Abstract

In robust controller design, controllers intend to achieve the best control performance in the presence of the worst uncertainties, and the use of a high controller gain is the general method to handle the effect of uncertainties in nonlinear control theory. However, such a strategy causes massive control actions, so very powerful actuators are demanded to perform unnecessarily large control actions. Furthermore, the robust control performance is mostly obtained at a price of sacrificing the nominal control performance of the system, because the nominal control performance is not taken into account in robust controller design. Therefore, a control method is required to maintain the nominal control performance in the absence of uncertainties and exhibit robust control performance in the presence of uncertainties. In this talk, I will demonstrate two methods: 1) a real-time optimization-based nonlinear observer to estimate not only unmeasurable states but also unknown parameters 2) a self-learning disturbance observer. In the first method, a nonlinear moving horizon estimator is designed for systems that have constrained states and parameters. In the second method, the basic nonlinear disturbance observer is used in the estimation scheme for the self-learning disturbance observer to provide a conventional estimation law, which is used as being the learning error for the neuro-fuzzy system (NFS). Thus, the NFS learns uncertainties, and eventually takes the overall control of the estimation signal completely in a very short time and gives unbiased estimation results for the disturbance. A few real-time implementations on custom-design agricultural robotic systems will also be introduced. 

Bio

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 for 5 months under supervision of Prof. Calin Belta. After his Ph.D., he became a Postdoctoral Researcher with Delft Center for Systems and Control, Delft University of Technology, The Netherlands. He is currently a Postdoctoral Researcher with Coordinated Science Lab and Distributed Autonomous Systems Lab in the University of Illinois at Urbana-Champaign under supervision of Assist. Prof. Girish Chowdhary. His research interests center around real-time optimization-based control and estimation methods, and learning algorithms with a heavy emphasis on applications to autonomous systems.