This paper presents the output feedback control design for a tracking problem of a small-scale helicopter in the presence of uncertainties. The dynamics of the helicopter is an underactuated mechanical system and the form of control inputs is nonaffine. A time-scale approach is suggested to cope with underactuated mechanical systems to overcome lack of the number of inputs. A newly developed dynamic inversion with projection is used to deal with nonaffine control inputs to increase the region of attraction as compared to linearized inputs, i.e., an affine control input form and to deal with actuator's constraints and peaking in estimates from an Extended High-Gain Observer. The Extended High-Gain Observer is employed to quickly estimate model uncertainties and external disturbances. The singular perturbation method is used to analyze the stability of the closed-loop system in a multi-time scale structure. Based on the stability analysis, the design procedure for the proposed algorithm is presented for practical implementation. The effectiveness of the proposed control algorithm is shown via numerical simulations as well as experimental tests with a small-scale helicopter in an outdoor environment.
|Publication status||Published - 2021 Nov|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT). (No. 2021R1A2B5B01002620 ).
Jongeun Choi received the B.S. degree in mechanical design and production engineering from Yonsei University, Seoul, South Korea, in 1998, and the M.S. and Ph.D. degrees in mechanical engineering from the University of California at Berkeley, in 2002 and 2006, respectively. He is currently the director of the Machine Learning and Control Systems Laboratory and a Professor with the School of Mechanical Engineering, Yonsei University. Since 2020, he has been the Graduate Program Chair of the School of Mechanical Engineering, Yonsei University. Since 2019, he has been the Chairperson of the Department of Vehicle Convergence Engineering, Yonsei University, funded by Hyundai Motor Company. From 2020, he is affiliated with the Department of Artificial Intelligence, Yonsei University. Prior to joining Yonsei University, he worked for ten years as an Associate Professor from 2012 to 2016 and an Assistant Professor from 2006 to 2012, with the Department of Mechanical Engineering, and also with the Department of Electrical and Computer Engineering, Michigan State University. His current research interests include machine learning, systems and control, system identification, and Bayesian methods with applications to autonomous robots, self-driving vehicles, mobile sensor networks, (physical) human and robot interaction, and medical machine learning. He received the Best Conference Paper Award at the 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), in 2015. His articles were finalists for the Best Student Paper Award at the 24th American Control Conference (ACC), in 2005, and the Dynamic System and Control Conference (DSCC), in 2011 and 2012. He serves as a Guest Editor with a two-year term (2021 and 2022) for the IEEE/ASME TMECH/AIM Focused Section on Emerging Topics, and he serves as an Associate Editor for 2021 IEEE International Conference on Robotics and Automation. He served as an Associate Editor for the IEEE Robotics and Automation letters (RA-L), in 2018, Journal of Dynamic Systems, Measurement and Control (JDSMC) during 2014–2019, and International Journal of Precision Engineering and Manufacturing (IJPEM) during 2017–2018. He served as a Senior Editor for Ubiquitous Robots (UR), in 2020. He was a recipient of an NSF CAREER Award, in 2009. He is a member of ASME and IEEE.
© 2021 Elsevier Ltd
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering