Title: Optimal Fully Actuated System Approach (FASA) Based Control Theory and Applications

Abstract: In this talk, the optimal fully actuated system approach (FASA) based theory is presented. First, the idea of the theory is given. Then, the required numerical optimal control method is introduced. Last but not least, three applications of the optimal FASA-based control are provided to show the effectiveness and advantages of the proposed theory. 

Bin Li is the professor and doctoral supervisor at School of Aeronautics and Astronautic, Sichuan University. He was selected for the National High-Level Talent Youth Program, the Sichuan Provincial Top Youth Talent Program, and Sichuan Provincial Distinguished Expert. He is a Senior Member of IEEE and serves as an editorial board member for top international journals IEEE Transactions on Neural Networks and Learning Systems and Applied Mathematical Modeling. His primary research focuses on optimization-based control theory and its applications in autonomous decision-making and control of unmanned aerial vehicles/swarms. He has published over 80 SCI-indexed journal papers, obtained more than 50 authorized national invention patents, and authored one English monograph. He has led key national projects such as Key Program of National Natural Science Foundation of China. He was awarded the 9th Youth Scientist Award by the Chinese Association of Automation.

报告人2：

Title:Distributed online resource allocation with free-in and free-out nodes

Abstract: In this talk, an online resource allocation problem with free-in and free-out nodes is introduced. A distributed online optimization algorithm is constructed for agents to achieve the minimization of their total cost while satisfying local box constraints and a global balancing equality constraint. In the developed algorithm, the agents update their decision variables and dual variables via the projected gradient method and dual averaging method, respectively. A dynamic regret and an accumulation of constraint violation are introduced as performance indices of evaluating the established algorithm.

Maojiao Ye received the B.Eng. degree in automation from the University of Electronic Science and Technology of China, Sichuan, China, in 2012 and the Ph.D. degree from Nanyang Technological University, Singapore, in 2016. She was a research fellow in the School of Electrical and Electronic Engineering at Nanyang Technological University from 2016 to 2017. She is currently a Professor with the School of Automaton, Nanjing University of Science and Technology. Her research interests include game theory, distributed optimization, and their applications.

Prof. Ye was a recipient of the Young Scientist Award from the Chinese Association of Automation in 2023, Guan Zhao-Zhi Award in the 36th Chinese Control Conference 2017, and the Best Paper Award in the 15th IEEE International Conference on Control and Automation 2019. She received the National Natural Science Fund for Excellent Young Scholars in 2022. She was selected into the 7th Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (CAST). Prof. Ye is an Associate Editor of IEEE Transactions on Industrial Informatics, IEEE/CAA Journal of Automatica Sinica, Control Engineering Practice, and IEEE CSS Conference Editorial Board. She is the Vice-Chair of IEEE IES Technical Committee on Network-Based Control Systems and Applications and Secretary of ACA Technical Committee on MetaSystems and MetaControl.

报告人3：

Title:Motion control of underactuated robots based on the fully actuated system approach and related applications

Abstract: In practice, many mechanical systems, such as naval vessels, cranes, and helicopters, are underactuated to reduce energy consumption and enhance flexibility. However, compounded by strong nonlinearity arising from state coupling, the underactuated nature and high-order unavailable states pose significant challenges to motion control (particularly for un-actuated states lacking independent actuators or kinematic constraints). This talk mainly discusses the method of rearranging nonlinear underactuated systems into high-order linear fully-actuated systems, and further introduces an adaptive control method based on the fully actuated system approach, as well as a universal and scalable analysis method. In addition, the fully actuated system approach is applied to pneumatic artificial muscle-actuated robots, where their disturbance rejection and hysteresis modeling problems are considered. Finally, comparative tests on hardware platforms verify the feasibility of the proposed methods based on the fully actuated system approach.

Ning Sun is a Young Scholar of the Changjiang Scholars Program and a professor with Nankai University, Tianjin, China, and the Shenzhen Research Institute of Nankai University, Shenzhen, China. He received the B.S. degree in measurement & control technology and instruments from Wuhan University, Wuhan, China, in 2009, and the Ph.D. degree in control theory and control engineering from Nankai University, Tianjin, China, in 2014; he was a Japan Society for the Promotion of Science (JSPS) Fellow from 2018 to 2019. His research interests include intelligent control for mechatronic/robotic systems with an emphasis on (industrial) applications. Dr. Sun received the 2021 IEEE Transactions on Industrial Electronics Outstanding Paper Award, the Machines 2021 Young Investigator Award, the 2019 Wu Wenjun Artificial Intelligence Excellent Youth Award, the ICCAR 2022 Young Scientist Award, the 2024 IEEE Transactions on Systems, Man, and Cybernetics: Systems Outstanding Associate Editor Award, the 2023 International Journal of Control, Automation, and Systems Best Associate Editor, and several outstanding journal/conference paper awards. He serves as an Associate Editor for several journals, including the IEEE Transactions on Industrial Electronics, IEEE Transactions on SMC: Systems, IEEE Transactions on Intelligent Transportation Systems, and IEEE/ASME Transactions on Mechatronics. He is a Senior Member of the IEEE.

报告人4：

Title:Lightweight learning model for industrial intelligent computing: Taking the energy industry as an example

Abstract: The deep integration of new-generation artificial intelligence (AI) technologies with the manufacturing industry is driving a profound industrial transformation. As a cornerstone of China’s energy supply system, coal plays a fundamental role in both energy security and system regulation. In alignment with the national "dual carbon" development strategy, the coal industry is gradually evolving from automation and informatization toward intelligentization. However, in practical production settings, the industry faces a series of challenges, such as difficulties in detecting key operational indicators like product quality and yield, time-varying working conditions, unclear mechanisms, complexity in control method design, and challenges in validating control systems due to intricate control structures. These issues present new challenges for the intelligent transformation of the energy and resources sector. This report introduces a lightweight machine learning model and, taking the coal industry as a case study, addresses its real-world intelligentization needs. By integrating data and domain knowledge, combining intelligent behavior with intelligent methodologies, and merging modeling with control, the report demonstrates how AI technologies can be applied to the coal preparation process. The goal is to achieve AI-driven modeling and operational optimization control for coal sorting. Finally, the report explores new opportunities and challenges brought by the industrial internet in the realm of intelligent computing.

Prof. Wei Dai is a Full Professor and Vice Dean of the School of Information and Control Engineering, China University of Mining and Technology, where he also supervises PhD candidates. He is a recipient of the National Young Talents Program, the Jiangsu Distinguished Young Scholars Fund, and the Jiangsu Excellent Young Scholars Fund. He has also been recognized as an Excellent Young Backbone Teacher under Jiangsu’s “Qinglan Project” and is a IEEE Senior Member. He currently serves as a council member of the Jiangsu Association of Automation and the Jiangsu Coal Society. His main research interests include AI-driven modeling and operational optimization control of complex process industrial systems, as well as next-generation AI methodologies such as federated learning and incremental learning. His research has been recognized with multiple awards, including the Second Prize of the Ministry of Education Natural Science Award, the First Prize of the Liaoning Patent Award, the Youth Science and Technology Award of the Chinese Association of Automation (CAA), the Youth Science and Technology Award of China Coal Society, the First and Second Prizes of CAA Natural Science Awards, the Second Prize of the CAA Science and Technology Progress Award, the Innovation Award (Individual) from the China Industry-University-Research Institute Collaboration Association, and the Third Prize of Jiangsu Science and Technology Award. He has successfully transferred four patents into application.