Erkan Kayacan was born in Istanbul, Turkey on April 17, 1985. He received the B.Sc. and M.Sc. degrees in mechanical engineering from Istanbul Technical University, Turkey, in 2008 and 2010, respectively. In December 2014, he received the Ph.D. degree at University of Leuven (KU Leuven), Belgium. During his PhD, he held a visitor PhD scholar position at Boston University, USA for 5 months. After his Ph.D., he became a Postdoctoral Researcher with Delft Center for Systems and Control (DCSC), Delft University of Technology, The Netherlands. He is currently a Postdoctoral Researcher with Coordinated Science Lab (CSL) and Distributed Autonomous Systems lab (DASlab) in the University of Illinois at Urbana-Champaign. His research interests include robotics, autonomous systems/vehicles, and control theory.

The link to my scholar google page is here.

Ph.D., Mechatronics, Biostatics and Sensors (2014)
University of Leuven (KU Leuven), Belgium
Postdoctoral Researcher (Dec 2015 - Now)
University of Illinois at Urbana-Champaign, USA
M.Sc., System Dynamics and Control Program (2010)
Istanbul Technical University, Turkey
Postdoctoral Researcher (Mar 2015 - Dec 2015)
Delft University of Technology , Netherlands
B.Sc., Mechanical Engineering (2008)
Istanbul Technical University, Turkey
Research Assistant (Mar 2011 - Dec 2014)
University of Leuven (KU Leuven), Belgium
 
Visiting PhD Scholar (Feb 2014 - Jul 2014)
Boston University, USA
Nowadays, the complexity in design of systems increases enormously due to the fact that human beings desire intelligence and autonomy in systems, such as cyber-physical systems. These systems are described as large-scale, distributed and interconnected. The challenge in intelligent systems is to have adaptability to maintain reliability even in highly uncertain environment. Therefore, my research interests are in the areas of optimization-based control and estimation methods, and learning algorithms for control systems and observers with an application focus on complex mechatronics systems, e.g., unmanned vehicles. The core area of expertise are control systems, e.g. model predictive control, with a focus on nonlinear systems. My study spans from modeling and identification of dynamic system, control systems and estimation methods. I have published 12 journal papers (SCI and SCI-E, mostly in IEEE Trans. Control Syst. Technol., IEEE/ASME Trans. Mechatronics, IEEE Trans. Ind. Electron., IEEE Trans Cybernetics, IFAC Mechatronics) and 8 conference papers (SCI and SCI- E). I am the first author on 11 journal papers and 4 conference papers. My H-index is respectively 10 and 8 in google scholar and web of science.

Media Release

A robot under development at the University of Illinois automates the labor-intensive process of crop phenotyping, enabling scientists to scan crops and match genetic data with the highest-yielding plants. Agricultural and biological engineering professor Girish Chowdhary, right, is working on the $3.1 million project, along with postdoctoral researcher Erkan Kayacan.

A semiautonomous robot may soon be roaming agricultural fields gathering and transmitting real-time data about the growth and development of crops, information that crop breeders – and eventually farmers – can use to identify the genetic traits in plants likely to produce the greatest yields.

On Illinois News: https://news.illinois.edu/blog/view/6367/467197

On DTN: https://www.dtnpf.com/agriculture/web/ag/news/equipment-tech/article/2016/11/28/meet-next-farm-hand

TERRA-MEPP Promo Video

TERRA-MEPP (Mobile Energy-Crop Phenotyping Platform) is a low-cost, autonomous robot that analyzes biofuel crops throughout the growing season to pinpoint plants with desirable yield and sustainability traits.

Receding Horizon Control and Estimation Methods for a Mobile Robot

Autonomous guidance systems are working in uncertain environments so that it is a requirement to adapt themselves continuously to changing conditions to avoid steady-state errors, oscillations at the output or even instability of the closed loop system. Receding horizon control and estimation algorithms, which are optimization based methods, are proposed to control a mobile robot by utilizing an adaptive nonlinear kinematic model.

Learning with Moving Horizon Estimation

When model-based control structures have to deal with uncertain and varying process conditions, it is inevitable to use adaptive models. Real-time estimators allow to make these model adaptations through online parameter estimation. Nonlinear moving horizon estimation method has been chosen as a state and parameter estimation algorithm because it considers the state and parameter estimation within the same problem and allows to incorporate constraints both on states and parameters.

Centralized Model Predictive Control

To automate the trajectory following problem of an autonomous tractor-trailer system and also increase its steering accuracy, a centralized nonlinear model predictive control (CeNMPC) approach has been used. A fast CeNMPC is combined with nonlinear moving horizon estimation (NMHE) to obtain accurate trajectory tracking of an autonomous tractor-trailer system under unknown and variable soil conditions.

Type-2 Neuro-Fuzzy Control

Instead of modeling the interactions between the subsystems prior to the design of a model-based control, we develop a control algorithm which learns the interactions online from the measured feedback error. The proposed learning algorithm is tested on the trajectory tracking problem of an autonomous agricultural tractor-trailer system in the presence of various nonlinearities and uncertainties in real time.

