IFAC Workshop Series


IFAC Workshop Series

Workshop Series on Control Systems and Data Science Towards Industry 4.0

Organised by
Hong Kong Institute for Data Science, City University of Hong Kong,
School of Data Science, City University of Hong Kong

Co-sponsored by
International Federation of Automatic Control

The workshop series is supported by IFAC Activity Fund and Technical Committees of IFAC, namely TC 6.1 Chemical Process Control (chaired by Prof Rolf Findeisen) and TC 1.1 Modelling, Identification and Signal Processing (chaired by Prof Alessandro Chiuso)

NOC Chair: Prof S Joe QIN (CN)
NOC Co-Chair: Prof Furong GAO (CN)
IPC Chair: Prof Rolf FINDEISEN (DE)

IPC members:
Prof Alain BENSOUSSAN
Prof Richard BRAATZ
Prof Jie CHEN
Prof Alessandro CHIUSO
Prof Yining DONG
Prof Gang FENG
Prof Bhushan GOPALUNI
Prof Zhong-Ping JIANG
Dr Yuan JIN
Prof Jay H. LEE
Prof Qiang LIU
Prof Li QIU
Prof James RAWLINGS
Prof Victor ZAVALA
Prof Jin WANG
Prof Chunhua YANG
Prof Fengqi YOU
Dr Hong ZHAO
Prof Qinqin ZHU  

Data Science and Automatic Control Systems 
 19 March 2021 (Friday)
 Chemical and Biological Engineering Systems   
21 May 2021 (Friday)
 Electrical and Computer Engineering Systems   
9 July 2021 (Friday)

About the workshop

Industry 4.0 refers to the fourth industrial revolution using cyber-physical systems to produce breakthrough improvement in manufacturing productivity, safety, and competitiveness. It is driven by several emerging technological frontiers, including data science, robotics, augmented reality, digital twins, industrial internet of things (IIoT), cybersecurity, and artificial intelligence (AI). Of numerous success stories reported across all industries, machine learning and data analytics provide a ubiquitous tool to improve the decision-making and optimization process, so that intelligent decisions can be made. Along the journey of Industry 4.0, control systems theory and implementation must go through a generational transformation. Beyond the traditional control field, data science is emerging as a multidisciplinary field with tremendous recent development in theoretical foundations and expanded applications in both science and engineering. Applications include industrial data analytics, autonomous systems, energy analytics, economic data modelling, image sequence modelling, and high dimensional time-series analytics. Artificial intelligence and machine learning are seen as cornerstones for Industry 4.0 and smart manufacturing.

This workshop series is to be given by world-renowned scholars and industrial leaders in the intersection of control systems theory and data analytics. It is also to bring control theory and principles into the context of machine learning towards Industry 4.0. On one hand, how will the development of industrial internet of things (IIoT), smart and wireless sensors, wireless communications, and cloud-edge computing likely revolutionize control system theory and practice? On the other hand, as machine learning and data analytics penetrate to industrial applications, including chemicals, petrochemicals, energy, power grids, and pharmaceuticals, how can control systems theory help data scientists avoid running against innate performance limitations due to feedback?” 

Mobirise

Speakers

Prof S. Joe Qin

Prof S. Joe QIN

Dean and Chair Professor of Data Science, School of Data Science, City University of Hong Kong, Hong Kong
Director, Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong
Director, Centre for Systems Informatics Engineering, City University of Hong Kong, Hong Kong

Biography
Prof S. Joe Qin is currently Chair Professor of Data Science of the School of Data Science, and Director of Hong Kong Institute for Data Science at City University of Hong Kong. He is a Fellow of IFAC, AIChE, and IEEE. He is a recipient of the U.S. National Science Foundation CAREER Award, the 2011 Northrop Grumman Best Teaching award at Viterbi School of Engineering, the DuPont Young Professor Award, and recipient of the IFAC Best Paper Prize for a model predictive control paper published in Control Engineering Practice. He served as Senior Editor of Journal of Process Control, Editor of Control Engineering Practice, and Associate Editor for several journals. He has published over 400 international journal and conference papers. He received 30,000 Google Scholar citations with an h-index of 75. His research interests include data analytics, machine learning, process monitoring, predictive control, system identification, and predictive maintenance.

