensors have mostly been used to detect the object’s position. Despite the preferred usage of conventional sensors, some disadvantages are encountered such as: calibration requirement, non-linearity, noise and non robustness. These disadvantages inevitably cause disturbances on the system. In this study, the position of the magnetic object was detected using image processing methods in order to overcome the disadvantages associated with the light based sensors.

Design and Navigation of a Robotic Wheel Chair

Gürkan Küçükyıldız, Suat Karakaya

In this study, design and navigation of a robotic wheelchair for disabled or elderly people was explored. Developed system consists of a wheelchair, high-power motor controller card, Kinect camera, RGB camera, EMG sensor, EEG sensor, and computer.  Kinect camera was installed on the system in order to provide safe navigation of the system. Depth frames, captured by Kinect camera, were processed with developed image processing algorithm to detect obstacles around the wheelchair. RGB camera was mounted to system in order to detect head-movements of user. Head movement, has the highest priority for controlling of the system. If any head movement detected by the system, all other sensors were disabled. EMG sensor was selected as second controller of the system. Consumer grade an EMG sensor (Thalmic Labs) was used to obtain eight channels EMG data in real time. Four different hand movements: Fist, release, left and right were defined to control the system using EMG. EMG data was classified different classification algorithms( ANN,SVM and random forest) and most voted class was selected as result.  EMG based control can be activated or disabled by user making a fist or release during three seconds.  EEG based control has lowest priority for controlling the robotic wheelchair. A wireless 14 channels EEG sensor (Emotiv Epoch) was used to collect real time EEG data. Three different cognitive tasks: Solving mathematical problems, relaxing and social task were defined to control the system using EEG. If system could not detect a head movement or EMG signal, EEG based control is activated.   In order to other to control user should accomplish the relative cognitive task.   During experiments, all users could easily control the robotic wheelchair by head movements and EMG movements. Success of EEG based control of robotic wheelchair varies because of user experiments. Experienced users and  un-experienced user changes the result of the system.

DC Motorun Gerçek Zamanda Konum ve Hız Kontrolü

PID ve Bulanık Mantık ile DC Motorun Gerçek Zamanda DSPIC Tabanlı Konum ve Hız Kontrolü

Gürkan Küçükyıldız, Gökhan Taşçı

Bu çalışmada seri uyartımlı fırçalı bir DC motorun konum, hız kontrolleri PID ve Bulanık Mantık kontrol yöntemleri kullanılarak gerçek zamanda gerçekleştirilmiştir. Sistem girişine farklı referans değerleri uygulanarak konum ve hız değişkenlerinin istenilen referans değerinde tutulması amaçlanmıştır. Sistem için gerekli kodlar C18 ortamında geliştirilmiş olup DSPIC33FJ128MC804 isimli bir işlemci yardımıyla gerçek zamana aktarılmıştır. PID ve Bulanık mantık yöntemleriyle elde edilen deneysel veriler sonuçlar bölümünde karşılaştırılmıştır.

DSP Based Real Time Lane Detection Algorithm

Development And Optimization Of DSP Based Real Time Lane Detection Algorithm On A Mobile Robot Platform

Gürkan Küçükyıldız

In this study, development and optimization of a Hough transform based real time lane detection algorithm was explored. Finding lane marks by using Hough transform on captured video frames was the main goal of the system. Image processing code was developed on Visual DSP 5.0 environment and the code was run on BF-561 processor embedded in ADSP BF561 EZ KIT LITE evaluation board. The code was optimized into a form which is satisfactory for real time applications. A mobile robot platform was developed during the study and the image processing algorithm was tested on this platform. The experimental results which were obtained before and after the optimization of the code were compared.