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Erdal Kayacan


Erdal Kayacan
Assistant Professor
Tel: 6790 5585
Email: erdal@ntu.edu.sg
Office: N3.2-02-21 
Homepage: http://www.erdal.info
   
Education
  • Ph.D. Electrical & Electronics Engineering, Bogazici University
  • M.Sc. Systems and Control Engineering, Bogazici University
  • B.Sc. Electrical Engineering, Istanbul Technical University

Biography
Erdal Kayacan holds a PhD in Electrical and Electronic Engineering from Bogazici University (2011). He was a visiting scholar in University of Oslo in 2009 with the research fellowship of Norway Research Council. After his post-doctoral research in KU Leuven at the Division of Mechatronics, Biostatistics and Sensors, Dr. Kayacan went on to pursue his research in Nanyang Technological University at the School of Mechanical and Aerospace Engineering as assistant professor (2014 - current).

He has published more than 90 peer-refereed book chapters, journal and conference papers in intelligent control, fuzzy systems and robotics. In the area of artificial intelligence in system identification and control theory as applied to robotic applications, his contributions to interval type-2 fuzzy neural networks have made a notable impact on the computational intelligence community and have also reached a high level of international recognition. Dr. Kayacan is co-writer of a course book "Fuzzy Neural Networks for Real Time Control Applications, 1st Edition Concepts, Modeling and Algorithms for Fast Learning", Butterworth-Heinemann, Print Book. He is a Senior Member of Institute of Electrical and Electronics Engineers (IEEE). From 1st Jan 2017, he is an Associate Editor of IEEE Transactions on Fuzzy Systems, the leading international journal in artificial intelligence.

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 Learning Control of Unmanned Aerial Vehicles Using Artificial Intelligence-Based Methods
Yunus Govdeli   Autonomous Flight Control of a 3D Printed Lightweight Flying Wing VTOL Unmanned Aerial Vehicle
Efe Camci Enhanced Path Planning of UAVs By Artificial Intelligence Methods
Nursultan Imanberdiyev Towards Aerial Manipulation: Intelligent Control approach for Unmanned Aerial Manipulators
Wong Zhuo Wei   Monitoring of Additive Manufacturing Process With Machine Learning Approaches
Mehndiratta Mohit Online Learning-based Control of Unmanned Aerial Vehicles Incorporating Dynamic Optimisation

Selected Publications
  • Andriy Sarabakha, Changhong Fu, Erdal Kayacan and Tufan Kumbasar, “Type-2 Fuzzy Logic Controllers Made Even Simpler: From Design to Deployment in Real-Time for Quadcopter UAVs”, IEEE Trans. on Industrial Electronics (In press).
  • Efe Camci, Devesh Raju Kripalani, Linlu Ma, Erdal Kayacan and Mojtaba Ahmadieh Khanesar, “An Aerial Robot for Rice Farm Quality Inspection With Type-2 Fuzzy Neural Networks Tuned by Particle Swarm Optimization-Sliding Mode Control Hybrid Algorithm, Swarm and Evolutionary Computation (In press).
  • Anna Prach and Erdal Kayacan, “An MPC-based Position Controller for a Tilt-Rotor Tricopter VTOL UAV”, Optimal Control, Applications and Methods, 2017; 1-14. https://doi.org/10.1002/oca.2350
  • Andriy Sarabakha, Nursultan Imanberdiyev, Erdal Kayacan, Mojtaba Ahmadieh Khanesar and Hani Hagras, “Novel Levenberg-Marquardt Based Learning Algorithm for Unmanned Aerial Vehicles”, Information Sciences, vol.417, pp. 361-380, November 2017
  • Yiqun Dong, Efe Camci and Erdal Kayacan, “Faster RRT-based Nonholonomic Path Planning in 2D Building Environments Using Skeleton-constrained Path Biasing”, Journal of Intelligent & Robotic Systems, pp. 1-12, May 2017
  • Mohit Mehndiratta and Erdal Kayacan, “Receding Horizon Control of 3 DOF Helicopter Using Online Estimation of Aerodynamic Parameters”, Proceedings of the Institution of Mechanical engineers Part G-Journal of Aerospace Engineering, pp. 1-12, April 2017.
  • Ran Duan, Changhong Fu and Erdal Kayacan, “Tracking-Recommendation-Detection”, Engineering Applications of Artificial Intelligence vol.64, pp.128-139, September 2017
  • Utku Eren, Anna Prach, Basaran Bahadir Kocer, Sasa Rakovic, Erdal Kayacan, and Behcet Acikmese, “Model Predictive Control in Aerospace Systems: Current State and Opportunities” AIAA Journal of Guidance, Control and Dynamics, vol.40, no.7, pp.1541-1566, June 2017
  • Lily Liu, Rui-Jun Yan, Varun Maruvanchery, Erdal Kayacan, I-Ming Chen and Tiong Lee Kong, “TLCAF: Transferred Learning on Convolutional Activation Feature as Applied to a Building Quality Assessment Robot”, International Journal of Advanced Robotic Systems, vol.14, no.3, pp.1-12, June 2017
  • 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, vol.22, no. 1, pp. 339-348, February 2017
  • Erdal Kayacan, Mojtaba Ahmadieh Khanesar, Jaime Rubio Hervas and Mahmut Reyhanoglu, “Learning control of fixed-wing unmanned aerial vehicles using fuzzy neural networks”, International Journal of Aerospace Engineering, Article ID 5402809, vol. 2017 (2017), pp.1-12.

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