Short Bio

Dr. Erdal Kayacan received a Ph.D. degree in electrical and electronic engineering at Bogazici University, Istanbul, Turkey. After finishing his post-doctoral research at KU Leuven at the division of mechatronics, biostatistics and sensors (MeBioS) in 2014, he worked in Nanyang Technological University at the School of Mechanical and Aerospace Engineering as an assistant professor for four years. Currently, he is pursuing his research in Aarhus University at the Department of Engineering as an associate professor. He is the Director of Artificial Intelligence in Robotics (Air Lab) laboratory.

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 ISBN:9780128026878. (17 Sept 2015).

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.

Positions

Now2018

Associate Professor
Aarhus University, Department of Engineering
20182014

Assistant Professor
Nanyang Technological University, Mechanical and Aerospace Engineering
20142011

Post-doctoral Researcher
University of Leuven (KU Leuven), Division of Mechatronics, Biostatistics and Sensors

Artificial Intelligence in Robotics Laboratory (Air Lab) opened and open for research…

Artificial Intelligence in Robotics Laboratory (Air Lab) is opened and open for research. Our new facilities in Skejby: Deep Tech Experimental Hub.

12 V8 Vicon cameras, 15x15x5 meters of flight arena!

A new book in adaptive neuro-fuzzy systems is available!

Fuzzy Neural Networks for Real Time Control Applications: Concepts, Modeling and Algorithms for Fast Learning
by Erdal Kayacan and Mojtaba Ahmadieh Khanesar
With foreword by Jerry M. Mendel

AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS

Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book!

Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book.

Projects Directed (Ongoing)

DHRTC

Danish Hydrocarbon Research and Technology Centre: Visualisation of Virtual Outcrops Using Aerial Robots

by Technical University of Denmark, Danish Hydrocarbon Research and Technology Centre
EU

OpenDR: Open Deep Learning Toolkit for Robotics

by HORIZON 2020 - LEIT ICT WORK PROGRAMME 2018-2020

Projects Directed (Finished)

ST Eng-NTU Corp Lab

ST Eng-NTU Corp Lab: Precise landing for unmanned aerial vehicles

by ST Eng-NTU Corp Lab
ST Eng-NTU Corp Lab

ST Eng-NTU Corp Lab: Fuzzy neural network-based learning control of unmanned aerial vehicles

by ST Eng-NTU Corp Lab
JTC Corporation - NRF

JTC Corporation - NRF: Automated Construction Quality Assessment Robot System (A-CONQUARS)

by JTC Corporation - NRF
NTU Start up Grant

NTU Start up Grant (Learning control algorithms for unmanned aerial vehicles)

by Nanyang Technological University
MOE Tier 1

MOE Academic Research Funding (AcRF) Tier 1: Learning-based path planning of unmanned aerial vehicles with vision-based sensing

by Nanyang Technological University
MOE Tier 1

MOE Academic Research Funding (AcRF) Tier 1: Model predictive control-moving horizon estimation framework as applied to tilt rotor UAVs and its experimental evaluation

by Nanyang Technological University
NRF

NRF - Design of lightweight UAV for 3D Printing

by NRF Medium-Sized Centre (MSC)