Journal Papers

12. Erkan Kayacan, Joshua M. Peschel and Girish Chowdhary, “A self-learning disturbance observer for nonlinear systems in feedback-error learning scheme,” Engineering Applications of Artificial Intelligence, vol. 62, pp. 276-285, 2017. PDF URL

11. Erkan Kayacan, “Multi-Objective H-infinity Control for String Stability of Cooperative Adaptive Cruise Control Systems,” IEEE Transactions on Intelligent Vehicles, vol. 2, no. 1, pp. , 2017. PDF URL

10. Erkan Kayacan, Herman Ramon, Wouter Saeys, “Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles,” IEEE/ASME Transactions on Mechatronics, , vol. 21, no. 2, pp. 806-814, 2016. PDF URL

9. Erkan Kayacan, Erdal Kayacan, Mojtaba Ahmadieh Khanesar, “Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks-A Novel Learning Algorithm and a Comparative Study,” IEEE/ASME Transactions on Industrial Electronics, vol.62, no.3, pp.1716-1724, 2015. PDF URL

8. Erkan Kayacan, Erdal Kayacan, Herman Ramon, Wouter Saeys, “Robust Tube-based Decentralized Nonlinear Model Predictive Control of an Autonomous Tractor-Trailer System,” IEEE Transactions on Mechatronics, vol.20, no.1, pp.447-456, 2015. PDF URL

7. Erkan Kayacan, Erdal Kayacan, Herman Ramon, and Wouter Saeys, "Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-trailer System", IEEE Transactions on Control Systems Technology, vol.23, no.1, pp.197-205, 2015 PDF URL

6. Erdal Kayacan, Erkan Kayacan, Herman Ramon, Wouter Saeys, “Towards Agrobots: Trajectory Control of an Autonomous Tractor Using Type-2 Fuzzy Logic Controllers,” IEEE/ASME Transactions on Mechatronics, vol.20, no.1, pp.287-298, 2015. PDF URL

5. Erkan Kayacan, Erdal Kayacan, Herman Ramon, Wouter Saeys, “Towards agrobots: Identification of the yaw dynamics and trajectory tracking of an autonomous tractor,” Computers and Electronics in Agriculture, vol. 115, pp.78-87, 2015. PDF URL

4. Erkan Kayacan, Erdal Kayacan, Herman Ramon, and Wouter Saeys, "Distributed Nonlinear Model Predictive Control of an Autonomous Tractor-trailer System", Mechatronics, vol.24, no.8, pp.926-933, 2014. PDF URL

3. Erkan Kayacan, Erdal Kayacan, Herman Ramon, and Wouter Saeys, "Nonlinear Modeling and Identification of an Autonomous Tractor-Trailer System ", Computers and Electronics in Agriculture, vol. 106, pp.1-10, 2014. PDF URL

2. Erkan Kayacan, Erdal Kayacan, Herman Ramon, Wouter Saeys, “Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm,” IEEE Transactions on Cybernetics, vol.43, no.1, pp.170-179, Feb. 2013.PDF URL

1. Erkan Kayacan, Zeki Y. Bayraktaroglu, and Wouter Saeys, “Modeling and Control of a Spherical Rolling Robot: A Decoupled Dynamics Approach” Robotica, vol. 30, pp. 671-680, 2012. PDF URL

Conference Articles

8. Erkan Kayacan and Joshua M. Peschel, "Robust Model Predictive Control of Systems by Modeling Mismatched Uncertainty", 10th IFAC Symposium on Nonlinear Control Systems, Monterey, CA, 2016. (Accepted)

7. Erkan Kayacan Joshua M. Peschel and Erdal Kayacan, "Centralized, Decentralized and Distributed Nonlinear Model Predictive Control of a Tractor-Trailer System: A Comparative Study", 2016 American Control Conference (ACC), Boston, MA, 2016, pp. 4403-4408. PDF URL

6. Erdal Kayacan, Mojtaba A. Khanesar and Erkan Kayacan, "Stabilization of Type-2 Fuzzy Takagi-Sugeno-Kang Identifier Using Lyapunov Functions", The 2015 IEEE International Conference on Fuzzy Systems, Istanbul, Turkey, pp. 1-6, August 2-5, 2015. PDF URL

5. Erkan Kayacan, Erdal Kayacan, Herman Ramon, and Wouter Saeys, "Modeling and Identification of the Yaw Dynamics of an Autonomous Tractor", The 9th Asian Control Conference (ASCC 2013), Istanbul, Turkey,June 23-26, 2013 PDF URL

4. Erdal Kayacan, Erkan Kayacan, Herman Ramon, and Wouter Saeys, "A Robust On-line Learning Algorithm for Type-2 Fuzzy Neural Networks and its Experimental Evaluation on an Autonomous Tractor", 2012 IEEE International Conference on Systems, Man, and Cybernetics, Seoul, South Korea, pp. 1652-1657, 14-17 October, 2012. PDF URL

3. Erdal Kayacan,Wouter Saeys, Erkan Kayacan, Herman Ramon, and Okyay Kaynak, "Intelligent control of a tractor-implement system using type-2 fuzzy neural networks", WCCI 2012 IEEE World Congress on Computational Intelligence, Brisbane, Australia, 2012, pp. 171-178. 2012. PDF URL

2. Erkan Kayacan, Erdal Kayacan, Herman Ramon, and Wouter Saeys, "Velocity Control of a Spherical Rolling Robot Using a Grey-PID Type Fuzzy Controller With an Adaptive Step Size", SYROCO 2012 10th International IFAC Symposium on Robot Control, Dubrovnik, Croatia, pp. 863-868, 5-7 September, 2012. PDF URL

1. Erdal Kayacan, Erkan Kayacan, Herman Ramon, andWouter Saeys, "Neuro-Fuzzy Control with a Novel Training Method Based-on Sliding Mode Control Theory: Application to Tractor Dynamics", SYROCO 2012 10th International IFAC Symposium on Robot Control, Dubrovnik, Croatia, pp. 889-894, 5-7 September, 2012. PDF URL

Postal address: Room 1115, 1206 W Gregory Drive, Urbana, Illinois 61801
Email: (myname) k at illinois (dot) edu