Erkan Kayacan received the B.Sc. degree in mechanical engineering and the M.Sc. degree in system dynamics and control from Istanbul Technical University, Istanbul, Turkey in 2008 and 2010, respectively. He received the Ph.D. degree in Mechatronics, Biostatistics and Sensors from University of Leuven (KU Leuven), Leuven, Belgium in 2014.

He is currently a Lecturer (equivalent to Tenure-Track Asst. Prof. in US) with the School of Mechanical & Mining Engineering, University of Queensland (UQ), Australia. Prior to UQ, he was a Postdoctoral Researcher with Delft University of Technology, University of Illinois at Urbana-Champaign, and Massachusetts Institute of Technology. His research interests include real-time optimization-based control and estimation methods, nonlinear control theory, learning algorithms and machine learning with a heavy emphasis on applications to autonomous systems and field robotics.

Dr. Kayacan is a recipient of the Best Systems Paper Award at Robotics: Science and Systems (RSS) in 2018.

The link to my scholar google page is here.

  • 07/2019, My journal paper accepted to IEEE/ASME Transactions on Mechatronics.
  • 06/2019, Two papers accepted to 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
  • 04/2019, Moved to the University of Queensland, Australia as a Lecturer (equivalent to Tenure-Track Asst. Prof. in US) in Mechatronic Engineering.
  • 03/2019, I have been elected to the grade of Senior Member, IEEE.
  • 02/2019, I serve as an Associate Editor for IROS 2019.
  • 02/2019, Our paper accepted to 2019 International Conference on Robotics and Automation (ICRA).
  • 01/2019, Our journal paper accepted to Neurocomputing. 20th journal paper!
  • 09/2018, Our book chapter accepted to Handbook of Model Predictive Control.
  • 09/2018, Our paper accepted to Precision Agriculture.
  • 08/2018, Our paper accepted to Journal of Intelligent & Robotic Systems.
  • 08/2018, More than 500 Citations in Google Scholar.
  • 07/2018, My paper accepted to Transactions of the Institute of Measurement and Control.
  • 07/2018, Our paper accepted to IEEE/ASME Transactions on Mechatronics.
  • 06/2018, Our paper selected as Best Systems Paper in Robotics: Science and Systems 2018 (RSS 2018).
  • 06/2018, My paper accepted to Transactions of the Institute of Measurement and Control.
  • 05/2018, Our paper accepted to Journal Field Robotics.
  • 05/2018, Our paper is a finalist for the best systems paper award in Robotics: Science and Systems (RSS) 2018.
  • 04/2018, One paper accepted to RSS 2018, Pennsylvania, USA.
  • 04/2018, One paper accepted to CCTA, Denmark.
  • 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.
  • Ph.D., 2014
    Mechatronics, Biostatics and Sensors

    University of Leuven (KU Leuven), Belgium

    Lecturer (Apr 2019 - Now)
    (Equivalent to Tenure-Track Asst. Prof. in US)
    The University of Queensland, Australia

    M.Sc., 2010
    System Dynamics and Control Program

    Istanbul Technical University, Turkey
    Postdoctoral Associate (Feb 2018 - Mar 2019)
    Massachusetts Institute of Technology, USA
    B.Sc., 2008
    Mechanical Engineering

    Istanbul Technical University, Turkey
    Postdoctoral Researcher (Dec 2015 - Feb 2018)
    University of Illinois at Urbana-Champaign, USA

    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.

    Learning-based Control of Reconfigurable Autonomous Vessels: Roboats

    The number of possible configurations of reconfigurable autonomous vessels exponentially grows as the total number of vessels increases, which imposes a technical challenge in modeling and identification. In this work, we propose a framework consisting of a real-time parameter estimator and a feedback control strategy, which is capable of ensuring high-accurate path tracking for any feasible configuration of vessels. Through experiments on different configurations of connected-vessels, we demonstrate stability of our proposed approach and its effectiveness in high-accuracy in path tracking.

    Coordinated Control of a Reconfigurable Multi-Vessel Platform: Robust Control Approach

    We e develop a coordinated robust control scheme for a reconfigurable multi-vessel platform. The platform consists of N propeller-driven vessels each of which is capable of latching to another vessel to form a rigid body of connected vessels. Through experiments we assess trajectory tracking and disturbance attenuation performance of the control scheme in various configurations of the platform. Experiment results yield that average position and orientation tracking error are approximately 0.09m and 3 degrees, and the maximum tracking error-to-disturbance ratio is 1.12.