Prof Richard D. Braatz

Prof Richard D. BRAATZ

Edwin R. Gilliland Professor and Faculty Research Officer, Department of Chemical Engineering, Massachusetts Institute of Technology, United States of America

Biography
Prof Richard Braatz is currently the Edwin R. Gilliland Professor and Research Officer of Chemical Engineering at the Massachusetts Institute of Technology. He is a Fellow of IFAC, IEEE, AAAS, and AIChE, and a member of the U.S. National Academy of Engineering. He is a recipient of the Automatica Paper Prize, the IEEE Control Systems Society Transition to Practice Award, and the Donald P. Eckman Award from the American Automatic Control Council. He has served on the editorial boards of Automatica, Annual Reviews in Control, Journal of Process Control, and IEEE Transactions in Automatic Control, and was the Editor in Chief of IEEE Control Systems Magazine. He has co-authored more than 500 journal and conference papers. He received over 20,000 Google Scholar citations with an h-index of 73. His research interests include systems and control theory, process data analytics and machine learning, fault diagnosis, Industry 4.0, and advanced manufacturing systems.

Prof Jie CHEN

Prof Jie CHEN

Chair Professor, Department of Electrical Engineering, City University of Hong Kong, Hong Kong
Fellow of IEEE, AAAS, IFAC

Biography
Prof Jie Chen is currently Chair Professor of Electronic Engineering at City University of Hong Kong, Hong Kong. An elected Fellow of IFAC, IEEE, and AAAS, he was a recipient of NSF CAREER Award, 2004 SICE International Award, and 2006 NSF China Outstanding Overseas Young Scholar Award. He served as an Associate Editor and a Guest Editor for the IEEE Transactions on Automatic Control, a Guest Editor for IEEE Control Systems Magazine, an Associate Editor for Automatica, Journal of Control Theory and Applications, and the founding Editor-in-Chief for Journal of Control Science and Engineering. He is the author of several books. Currently, he serves on the IFAC Publication Committee and the IFAC Fellow Search Committee. His main research interests are in the areas of networked control and information theory, multi-agent systems, time-delay systems, linear multivariable systems theory, system identification, robust control, and in broad applications of control theory and techniques.

Prof Rolf FINDEISEN

Prof Rolf FINDEISEN

Prof. Dr.-Ing., Institut für Automatisierungstechnik (IFAT)
Chair of Systems Theory and Automatic Control, Otto-von-Guericke-University Magdeburg, Germany

Biography
Prof Rolf Findeisen heads the Systems Theory and Automatic Control Laboratory, Otto-von-Guericke University Magdeburg, Magdeburg, Germany, where he is also a full chaired Professor. He was the Chair of IEEE Control Systems Society International Affairs in 2012-2015, IPC Co-Chair of IFAC World Congress 2020 (Berlin), Head of Internet of Things activity group, IEEE TC Networks and Communications, International Programme Chair NMPC at University of Wisconsin in 2017 and delivered talks at GAMM Fachausschusses Dynamik und Regelungstheorie, plenary speaker of IFAC Workshop Nonlinear Model Predictive Control. In 2016, he was the plenary speaker of Congreso Nacional de Control Automatico, Mexico. He has served as associate editor of various journals. He was an associate editor of IEEE Control Systems Magazine, IEEE Transactions on Automatic Control, Optimal Control Applications and Methods and Processes. His research interests include the areas of Autonomous systems, predictive control, machine learning, learning and control, cyber-physical systems, uncertainty, robustness.

Prof Furong GAO

Prof Furong GAO

Chair Professor, Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong
Director, Center for Polymer Processing and Systems, Hong Kong University of Science and Technology, Hong Kong

Biography
Prof Furong Gao is Chair Professor of Chemical and Biological Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as Head of Division of Advanced Manufacturing and Automation, Director of Center for Polymer Processing and Systems. Before joining HKUST, he worked as a Senior Research Engineer at MOLDFLOW Co Ltd (now, a part of Autodesk Inc) in Melbourne, Australia. He has authored 6 books, 500 journal and conference papers, and 80 patents. He served or is serving as Editor, Associate Editor, or Editorial Board members of seven journals of his area. He has received over 10 times best paper award from national, international conferences or journals. He is a recipient of 1st class prize and 2nd class prize from the Chinese Ministry of Education. Prof Gao’s research interests are in the area of batch process modelling, monitoring and control and their applications to polymer processing.

