Prof. Ikuro Mizumoto
Kumamoto University, Japan
Robot, Control and Instrumentation
Division of Industrial Fundamentals
Faculty of Advanced Science and Technology
Title:
Output Feedback based Adaptive Control and Parallel Feedforward Compensator
-Design and Applications-
Abstract:
The system is said to be `almost strictly positive real (ASPR)' if there exists a static output feedback such that the resulting closed-loop system is strictly positive real (SPR). The conditions for the system to be ASPR are given by (1) the system is minimum-phase, (2) the system has a relative degree of 1, (3) the high-frequency gain of the system is positive. It is well recognized that, under the ASPR condition, one can easily design stable adaptive output feedback control systems and/or adaptive output tracking control systems which have simple structure utilizing only output of the controlled system. Under the ASPR conditions, the ASPR based adaptive output feedback controls including simple adaptive control (SAC) can reduce the number of adjusting parameters and can design the controller without the knowledge of the order of the controlled system. Therefore, the ASPR based adaptive output feedback controls have strong robustness with respect to system's uncertainties and disturbances so that several kinds of practical applications have been reported. Unfortunately, however, since there are many systems that have relative degree greater than one and/or is not minimum phase, most practical systems do not satisfy these ASPR conditions. One of the simple solution to overcome the problem is to introducing a parallel feedforward compensator (PFC). By introducing the PFC to the controlled system, the difficulty to control uncertain system is drastically alleviated via simple adaptive output feedback based on ASPR properties.
In my talk, the basic design concept of the output feedback based adaptive control system is presented and the availability of the PFC is explained. The applicability of the output feedback based adaptive control is also shown with some application results.
Brief Biography:
Ikuro Mizumoto received the B.E. degree, the M.E. degree and the Dr. Eng. degree, all in mechanical engineering from Kumamoto University, Kumamoto, Japan, in 1989, 1991 and 1996 respectively. Since 1991 he has been with Kumamoto University, where he is currently a Professor of Robot, Control and Instrumentation Group, in Faculty of Advanced Science and Technology. In 2000, he has held a visiting position at University of Alberta for 7 months by the research fellowship funded from the Ministry of Education of Japan. He received Best Book Award in 2011 from SCIE for the book ‘Simple Adaptive Control’. His research interests over last few years have been adaptive control system design, robust adaptive control and output feedback based control for nonlinear systems and their applications.
Title:
Dealing with motions in the visual world - from insects to robotic vision systems
Abstract:
Animals, as small as insects, have amazing ability in exploring within this dynamic visual world. This ability has not been observed in, or easy to implement to, human made intelligent moving machines such as robots. It is interesting that visual neurons in insects respond to visual motion with obvious preferences – some are only responding to objects moving to left side or leftward movements, some are only interested in rightward movements, while other neurons can only be triggered by clockwise rotation, etc. By modelling these neurons and their presynaptic networks, we can not only contribute to further understanding of how animals visual systems or particular visual neurons work, but also step forward in developing new artificial intelligent vision systems fit for future robots.
In this talk, I will introduce the most recent work in my group on modelling specific visual neurons in insects with motion preferences, also our recent progresses in swarm robots.
Brief Biography:
Shigang Yue is a Professor (since 2012) in the School of Computer Science, University of Lincoln, United Kingdom. He received his PhD and MSc degrees from Beijing University of Technology (BJUT) in 1996 and 1993, and his BEng degree from Qingdao Technological University (1988). He worked in BJUT as a Lecturer (1996-1998) and an Associate Professor (1998-1999), also in City University of Hong Kong (MEEM) as a Senior Research Assistant (1998-1999). He was an Alexander von Humboldt Research Fellow (2000, 2001) working with Prof. Henrich in the Faculty of Computer Science, University of Kaiserslautern, Germany. Before joining the University of Lincoln as a Senior Lecturer (2007) and promoted to a Reader (2010), he held research positions in the University of Cambridge (2006-2007), Newcastle University (2003-2006) and the University College London (UCL) (2002-2003) respectively.
His research interests are mainly within the field of artificial intelligence, computer vision, robotics, brains, and neuroscience. He is particularly interested in biological visual neural systems, evolving of neural systems, neuromorphic vision chip and its applications in collision detection for vehicles, interactive systems, UAVs and ground robots. He has published more than 150 journal and conference papers in information theory, computer vision, artificial life, neural systems, neural evolution, swarm intelligence, vehicle collision detection, robotic navigation, robotic manipulation skills and dynamic simulations, many of them are in top tier high impact journals. He has chaired several international conferences. He is the founding director of the multiple discipliary group CIL in UoL. He is the deputy director of LCAS (Lincoln Centre for Autonomous Systems) which is one of the biggest robotics research centres in the Europe. He is the coordinator for several EU H2020 projects.