Keynote Speakers




Keynote Speakers-ICEET 2022

Keynote Speaker I

Prof. Francesco Bullo, Professor, Department of Mechanical Engineering
College of Engineering, University of California, Santa Barbara, USA
IEEE Fellow, IFAC Fellow, SIAM Fellow


Biography: Francesco Bullo is a Professor with the Mechanical Engineering Department and the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was previously associated with the University of Padova (Laurea degree in Electrical Engineering, 1994), the California Institute of Technology (Ph.D. degree in Control and Dynamical Systems, 1999), and the University of Illinois. He served on the editorial boards of IEEE, SIAM, and ESAIM journals and as IEEE CSS President. His research interests focus on network systems and distributed control with application to robotic coordination, power grids and social networks. He is the coauthor of “Geometric Control of Mechanical Systems” (Springer, 2004), “Distributed Control of Robotic Networks” (Princeton, 2009), and “Lectures on Network Systems” (Kindle Direct Publishing, 2021, v1.5). He received best paper awards for his work in IEEE Control Systems, Automatica, SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, and IEEE Transactions on Control of Network Systems. He is a Fellow of IEEE, IFAC, and SIAM.

Speech Title: Perspectives on Contraction Theory and Neural Networks

Abstract: Basic questions in dynamical neuroscience and machine learning motivate the study of the stability, robustness, entrainment, and computational efficiency properties of neural network models. I will present some elements of a comprehensive contraction theory for neural networks. Using nonEuclidean norms I will review recent advances in analyzing and training a class of recurrent/implicit models.

Keynote Speaker II

Prof. Makoto IWASAKI, Nagoya Institute of Technology, Japan
Dr. Eng., IEEE Fellow, Co-EiC of IEEE/TIE
IEEE/IES Vice President for Planning and Development
Member of Science Council of Japan (SCJ)

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: High Trajectory Tracking Performance of Industrial Robots by Iterative Learning Control

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 an improvement approach of trajectory tracking performance of multi-axis robot manipulator, where an iterative learning control framework is especially applied as one of practical and/or promising approaches to improve the robot motion performance. Actual issues and relevant solutions for the robot trajectory control performance are clarified and, then, a practical controller design for the iterative learning approach, including the stability analyses, is presented to improve the trajectory tracking performance. In this speech, the effectiveness of the proposed controller design is discussing for an actual multi-axis robot manipulator, comparing to the conventional tracking control approaches.


Plenary Speaker I

Prof. Rezia Maria Molfino, University of Genova, Italy
President of SIRI (Italian Association of Robotics and Automation) from 2000 to 2016; then Vice President
President of IIS (Italian Institute of Welding) from 2013 to 2016, then Past-President
National Coordinator at IFR (International Federation of Robotics


Biography: She has carried out interdisciplinary research activities in the fields of complex mechatronic systems including machine mechanics, sensory-actuation, regulation and control, intuitive interfaces, expert systems, AI in particular with reference to the fields of Robotics and Flexible automation.
Main research topics: smart instrumental  robot design, kinematic and dynamic modelling and simulation; intelligent manipulation and soft grasping; surgical robots, climbing robots, extreme robotics; intelligent flexible manufacturing.
Author or co-author of some 250 publications in the area of robotics and manufacturing systems.
Referee of several international journals on robotics and industrial automation. CV @

Speech Title: From Mobile Robots to Smart Vehicles

Abstract: The main technologies that are a common base of mobile robots and smart vehicles are first introduced. Then  some example smart vehicles developed at the PMARlab (Design and Measurement for Automation and Robotics) of University of Genoa are presented. In particular we talk about:
· A personal intelligent city accessible vehicle  that can be used by normally able people as well as by elder and differently able. Ergonomics, comfort, stability, assisted driving, eco-sustainability, parking and mobility dexterity as well as vehicle/infrastructures intelligent networking are the main drivers of its design.
·  A new paradigm to freight transport: a freight urban robotic vehicle whose characteritics are: small size, zero emission and zero noise, intelligent behavior, multi-functional, transport optimization, intuitive human machine interface.
·  A new concept humanitarian demining low cost vehicle
·  A robotic fleet for last mile delivery services in city centers
The last part is dedicated to megatrends and innovation perspectives in the field.


Plenary Speaker II

Prof. Qianchuan Zhao, Department of Automation, Tsinghua University, Beijing, China


Biography: Prof.Qianchuan Zhao received the B.E. degree in automatic control in July 1992, the B.S. degree in applied mathematics in July 1992, and MS and Ph.D. degrees in control theory and its applications in July 1996, all from Tsinghua University, Beijing, China. He is currently a Professor and Director of the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. His current research focuses on the modeling, control and optimization of complex networked systems. He has published more than 80 research papers in peer-reviewed journals and conferences. Dr. Zhao is an editor for the IEEE Transactions on Automation Science and Engineering and Editor-in-Chief of the journal Results in Control and Optimizaiton. He was awarded The National Science Fund for Distinguished Young Scholars of China in 2014.

Speech Title: Online Trajectory Planning for the Planetary Powered-Descent Guidance

Abstract: The powered descent guidance problem for planetary landing is finding a fuel-optimal trajectory that takes a vehicle from the given initial position to a prescribed final location with zero speed.  This problem is also known as soft landing problem and could be solved offline as an optimal control problem. The problem is very important for planetary exploration and reuse of commercial rockets.  In this talk, we will review recent offline and online solution to this important and challenging problem. We will focus on computation efficient solutions to the problem and cover a key convexification technique, and point out connections between model predication control (MPC), second-order cone programming (SOCP) problems and efficient solutions.


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