    A simple learning strategy for feedback linearization control: Experimental validation on aerial robots

    A simple learning strategy for feedback linearization control (SL-FLC) of systems with uncertainties is developed to facilitate accurate tracking in unknown/uncertain environments. The SL-FLC utilizes desired closed-loop error dynamics of the nominal system, which is minimized via the gradient-descent method to find the adaptation rules for feedback controller gains and disturbance estimate in the feedback control law, and finds the global optimum point. The SL-FLC framework can ensure the desired closed-loop error dynamics in the presence of disturbances. The performance of the SL-FLC is experimentally validated for the position tracking of a 3D-printed tilt-rotor tricopter UAV.

    Media Release - Illinois’ corn-counting robot earns top recognition at leading robotics conference

    A robot developed by the University of Illinois to find these proverbial needles in the haystack was recognized by the best systems paper award at Robotics: Science and Systems, the preeminent robotics conference held in Pittsburgh.

    “The lack of automation for measuring plant traits is a bottleneck to progress,” said first author Erkan Kayacan, now a postdoctoral researcher at the Massachusetts Institute of Technology. “But it’s hard to make robotic systems that can count plants autonomously: the fields are vast, the data can be noisy (unlike benchmark datasets), and the robot has to stay within the tight rows in the challenging under-canopy environment.”

    On Illinois News: http://emails.illinois.edu/newsletter/176843.html

    Media Release - Agricultural robot may be ‘game changer’ for crop growers, breeders

    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.

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

    Embedded High Precision Control Algorithm for an Ultra-Compact 3D Printed Field Robot

    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.

    More information: Erkan Kayacan, Zhongzhong Zhang and Girish Chowdhary, "Embedded High Precision Control and Corn Stand Counting Algorithms for an Ultra-Compact 3D Printed Field Robot", Proceedings of Robotics: Science and Systems (RSS), Pittsburgh, Pennsylvania, USA, 2018. Best Systems Paper Award.

    Embedded Corn Stand Counting Algorithm for an Ultra-Compact 3D Printed Field Robot

    A real-time machine vision algorithm is designed to enable an ultra-compact ground robot to count corn stands by driving through fields autonomously. A compact deep learning model (MobileNet) is used to recognize corn stalks, and a vision-based motion estimate technique is used to determine the relative motion between the sensor on the moving robot and the corn stalks in view of minimizing double-counting. Robot predictions agree well with the ground truth with the mean accuracy 89:74% across the growing season and the correlation coefficient R=0.96.

    More information: Erkan Kayacan, Zhongzhong Zhang and Girish Chowdhary, "Embedded High Precision Control and Corn Stand Counting Algorithms for an Ultra-Compact 3D Printed Field Robot", Proceedings of Robotics: Science and Systems (RSS), Pittsburgh, Pennsylvania, USA, 2018. Best Systems Paper Award.

    Media Release - Chicago Tribune - This self-driving robot from U. of I. could shape the future of farming

    Experts from the University of Illinois at Urbana-Champaign have built a robot that can wheel itself around farms to monitor plants — an invention that could let farmers collect data on crops cheaper and easier than ever before.

    On Chicago Tribune: http://www.chicagotribune.com/bluesky/originals/ct-farm-robot-uiuc-bsi-20170419-story.html

    Media Release - Real Robots: Meet Your Next Farm Hand

    Terra-Mepp takes images and collect microclimate data for each plant. Using these data, software constructs a 3D image to predict the yield of each plant from the ground up. Through genomic technologies, top-yielding plant traits (phenotype) are linked to their corresponding genes (genotype) to increase crop productivity.

