Learning-based Control of Underactuated Balance Robots
Underactuated balance robots have more degrees of freedom than the number of control inputs and they perform the balancing and tracking tasks simultaneously, such as rotational inverted pendulums, bicycles and bipedal walkers, etc. The balancing task requires the robot to maintain its balance around unstable equilibrium points, while the tracking task requires following desired trajectories. In this talk, I first review the model-based control design of the underactuated balance robots. Balance equilibrium manifold is used to capture the external trajectory tracking and internal balance performance. I will then present a machine learning model-based control for underactuated balance robots. Gaussian process is used to obtain the estimation of the systems dynamics and the learning process is obtained without need of prior physical knowledge nor successful balance demonstrations. Additional attractive property of the design includes the guaranteed stability and closed-loop performance. Experiments from a Furuta pendulum and a bikebot are used to demonstrate the performance of the learning-based control design.
Professor Jingang Yi received the B.S. degree in electrical engineering from Zhejiang University in 1993, the M.Eng. degree in precision instruments from Tsinghua University in 1996, and the M.A. degree in mathematics and the Ph.D. degree in mechanical engineering from the University of California, Berkeley, in 2001 and 2002, respectively. He is currently a Full Professor in mechanical engineering and a Graduate Faculty member in electrical and computer engineering at Rutgers University. His research interests include human-robot interactions and assistive robotics, autonomous robotic and vehicle systems, dynamic systems and control, mechatronics, automation science and engineering, with applications to biomedical, transportation and civil infrastructure systems. Prof. Yi is a Fellow of American Society of Mechanical Engineers (ASME) and a Senior Member of IEEE. He has received several awards, including the 2018 Japan Society for the Promotion of Science (JSPS) Invitational Fellowship for Research, 2017 Rutgers Chancellor’s Scholars, 2014 ASCE Charles Pankow Award for Innovation, the 2013 Rutgers Board of Trustees Research Fellowship for Scholarly Excellence, and the 2010 NSF CAREER Award. He has coauthored several best papers in IEEE Transactions on Automation Science and Engineering and at IEEE/ASME AIM, ASME DSCC, and IEEE ICRA, etc. He currently serves as a Senior Editor for IEEE Transactions on Automation Science and Engineering and Editor-in-Chief of Conference Editorial Board for IEEE International Conference on Automation Science and Engineering (CASE). He also served as Associate Editor of IFAC journals Control Engineering Practice, Mechatronics, IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Automation Science and Engineering, IEEE Robotics and Automation Letters, and ASME Journal of Dynamic Systems, Measurement and Control and a Senior Editor of IEEE Robotics and Automation Letters.