Prof Zhong-Ping JIANG

Prof Zhong-Ping JIANG

Professor, Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, United States of America

Biography
Prof Zhong-Ping Jiang is Professor of the Department of Electrical and Computer Engineering at New York University. He is Fellow of IEEE, IFAC and CAA. He was awarded with the Clarivate Analytics Highly Cited Researcher in 2018. Prof. Jiang is a Deputy co-Editor-in-Chief of the IEEE/ CAA Journal of Automatica Sinica and the Journal of Control and Decision, an Editor for the International Journal of Robust and Nonlinear Control and has served as Senior Editor for the IEEE Control Systems Letters and Systems & Control Letters, Associate Editor and/or Guest Editor for several journals including Mathematics of Control, Signals and Systems, IEEE Transactions on Automatic Control, European Journal of Control, and Science China: Information Sciences. His research focuses on robust adaptive dynamic programming, learning-based optimal control, nonlinear control, distributed control and optimization, and their applications to computational and systems neuroscience, connected and autonomous vehicles, and cyber-physical systems.

Prof Jay H. LEE

Prof Jay H. LEE

Professor, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Korea
Director, Saudi Aramco-KAIST CO2 Management Center

Biography
Prof Jay H. Lee is with Korea Advanced Institute of Science and Technology (KAIST) as the former Head of the Chemical and Biomolecular Engineering Department. He is the founding director of Saudi Aramco-KAIST CO2 Management Center. He received the US NSF Young Investigator Award and AIChE CAST Computing in Chemical Engineering Award. He is a Fellow IFAC, IEEE, and AIChE. He is a member of both the Korean National Academy of Science and Technology (KAST) and the National Academy of Engineering Korea (NAEK). He was also the 29th Roger Sargent Lecturer in 2016. He published over 200 manuscripts in SCI journals with more than 16,000 Google Scholars citations with an h-index of 56. Prof Lee’s research interests are in the areas of state estimation, robust control, model predictive control, planning/ scheduling, and approximate dynamic programming with applications to energy systems and carbon management systems.

Prof Li QIU

Prof Li QIU

Professor, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong
Director of HKUST Shenzhen Cyber-Physical Systems Laboratory

Biography
Prof Li Qiu joined the Hong Kong University of Science and Technology in 1993, where he is now a Professor of Electronic and Computer Engineering. He is a Fellow of IFAC and IEEE. He co-authored Introduction to Feedback Control which was published by Prentice-Hall in 2009. He served as an associate editor of the IEEE Transactions on Automatic Control and an associate editor of Automatica. He was the general chair of the 7th Asian Control Conference. He was a Distinguished Lecturer from 2007 to 2010 and was a member of the Board of Governors in 2012 and 2017 of the IEEE Control Systems Society. He is vice president of Asian Control Association and the founding chairperson of the Hong Kong Automatic Control Association. His research interests include system, control, information theory, and mathematics for information technology, as well as their applications in manufacturing industry and energy systems.

Dr Hong ZHAO

Dr Hong ZHAO

Distinguished Technologist (Senior Director), Aspen Technology, United States of America

Biography
Dr Hong Zhao obtained his BSc, MSc and PhD degrees in Chemical Engineering & Process Control from Zhejiang University in Hangzhou, China, in 1982, 1985 and 1989 respectively. He is currently a Distinguished Technologist of Aspen Technology, Inc. located in Houston, USA. In 1986 – 1996, Dr Zhao worked as a Lecturer and Associate Professor at Zhejiang University, Visiting Scholars in Technical University of Denmark and University of Maryland. Since 1996, he joined industry and worked at NeuralWare, Inc. and Aspen Technology, Inc. for 24 years. Dr Zhao is a technology inventor and one of industrial leading practitioners in system identification, advanced process control and data analytics. He published over 20 international journal papers, conference papers and presentations and hold 14 patents.