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

    Media Release - 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

    B2. Mohit Mehndiratta, Erkan Kayacan, Siddharth Patel, Erdal Kayacan and Girish Chowdhary, "Learning-based Fast Nonlinear Model Predictive Control for Custom-made 3D Printed Ground and Aerial Robots", Handbook of Model Predictive Control, Editors: Sasa V. Rakovic, William S. Levine, pp. 581-605, 2019, Springer International Publishing. PDF URL

    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

    J21. Erkan Kayacan, “Closed-Loop Error Learning Control for Uncertain Nonlinear Systems With Experimental Validation on a Mobile Robot", IEEE/ASME Transactions on Mechatronics (In Press) PDF URL

    J20. Erkan Kayacan, Girish Chowdhary, “Tracking Error Learning Control for Precise Mobile Robot Path Tracking in Outdoor Environment", Journal of Intelligent & Robotic Systems, vol. 95, no. 3-4, pp 975–986, 2019. PDF URL

    J19. Sierra N. Young, Erkan Kayacan, and Joshua Peschel, "Design and Field Evaluation of a Ground Robot for High-Throughput Phenotyping of Energy Sorghum", Precision Agriculture, vol. 20, no. 4, pp 697–722, 2019. PDF URL

    J18. Erkan Kayacan, “Sliding Mode Control for Systems with Mismatched Time-Varying Uncertainties via a Self-Learning Disturbance Observer", Transactions of the Institute of Measurement and Control, vol. 41, no. 7 pp. 2039–2052, 2019. PDF URL

    J17. Erkan Kayacan, “Sliding Mode Learning Control of Uncertain Nonlinear Systems with Lyapunov Stability Analysis", Transactions of the Institute of Measurement and Control, vol. 41, no. 6, pp. 1750-1760, 2019. PDF URL

    J16. Erkan Kayacan, Thor I. Fossen, “Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers", Asian Journal of Control, vol. 21, no. 3, pp. 1064-1076, May 2019. PDF URL

    J15. Ardashir Mohammadzadeh and Erkan Kayacan, “A Non-singleton Type-2 Fuzzy Neural Network with Adaptive Secondary Membership for High Dimensional Applications", Neurocomputing, vol. 338, pp. 63-71, 2019. PDF URL

    J14. Erkan Kayacan, Sierra N. Young, Joshua M. Peschel and Girish Chowdhary, “High Precision Control of Tracked Field Robots in the Presence of Unknown Traction Coefficients", Journal of Field Robotics, vol. 35, no. 7, pp. 1050-1062, 2018. PDF URL

    J13. Erkan Kayacan, Wouter Saeys, Herman Ramon, Calin Belta, and Joshua M. Peschel, "Experimental Validation of Linear and Nonlinear MPC on an Articulated Unmanned Ground Vehicle", IEEE/ASME Transactions on Mechatronics, vol. 23, no. 5, pp. 2023- 2030, 2018. PDF URL

    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

    C13. Erkan Kayacan, Shinkyu Park, Carlo Ratti and Daniela Rus, "Online System Identification Algorithm without Persistent Excitation for Robotic Systems: Application to Reconfigurable Autonomous Vessels, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Accepted PDF

    C12. Erkan Kayacan, Shinkyu Park, Carlo Ratti and Daniela Rus, "Learning-Based Nonlinear Model Predictive Control of Reconfigurable Autonomous Robotic Boats: Roboats, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Accepted PDF

    C11. Shinkyu Park, Erkan Kayacan, Carlo Ratti and Daniela Rus, "Coordinated Control of a Reconfigurable Multi-Vessel Platform: Robust Control Approach", 2019 International Conference on Robotics and Automation (ICRA). PDF

    C10. Erkan Kayacan, Zhongzhong Zhang and Girish Chowdhary, "Embedded High Precision Control and Corn Stand Counting Algorithms for an Ultra-Compact 3D Printed Field Robot", Proceedings of Robotics: Science and Systems (RSS), Pittsburgh, Pennsylvania, USA, 2018. Best Systems Paper Award PDF URL

    C9. Mohit Mehndiratta, Erkan Kayacan, Erdal Kayacan*, "A Simple Learning Strategy for Feedback Linearization Control of Aerial Package Delivery Robot", 2018 IEEE Conference on Control Technology and Applications (CCTA), Copenhagen, 2018, pp. 361-367. PDF URL

    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: School of Mechanical & Mining Engineering, Mansergh Shaw Building (45), The University of Queensland, Queensland 4072 Australia.
    Email: {the initial letter of my name} dot {my surname} at {uq} dot {edu} dot {au}