Control for Intelligent Manufacturing – a multi-scale challenge

Abstract

China Industry 2025 will require full automation in all sectors from customer up to the production. This will bring great challenge to all sectors in manufacturing systems. All the device and systems in the future manufacturing should have capabilities of sensing and basic intelligence for control and adaptation. This will require the control action to be distributed, and to be integrated with different approaches including system design and intelligence-based method.

The presentation will discuss several fundamental issues related to the intelligent manufacturing.

• For the multi-time scale system, the dynamic design should be conducted to make the system robust to disturbance without control at the fast scale, while at the slow scale, the statistics-based control will be designed to maintain the consistent performance. Integrated design and control has been a challenge.
• For the spatially distributed dynamic system, the uniform temporal performance is required over the entire spatial domain. This strong space/time coupled dynamics makes the modeling and control extremely difficult, particularly under the limited sensing and actuating. The spatio-temporal modeling and control can be effectively developed using space/time separation based intelligent approach.
• Intelligent manufacturing will need the decision system for the high-level supervision and management. In difference to the low-level control action, the high-level decision is required to handle human knowledge under uncertainties. The probabilistic-fuzzy system would be a useful platform as it possesses the capability to produce linguistic rules under both deterministic uncertainty and stochastic variation.

Control action will be different at different scales. More design is required at the fast time scale, and more control is needed at the slow time scale. More quantitative action is required at the low-level operation, while more qualitative action is needed at the high-level supervision. The systematic work in this area should be built in a bottom-up approach, step by step from dynamic design, process control, intelligent supervision, up to plant-wide management control etc.. This is a multi-scale challenge.

Biography

Han-Xiong LI (李涵雄) received his B.E. degree in aerospace engineering from the National University of Defence Technology, China, M.E. degree in electrical engineering from Delft University of Technology, Delft, The Netherlands, and Ph.D. degree in electrical engineering from the University of Auckland, Auckland, New Zealand. Currently, he is a full professor in the Department of Systems Engineering and Engineering Management, the City University of Hong Kong. Over the last thirty years, he has had opportunities to work in different fields, including military service, investment, industry, and academia. His current research is in the field of intelligent manufacturing, with special interest on system intelligence and control, distributed parameter systems, intelligent learning and decision informatics. He authored 2 books and published over 180 SCI journal papers with h-index 34 (ISI web of science). He was rated as highly cited Chinese scholar by Elsevier in 2014 & 2015.Dr. Li serves as Associate Editor for IEEE Transactions on Systems, Man & Cybernetics: Systems (2016 – ), IEEE Transactions on Cybernetics (2002 – 2016), and IEEE Transactions on Industrial Electronics (2009-2015). He was awarded the Distinguished Young Scholar (overseas) by the China National Science Foundation in 2004, a Chang Jiang professor by the Ministry of Education, China in 2006, and a national professorship in China Thousand Talents Program in 2010. He serves as the distinguished expert for Hunan Government and China Federation of Returned Overseas. He is a fellow of the IEEE.