Prof. Edwin K. P. Chong
(Keynote Speaker)

IEEE Fellow
Colorado State University, USA

Keynote Lecture: Performance Guarantees for AI-based Automated Decision Making and Control

Abstract: Automated decision-making and control methods based on artificial intelligence (AI) have now demonstrated super-human performance in multiple domains, like playing strategic games (Weiqi or Go, chess, poker, etc.). A mathematical framework underlying such approaches is to approximate the optimal value function, founded on stochastic optimal control theory and Bellman's principle for Markov decision processes. But performance guarantees for such solutions have remained elusive. This leaves the performance evaluation of AI-based automated decision making reliant on extensive simulation or experimentation. In this talk, we describe a framework to provide guaranteed performance bounds. The approach involves computationally feasible analysis of the objective function in an optimization problem associated with a given decision scheme or control policy.

Biography: Edwin K. P. Chong received the B.E. degree with First Class Honors from the University of Adelaide, South Australia, in 1987 and the M.A. and Ph.D. degrees in 1989 and 1991, respectively, both from Princeton University, where he held an IBM Fellowship. He joined the School of Electrical and Computer Engineering (ECE) at Purdue University in 1991. Since August 2001, he has been a Professor of ECE and Professor of Mathematics at Colorado State University. He currently serves as Head of ECE. He coauthored the best-selling book, An Introduction to Optimization (5th Edition, Wiley-Interscience, 2023). He received the NSF CAREER Award in 1995 and the ASEE Frederick Emmons Terman Award in 1998. He was a co-recipient of the 2004 Best Paper Award for a paper in the journal Computer Networks. In 2010, he received the IEEE Control Systems Society (CSS) Distinguished Member Award. He was the founding chairman of the IEEE Control Systems Society Technical Committee on Discrete Event Systems and was an IEEE Control Systems Society Distinguished Lecturer. He was an inaugural Senior Editor of the IEEE Transactions on Automatic Control. He was the General Chair for the 2011 Joint 50th IEEE Conference on Decision and Control and European Control Conference. He was President of IEEE CSS in 2017. He is a Fellow of IEEE, AAAS, AAIA, and AIIA.

Prof. Shugen Ma
(Keynote Speaker)

IEEE Fellow
The Hong Kong University of Science and Technology (Guangzhou), China

Keynote Lecture: Bioinspired Intelligent Snake Robots

Abstract: Embodied Intelligence in a Multi-DOF Robot Abstract: Nature systems that have bodies with many degrees of freedom are often considered the ultimate models for machines. To confer the motion performance advantage of animal systems on robotic machines, we conducted in-depth studies on the motion characteristics of biological systems at the biomechanical level. We then used the insights that we obtained to develop intelligent biomimetic robots to achieve "intelligence," "environment adaptation," "flexibility," and "energy-saving." In this talk, I will introduce the bioinspired snake robots we have developed and discuss the evolution of their control methods, from shape-based to neural oscillator-based and then to embodied intelligence, to endow snake robots with motion intelligence.

Biography: Shugen Ma (IEEE Fellow, AAIA Fellow, JSME Fellow, Foreign Fellow of the EAJ) received his Ph.D. in Mechanical Engineering Science from the Tokyo Institute of Technology in 1991. Following this, he was with Komatsu Ltd. as a Researcher from 1991 to 1992 and then as a Visiting Scholar with the University of California, Riverside. He continued his academic career in Japan, joining the Department of Systems Engineering at Ibaraki University as an Assistant Professor in 1993. In 2005, he made a significant move to Ritsumeikan University as a Professor. In 2023, he further expanded his experience by joining the Robotics and Autonomous Systems Thrust of Systems Hub as a Professor at the Hong Kong University of Science and Technology (Guangzhou). His research interests include the design and control of environment-adaptive robots, field robotics, and Bio-robotics. He has published over 500 papers in refereed professional journals and international conference proceedings. He has also developed over 50 novel robot systems, filed over 80 patents, and supervised 45 Ph.D. students and over 100 M.Phil. students to graduation. For this achievement, he has been featured in the list of the world’s top 2% of scientists published by Stanford University.‬‬‬

He is the General Chair of IROS2022 in Kyoto, founded the ROBIO conference in 2004, and served as the General Chair of ROBIO 2004, ROBIO 2010, and ROBIO 2016. He was/is an Associate Editor of the IEEE Transaction on Robotics from 2003 to 2007, an Editor of Advanced Robotics, and an Associate Editor of Biomimetic Intelligence and Robotics, serving many societies and conferences.