Module 1  Topic Presentations

Presented by:
Prof S. Joe QIN

Abstract:

Machine learning and modern statistics are major foundation pillars of data science, which has enjoyed tremendous advances in both theoretical development and practical adoptions in the society. As the data-driven technologies move into the domain of industrial and engineering systems, systems theory and engineering could be another great application domain and a potential pillar of data science. This talk addresses the challenge in modeling multi-dimensional time series data from engineered and natural systems, where dynamics are contained in a latent subspace with much reduced dimensions. The dynamic latent space is the kernel that facilitates predictive analytics. Industrial and economic examples are used to illustrate this point of view, and methods from control engineering, statistics, econometrics, subspace identification, and machine learning are introduced to bring ideas for future development of dynamic system data science.


Presented by:
Prof Rolf FINDEISEN


Abstract:

New technologies like robots, communication, and the massive impact of digitalization dramatically influence every aspect of our lives. There is an ever-increasing need for autonomous systems that can adapt and learn to account for changing conditions. Simultaneously, large amounts of data are becoming available at a breathtaking rate. Control and decision-making often lie at the core of many of the involved technologies and underlying functionalities. Additionally, one sees an increasing complexity in describing and handle these interconnected systems. Machine learning and artificial intelligence are seen as critical technologies to tackle the challenges. Despite significant advances, the use of machine learning methods in control and automation is still in its infancy. One of the main reasons for this is the need for the dependable, explainable, and safe operation of autonomous systems. We focus on model predictive control (MPC) combined with machine learning approaches. Fusing predictive control with machine learning approaches is promising, as it allows to adapt to changes and to use data-driven or hybrid models. We outline results towards the fusion of predictive control and learning approaches, focusing on guarantees of performance and stability. After a brief introduction, we present different strategies that guarantee stable and safe operation and outline their application towards robotics, driving, and chemical processes. 

Presented by:
Dr Hong ZHAO


Abstract:

The current excitement around artificial intelligence (AI), particularly machine learning (ML), industrial internet of things (IIoT), and Industry 4.0 is palpable and contagious. The expectation that AI is poised to “revolutionize,” perhaps even take over, humanity has elicited prophetic visions and concerns from some luminaries. Sure, the advances AI has made in the last 10 years, for example, AlphaGo, autonomous cars, Alexa, Watson, and other such systems, in game playing, robotics, computer vision, speech recognition, and natural language processing are indeed stunning progress. But, as with earlier AI breakthroughs, such as expert systems in the 1980s and neural networks in the 1990s, there is also considerable hype and a tendency to overestimate the promise of these advances. So, what is different this time? What is the true potential and value for industry?

This presentation is not intended to offer a direct answer, but I would like to share some views and perspectives through a review of recent innovative advances of system identification, model predictive control, and data analytics in the process industry. More importantly, I explore how those innovations and applications successfully added tremendous value for process industries. As industrial practitioners of AI/ML, we witnessed the progressive development in applications of AI/ML technology for advanced process control (APC) and process optimization and learned lessons of what works and what does not. AI/ML is a powerful enabler, but success requires a solid foundation of domain knowledge and understanding of both AI/ML and the use cases to address. Challenges and opportunities exist simultaneously, and researchers must study them to provide both theoretical and applicable solutions for industry. 

Module 1
Data Science and Automatic Control Systems
19 March 2021 (Friday)

07:45 pm – 10:25 pm (Hong Kong Time/GMT +8)   
07:45 am – 10:25 am (Eastern Daylight Time/GMT -4) 
12:45 pm – 03:25 pm (Central Europe Time/GMT +1)
(Time Zone Converter)

07:45 pm - 08:00 pm
(Hong Kong Time/GMT +8)

Registration

08:00 pm - 08:05 pm
(Hong Kong Time/GMT +8)

Welcoming Speech

Presented by
Prof Jie CHEN
Chair Professor, Department of Electrical Engineering, City University of Hong Kong, Hong Kong
Fellow of IEEE, AAAS, IFAC

08:05 pm - 08:40 pm
(Hong Kong Time/GMT +8)

Predictive Analytics in Industrial IoT, Data, and Systems

Presented by
Prof S. Joe QIN
Dean and Chair Professor of Data Science, School of Data Science, City University of Hong Kong, Hong Kong
Director, Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong
Director, Centre for Systems Informatics Engineering, City University of Hong Kong, Hong Kong

08:40 pm - 09:15 pm
(Hong Kong Time/GMT +8)

Entangling Control and Machine Learning via Predictive Control - How to Achieve Flexibility and Safety

