Faculty

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Erdal Kayacan


Erdal Kayacan
Assistant Professor
Tel: 6790 5585
Email: erdal@ntu.edu.sg
Office: N3.2-02-28 
Homepage: http://www.erdalkayacan.com
   
Education
  • Ph.D. Electrical & Electronics Engineering, Bogazici University
  • M.Sc. Systems and Control Engineering, Bogazici University
  • B.Sc. Electrical Engineering, Istanbul Technical University

Biography
Asst. Prof. Erdal Kayacan received a B.Sc. degree in electrical engineering from Istanbul Technical University, Istanbul, Turkey, in 2003 and a M.Sc. degree in systems and control engineering from Bogazici University, Istanbul, Turkey, in 2006. In September 2011, he received a Ph.D. degree in electrical and electronic engineering at Bogazici University, Istanbul, Turkey. After finishing his post-doctoral research in KU Leuven at the division of mechatronics, biostatistics and sensors (MeBioS), he is currently pursuing his research in Nanyang Technological University at the School of Mechanical and Aerospace Engineering as an assistant professor. His research areas are computational intelligence methods, sliding mode control, model predictive control, mechatronics and unmanned aerial vehicles.

Research
  • Interest:
    Computational intelligence, unmanned ground and aerial vehicles control
  • Projects:
    Design of lightweight UAV for 3D Printing
    We are trying to have a set of documented design guidelines for lightweight structures via 3D printing and a finalized design of the lightweight UAV. We are planning to use hybrid manufacturing approach in using multiple materials to create integrated components with electrical and mechanical functionalities.
    [Flight Mechanics and Control Laboratory, Aerospace]
    Learning control algorithms for unmanned aerial vehicles.
    The main goal of this project is to design several learning model-based and model-free control techniques for the control of UAVs. As a model-based approach, linear and nonlinear model predictive controllers will be elaborated. As a model-free method, the combination of neural networks and fuzzy logic controllers will be studied.
    [Flight Mechanics and Control Laboratory, Aerospace]
    Model predictive control-moving horizon estimation framework as applied to tilt rotor UAVs and its experimental evaluation
    The main goal of this project is to design model predictive control (MPC)-moving horizon estimation (MHE) framework for highly nonlinear unmanned aerial vehicles (UAVs), e.g. a tilted rotor tricopter.
    [Flight Mechanics and Control Laboratory, Aerospace]
    Precise landing for unmanned aerial vehicles
    This project aims to solve the precise landing problem of a VTOL UAV by using a cost-effective hybrid method consisting of local positioning systems (vision based sensors) and global positioning systems (GPSs). In this project, the advantages of local and global positioning systems will be combined to realize one specific goal: precise landing.
    [ST Engineering-NTU Corporation Laboratory, Aerospace]
    Fuzzy neural network-based learning control of unmanned aerial vehicles
    For the online learning control purpose of small size unmanned aerial vehicles, the combination of artificial neural networks and fuzzy logic controllers will be implemented in this project.
    [ST Engineering-NTU Corporation Laboratory, Aerospace]
    Automated Construction Quality Assessment Robot System (A-CONQUARS)
    Developing a mobile robot system (A-CONQUARS) equipped with inspection instruments to conduct automatic quality assessment.
    [Robotics Research Center, Robotics & Automation]

Research Students under supervision

PhD Student
Name Project
Sarabakha Andriy Vision-based Control of UAV
Yunus Govdeli   Lightweight 3D-printed UAV
Efe Camci Model free control of unmanned aerial vehicles by using reinforcement learning
Nursultan Imanberdiyev Vision-based control of unmanned aerial vehicles
Wong Zhuo Wei   Design of Light Weight 3D Printed UAV With Intelligent Control 
Mehndiratta Mohit Optimization-based control of 3D printed Unmanned Aerial vehicles

Selected Publications
  • Erdal Kayacan and Reinaldo Maslim, ‘Type-2 Fuzzy Logic Trajectory Tracking Control of Quadrotor VTOL Aircrafts With Elliptic Membership Functions", Mechatronics, IEEE/ASME Transactions on (Impact factor 3.851, Q1, rank: 3/59 in Automation and Control Systems) (Accepted for publication).
  • Rui-Jun Yan, Jing Wu, Ji Yeong Lee, Abdul Manan Khan, Chang-Soo Han, Erdal Kayacan, I-Ming Chen “A Novel Method for 3D Reconstruction: Division and Merging of Overlapping B-spline surfaces", Computer-Aided Design, vol.81, pp.14-23, December 2016. (Impact factor 2.149, Q1, rank: 8/106 in Computer Science, Software Engineering)
  • Changhong Fu, Ran Duan, Dogan Kircali and Erdal Kaycan, “Onboard Robust Visual Tracking for UAVs Using a Reliable Global-Local Object Model", Sensors, vol.16, no.9, pp. 1406, 2016 (Impact factor 2.033, Q1, rank: 12/56 in Instruments and Instrumentation)
  • Saima Hassan, Mojtaba Ahmadieh Khanesar, Erdal Kayacan, Jafreezal Jaafar and Abbas Khosravi “Optimal design of adaptive type-2 neuro-fuzzy systems: A review", Applied Soft Computing, vol.44, pp.134-143, July 2016. (Impact factor 2.857, Q1, rank: 21/130 in Computer Science, Artificial Intelligence)
  • Tien Thanh Nguyen, Koenraad Vandevoorde, Niels Wouters, Erdal Kayacan, Josse De Baerdemaeker and Wouter Saeys “Detection of red and bicoloured apples on tree with an RGB-D camera", Biosystems Engineering, vol.146, pp. 33-44, June 2016. (Impact factor 1.997, Q1, rank: 7/57 in Agriculture, Multidisciplinary)
  • Jaime Rubio Hervas, Mahmut Reyhanoglu, Hui Tang and Erdal Kayacan, “Nonlinear control of fixed-wing UAVs in presence of stochastic winds", Communications in Nonlinear Science and Numerical Simulation, vol.33, pp.57-69, April 2016. (Impact factor 2.834, Q1, rank: 5/254 in Mathematics, Applied)

Teaching
  • Control Theory
  • Aircraft Navigation & Flight Computers
  • Flight Dynamics
  • Advanced Flight Dynamics