Prof. Graziano Chesi
(Keynote Speaker)

IEEE Fellow
The University of Hong Kong, HKSAR, China

Keynote Lecture: Response Peak of Structured Polytopic Systems via LMIs

Abstract: A fundamental and challenging problem in systems analysis and control consists of determining the response peak of a dynamical system. This talk addresses the problem of determining the response peak of a linear system whose system matrices are rational functions of an uncertainty vector constrained into a convex bounded polytope. The uncertainty can be time invariant, bounded rate time varying or arbitrarily time varying. The input of the system can be any signal obtainable as the impulse response of a linear time invariant (LTI) system. An approach is proposed for obtaining upper bounds of the sought peak by solving convex optimization problems with linear matrix inequality (LMI) constraints based on the construction of a structured polynomial Lyapunov function in the state and in the uncertainty. A priori and a posteriori conditions for establishing optimality of the obtained upper bounds are also provided. Some numerical examples illustrate the use and potentialities of the proposed approach.

Biography: Graziano Chesi is a full professor at the Department of Electrical and Electronic Engineering of the University of Hong Kong. He received the Laurea in Information Engineering from the University of Florence and the PhD in Systems Engineering from the University of Bologna. He served as associate editor for various journals, including Automatica, the European Journal of Control, the IEEE Control Systems Letters, the IEEE Transactions on Automatic Control, the IEEE Transactions on Computational Biology and Bioinformatics, and Systems and Control Letters. He founded the Technical Committee on Systems with Uncertainty of the IEEE Control Systems Society. He also served as chair of the Best Student Paper Award Committees of the IEEE Conference on Decision and Control and the IEEE Multi-Conference on Systems and Control. He authored the books "Homogeneous Polynomial Forms for Robustness Analysis of Uncertain Systems" and "Domain of Attraction: Analysis and Control via SOS Programming". He is a Fellow of the IEEE, AAIA and AIIA.

Prof. Haibin DUAN
(Keynote Speaker)

Beihang University, China

Keynote Lecture: Incentive and Convergence in Bird Flock Intelligence

Abstract: Nature is a rich source of human creativity. On the basis of a series of flight experiments on bird flock behavior in nature, the internal mechanism of different behavior phenomena of birds is analyzed through collecting and processing the experimental data of different flight behavior of birds, and the bird flock intelligence incentive and convergence behaviors are modeled. The positive and negative feedback mechanism model of bird flock intelligence emergence is constructed. The mapping relationship between the bird flock intelligence incentive and convergence and the flight of unmanned aerial vehicles (UAVs) is studied. Inspired by the different behavior models of bird flock intelligence incentive and convergence, the UAV cooperative searching method based on the bird flock intelligence incentive and the UAV swarm countermeasure method based on the bird flock intelligence convergence are proposed. The recent progresses in incentive and convergence in bird flock intelligence will also be highlighted.

Biography: Haibin Duan is currently a professor with the School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China. He is a Fellow of Chinese Association of Automation (CAA).

He received the National Science Fund for Distinguished Young Scholars of China in 2014. He was also enrolled in the Chang Jiang Scholars Program of China in 2018, Scientific and Technological Innovation Leading Talent of “Ten Thousand Plan”-National High Level Talents Special Support Plan in 2017, and Top-Notch Young Talents Program of China in 2012, Program for New Century Excellent Talents in University of China in 2010, and Beijing NOVA Program in 2007.

He is the director of Technical Committee on Guidance Navigation and Control (TCGNC), Chinese Society of Aeronautics and Astronautics (CSAA), and the director of Technical Committee on Unmanned Aerial Systems Autonomous Control (TCUASAC), Chinese Association of Automation (CAA). He is the Editor-in-Chief of Guidance, Navigation and Control, deputy Editor-in-Chief of Acta Automatica Sinica, Associate Editor of the IEEE Transactions on Cybernetics, IEEE Transactions on Circuits and Systems I: Regular Papers and IEEE Transactions on Circuits and Systems II: Express Briefs. He is the general chair of 2026 International Conference on Guidance, Navigation and Control (ICGNC2026) in Guilin, China. He has authored or coauthored more than 90 publications and 4 monographs. His current research interests are bio-inspired intelligence, and unmanned systems swarm autonomous control.

Prof. Toshio Fukuda

Meijo University,Japan

Prof. Dan Zhang

Hong Kong Polytechnic University, HKSAR, China

Prof. Feng Gao

Shanghai Jiaotong University, China

Prof. Kenji Suzuki

Tokyo Institute of Technology, Japan

Prof. Hujun Yin

The University of Manchester, UK

Prof. Sheng Guo

Beijing Jiaotong University, China

Prof. Hongliu Yu 

University of Shanghai for Science and Technology, China

Prof. Jinsong Bao

Donghua University, China

Prof. Songyi Dian

Sichuan University, China

Prof. Bin He 

Shanghai University, China

Dr. Haijun Shan

Zhejiang Lab, China