Keynote Speakers

Prof. Makoto IWASAKI (IEEE Fellow, IEEJ Fellow)

Nagoya Institute of Technology, Japan

Biography: Makoto Iwasaki received the B.S., M.S., and Dr. Eng. degrees in electrical and computer engineering from Nagoya Institute of Technology, Nagoya, Japan, in 1986, 1988, and 1991, respectively. He is currently a Professor at the Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology. As professional contributions of the IEEE, he has participated in various organizing services, such as, a Co-Editors-in-Chief for IEEE Transactions on Industrial Electronics since 2016, a Vice President for Planning and Development in term of 2018 to 2021, etc. He is IEEE fellow class 2015 for "contributions to fast and precise positioning in motion controller design".
He has received many academic, foundation, and government awards, like the Best Paper and Technical Awards of IEE Japan, the Nagamori Award, the Ichimura Prize, and the Commendation for Science and Technology by the Japanese Minister of Education, respectively. He is also a fellow of IEE Japan, and a member of Science Council of Japan.
His current research interests are the applications of control theories to linear/nonlinear modeling and precision positioning, through various collaborative research activities with industries.

Speech Title: GA-Based Practical System Identification and Auto-Tuning for Multi-Axis Industrial Robots

Abstract: Fast-response and high-precision motion control is one of indispensable techniques in a wide variety of high performance mechatronic systems including micro and/or nano scale motion, such as data storage devices, machine tools, manufacturing tools for electronics components, and industrial robots, from the standpoints of high productivity, high quality of products, and total cost reduction. In those applications, the required specifications in the motion performance, e.g. response/settling time, trajectory/settling accuracy, etc., should be sufficiently achieved. In addition, the robustness against disturbances and/or uncertainties, the mechanical vibration suppression, and the adaptation capability against variations in mechanisms should be essential properties to be provided in the performance.
The keynote speech presents a practical auto-tuning technique based on a genetic algorithm (GA) for servo controllers of multi-axis industrial robots. Compared to conventional manual tuning techniques, the auto-tuning technique can save the time and cost of controller tuning by skilled engineers, reduce performance deviation among products, and achieve higher control performance. The technique consists of two main processes: one is an autonomous system identification process, involving the use of actual motion profiles of a typical robot. The other is an autonomous control gain tuning process in the frequency and time domains, involving the use of GA, which satisfies the required tuning control specifications, e.g., control performance, execution time, stability, and practical applicability in industries. The proposed technique has been practically evaluated through experiments performed with an actual six-axis industrial robot.

Prof. Mingcong Deng, Tokyo University of Agriculture and Technology, Japan

Biography: Prof. Mingcong Deng received his BS and MS in Automatic Control from Northeastern University, China, and PhD in Systems Science from Kumamoto University, Japan, in 1997. He was with Kumamoto University; University of Exeter, UK; NTT Communication Science Laboratories; Okayama University. Now, he is with Tokyo University of Agriculture and Technology, Japan, as a professor. Prof. Deng specializes in three complementary areas: Operator based nonlinear fault detection and fault tolerant control system design; System design on human factor based robot control; Learning based nonlinear adaptive control. Prof. Deng has over 550 publications including 195 journal papers, in peer reviewed journals including IEEE Transactions, IEEE Press and other top tier outlets. Prof. Deng is a co-chair of agricultural robotics and automation technical committee, IEEE Robotics and Automation Society; also a chair of the environmental sensing, networking, and decision making technical committee, IEEE SMC Society. He was the recipient of 2014 & 2019 Meritorious Services Award of IEEE SMC Society, 2020 IEEE RAS Most Active Technical Committee Award (IEEE RAS Society). He is a member of The Engineering Academy of Japan

Speech Title: Learning & Operator based Control Design for Smart Materials Actuated & Sensed Nonlinear Systems

Abstract: Control design for nonlinear systems has been a key technology in many engineering fields. Especially learning based nonlinear control design is necessary to compensate nonlinear factors more efficiently. Recently, smart materials have been used as actuators and sensors in many nonlinear dynamic systems to realize the reduction in size and weight of the systems, such as piezoelectric elements, shape-memory alloy etc. In this talk, we show 1) nonlinear vibration control schemes for a wing plate system with piezoelectric actuators & sensors based on operator theory, 2) nonlinear vibration control for a flexible arm using an interactive Shape Memory Alloy actuation, 3) robust nonlinear vibration control for a L-type arm with piezoelectric actuator & sensor and linear motor. Further, some current results are shown to combine learning schemes.

Prof. Liangjing Yang, Zhejiang University-University of Illinois at Urbana-Champaign (ZJUI) Institute, China

Biography: Liangjing Yang is an assistant professor in Zhejiang University-University of Illinois at Urbana-Champaign (ZJUI) Institute, Zhejiang University (ZJU) where he is appointed as the Vice Director of Research Division for Data and Information Sciences. He is also the principal investigator leading the Intelligent Robot, Vision & Control research group (IRVC) in ZJUI. The group’s research vision is to advance the science and technology of robotics in a human-centric fashion with an emphasis in intuitive and interactive man-machine interface, especially in the biomedical and healthcare domains.
Liangjing Yang received the B.Eng. and M.Eng. degrees in mechanical engineering from the National University of Singapore (NUS). He obtained the D.Eng. degree from the University of Tokyo (UTokyo) before receiving a joint postdoctoral fellowship to work at the Singapore University of Technology and Design (SUTD), and Massachusetts Institute of Technology (MIT). His work on image mapping for 3D ultrasound-guided endoscopic procedures is featured in both engineering and medical journals. He also developed a robotic system for overlapping ablation of large liver tumor, which is published in a special issue on “Surgical and Interventional Medical Devices” of ASME/IEEE Transactions on Mechatronics. He holds two US patents one on a Robotic Surgical Training System, which was named “Best Innovation in Biomedical Application” in a challenge organized by National Instruments.

Speech Title: Human-Centered Immersive Robot-Man Coexistence

Abstract: As robots permeate all aspects of our lives, from addressing medical needs, to easing imminent social issues like aging workforces, the coexistence of man and robot is inevitable. Robot-Man Coexistence (RoManCe) is especially inevitable in biomedical and healthcare applications involving humans in the workspace of the robots. It is, therefore, meaningful to adopt a human-centric approach for robotics research looking beyond the pursuit of autonomy. Building upon our work in biomedical robotics, the Intelligent Robot Vision & Control (IRVC) group strives to advance human-centricity in the design of robotic systems by incorporating machine perception, collaborative control and immersive technology. The talk will focus on our research work to augment human user with immersive man-machine interface, hence, achieving intuitive and safe human-robot interaction in applications including, but not limited to, biomedical, healthcare and educational areas.