Erkan Kayacan 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 was a Postdoctoral Researcher with Delft Center for Systems and Control (DCSC), Delft University of Technology, The Netherlands and Distributed Autonomous Systems lab, University of Illinois at Urbana-Champaign, Urbana, IL, USA. He is currently a Postdoctoral Associate with Senseable City Laboratory and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, MA, USA. His research interests include robotics, autonomous systems/vehicles, and control theory.

The link to my scholar google page is here.

  • 03/2018, I serve as an Associate Editor for IROS 2018.
  • 02/2018, Moved to MIT as a Postdoctoral Associate.
  • 02/2018, A journal paper accepted to Asian Journal of Control.
  • 01/2018, I gave a talk at Georgia Institute of Technology, USA.
  • 01/2018, I gave a talk at the University of Iowa, Iowa, USA.
  • 12/2017, I gave a talk at the National University of Singapore, Advanced Robotics Center.
  • A glimpse of my talk:

  • 08/2017, I gave a talk at Norwegian University of Science and Technology, Trondheim, Norway.
  • Ph.D., 2014
    Mechatronics, Biostatics and Sensors

    University of Leuven (KU Leuven), Belgium
    Postdoctoral Associate (Feb 2018 - Now)
    Massachusetts Institute of Technology, USA
    M.Sc., 2010
    System Dynamics and Control Program

    Istanbul Technical University, Turkey
    Postdoctoral Researcher (Dec 2015 - Feb 2018)
    University of Illinois at Urbana-Champaign, USA
    B.Sc., 2008
    Mechanical Engineering

    Istanbul Technical University, Turkey
    Postdoctoral Researcher (Mar 2015 - Dec 2015)
    Delft University of Technology , Netherlands
    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 the design of robotic systems increases enormously due to the fact that human beings desire a higher level of intelligence and autonomy. Additionally, it is important that the developed systems must be capable of autonomously adapting to the variations in the operating environment while maintaining the overall objective to accomplish tasks even in highly uncertain and unstructured environments. Such robotic systems must display the ability to learn from experience, adapt themselves to the changing environment and seamlessly integrate information to-and-from humans. My core interest is to enhance performance and autonomy for robots through safe learning with degraded sensing to adapt themselves to varying working conditions in unstructured and uncertain environments. My research interests center around real-time optimization-based control and estimation methods, nonlinear control, learning algorithms and machine learning with a heavy emphasis on applications to autonomous systems.

    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:

    On DTN:

    High Precision Control of an Ultra-Compact 3D Printed Field Robot in the Presence of Slip

    A nonlinear moving horizon estimator identifies key terrain parameters using on-board robot sensors, and a robust learning-based nonlinear model predictive controller is designed to establish an effective control law for the 3D printed field robot traveling on rough terrain. The framework is designed to ensure high precision autonomous path tracking in the presence of unknown wheel-terrain interaction.

    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.

    Cooperative Adaptive Cruise Control System

    String stability of connected self-driving cars.

    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.

    Feedback-Error Learning Algorithm

    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.

    Book Chapters

    B1. Erkan Kayacan, Erdal Kayacan, I-Ming Chen, Herman Ramon, and Wouter Saeys, "On the Comparison of Model-Based and Model-Free Controllers in Guidance, Navigation and Control of Agricultural Vehicles", In: John R., Hagras H., Castillo O. (eds) Type-2 Fuzzy Logic and Systems. Studies in Fuzziness and Soft Computing, vol 362. pp. 49-73, 2018, Springer, Cham. PDF URL

    Journal Papers

    J13. Erkan Kayacan, Thor I. Fossen, “Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers, Asian Journal of Control (In Press). PDF

    J12. 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

    J11. 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

    j10. 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

    J9. 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

    J8. 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

    J7. 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

    J6. 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

    J5. 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

    J4. 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

    J3. 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

    J2. 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

    J1. 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

    C8. Erkan Kayacan and Joshua M. Peschel, "Robust Model Predictive Control of Systems by Modeling Mismatched Uncertainty", 10th IFAC Symposium on Nonlinear Control Systems, IFAC-PapersOnLine, 49(18), Monterey, CA, pp. 265-269, 2016. PDF URL

    C7. 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

    C6. 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

    C5. 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

    C4. 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

    C3. 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

    C2. 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

    C1. 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: MIT 9-250, 77 Massachusetts Avenue, Cambridge, MA 02139 USA 
    Email: {myfirstname}{the initial letter of mysurname} at {mit} dot {edu}