Presented by
Prof Rolf FINDEISEN
Prof. Dr.-Ing., Institut für Automatisierungstechnik (IFAT)
Chair of Systems Theory and Automatic Control, Otto-von-Guericke-University Magdeburg, Germany

09:15 pm - 09:50 pm
(Hong Kong Time/GMT +8)

An Industry Perspective on AI, Machine Learning and Data Science towards Industry 4.0

Presented by
Dr Hong ZHAO
Distinguished Technologist (Senior Director), Aspen Technology, United States of America

09:50 pm - 10:25 pm
(Hong Kong Time/GMT +8)

Panel Discussion

Moderator:
Prof Jie CHEN

------------------------------
Panellists:
Prof Rolf FINDEISEN
Prof S. Joe QIN
Dr Hong ZHAO


Module 2  Topic Presentations

Presented by:
Prof Richard D. BRAATZ

Abstract:

This presentation describes Pharma 4.0 technologies for the advanced manufacturing of (bio)pharmaceutical products. The specific strategies that are described are: (1) optimization of each unit operation by exploiting process intensification, (2) automated high-throughput microscale technology for fast continuous process research and development, (3) plug-and-play modules with integrated control and monitoring to facilitate deployment, (4) dynamic models for unit operations for automated plant-wide simulation and control design, and (5) autonomous/smart data analytics and machine learning. These strategies are demonstrated in applications including in continuous viral vaccine production, monoclonal antibody manufacturing via batch processing, and fully automated end-to-end (semi-)continuous manufacturing.


Presented by:
Prof Jay H. LEE

Abstract:

This presentation provides a brief introduction to Reinforcement Learning (RL) technology, summarizes recent developments in this area, and discusses their potential implications for the field of process control. The paper begins with a brief introduction to RL, a machine learning technology that allows an agent to learn, through trial and error, the best way to accomplish a task. A comparison of the key features of RL vs. Model Predictive Control (MPC) is then presented in order to clarify their relative merits and shortcomings. This is followed by an assessment of areas where RL technology can potentially be used as an alternative or complementary technology to MPC.  

Presented by:
Prof Furong GAO

Abstract:

Chemical processes may be categorized into batch and continuous two types. Tremendous control and automation progress in both research and applications has been made for the continuous processes, in contrast, that for the batch processes is far behind. A batch process has characteristics inherently different from that of a continuous process, leading to difficulties in applying control methods developed for a continuous process to a batch process. This talk summarizes some of the key characteristic differences and highlight our progress made for batch processes in modeling, control, and monitoring. The speaker will take thermoplastic injection molding as the example to illustrate and discuss these related issues. 

Module 2
Chemical and Biological Engineering
21 May 2021 (Friday)
Registration Online

08:45am - 11:25am (Hong Kong Time/GMT +8)
08:45pm –11:25pm (Eastern Daylight Time/GMT -4)
02:45am – 05:25am (Central Europe Summer Time/GMT +2)
(Time Zone Converter)

08:45 am - 09:00 am
(Hong Kong Time/GMT +8)

Registration

09:00 am - 09:05 am
(Hong Kong Time/GMT +8)

Welcome Speech

Presented by
Prof Li QIU
Professor, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong
Director of HKUST Shenzhen Cyber-Physical Systems Laboratory

09:05 am - 09:40 am
(Hong Kong Time/GMT +8)

Pharma 4.0: Advanced Manufacturing of (Bio)Pharmaceutical Products

Presented by
Prof Richard D. BRAATZ
Edwin R. Gilliland Professor and Faculty Research Officer, Department of Chemical Engineering, Massachusetts Institute of Technology, United States of America

09:40 am - 10:15 am
(Hong Kong Time/GMT +8)

Batch Process Automation

Presented by
Prof Furong GAO
Chair Professor, Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong
Director, Center for Polymer Processing and Systems, Hong Kong University of Science and Technology, Hong Kong

10:15 am - 10:50 am
(Hong Kong Time/GMT +8)

Reinforcement Learning vs MPC – Alternative or Complementary?

Presented by
Prof Jay H. LEE
Professor, Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Korea
Director, Saudi Aramco-KAIST CO2 Management Center

10:50 am - 11:20 am
(Hong Kong Time/GMT +8)

Panel Discussion

Moderator:
Prof Li QIU
------------------------------ 
Panellists:    
Prof Richard D. BRAATZ
Prof Furong GAO
Prof Jay H. LEE

Module 3  Topic Presentations

Presented by:
Prof Li QIU

Abstract:

The phase or argument of a complex number is an important concept known to most of the university students in STEM. What are the phases of a matrix? What are the phases of a MIMO LTI system? What are the phases of a nonlinear system? What is the phase of a graph? What are the phases of a dynamic network? These are the questions to be answered in the newly developed phase theory, which will also answer the question why the phases are important in system analysis and data analytics. This talk will give a glimpse of the phase theory.   

Presented by:
 Prof Zhong Ping JIANG

Abstract:

In this talk, I will consider a cooperative control problem for semi-autonomous vehicles that operate in uncertain environments. In the absence of a precise model for human drivers, it is shown how to obtain data-driven vehicle control laws based on an entanglement of machine learning (especially reinforcement learning) and small-gain techniques. More precisely, the human-vehicle interaction is considered as an interconnected system with partially observable states. By means of reinforcement learning and adaptive dynamic programming, a shared steering controller is learned directly from the measurable data of the driver and the vehicle, independent of the unmeasurable internal state of the human driver. The effectiveness of the proposed method is validated by rigorous analysis and demonstrated by numerical simulations.
To make the talk accessible to upper-level undergraduates and graduate students with diverse backgrounds, the basics of learning-based control will be reviewed. 

Presented by:
Prof Jie CHEN

Abstract:

For well over a century, PID control stood out as the most favored method for its simplicity, robustness, ease of implementation and cost-effectiveness. It continues to demonstrate its sustained power and inexplicable charm, serving an awe-inspiring testimony to its incredible vitality and widespread acceptance by industrial control communities. Traditionally, the design and implementation of PID controllers are conducted by somewhat ad hoc, trial and error tuning methods. One must then wonder, with its seemingly simplistic structure and empirical rules of design, why on earth PID control is good? How good can it be? What is its fundamental limitation? In this talk I shall attempt to provide an anecdote and an analysis to these contemplations, using system robustness--arguably the most important goal in feedback control design--as a pilot problem. We address such canonical problems as gain, phase, and delay margins, which define in different manners the largest range of unknown, variable parameters that a system can tolerate to maintain stability robustness. I shall present our recent triumphs in determining these margins achievable by PID control, and argue in favor of a least amount of insight vis-a-vis massive computations for maximal yield. The solutions to the problems, much to our delight, provide analytical justifications to folk wisdom of one hundred years on PID controller tuning and design.

Module 3
Electrical and Computer Engineering
9 July 2021 (Friday)
Registration Online

07:45 pm – 10:25 pm (Hong Kong Time/GMT +8)  
07:45 am – 10:25 am (Eastern Daylight Time/GMT -4) 
12:45 pm – 03:25 pm (Central Europe Time/GMT +1)
(Time Zone Converter)

07:45 pm - 08:00 pm
(Hong Kong Time/GMT +8)

Registration

08:00 pm - 08:05 pm
(Hong Kong Time/GMT +8)

Welcome Speech

Presented by
Prof S. Joe QIN
Dean and Chair Professor of Data Science, School of Data Science, City University of Hong Kong, Hong Kong
Director, Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong
Director, Centre for Systems Informatics Engineering, City University of Hong Kong, Hong Kong

08:05 pm - 08:40 pm
(Hong Kong Time/GMT +8)

Renaissance of Phase

Presented by
Prof Li QIU
Professor, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong
Director of HKUST Shenzhen Cyber-Physical Systems Laboratory

08:40 pm - 09:15 pm
(Hong Kong Time/GMT +8)

Small-Gain and Machine Learning Techniques for Autonomous Driving

Presented by
Prof Zhong Ping JIANG
Professor, Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University, United States of America

09:15 pm - 09:50 pm
(Hong Kong Time/GMT +8)

Revisiting PID

Presented by
Prof Jie CHEN
Chair Professor, Department of Electrical Engineering, City University of Hong Kong, Hong Kong
Fellow of IEEE, AAAS, IFAC

09:50 pm - 10:20 pm
(Hong Kong Time/GMT +8)

Panel Discussion

Moderator:
Prof S. Joe QIN
------------------------------ 
Panellists:    
Prof Jie CHEN
Prof Zhong Ping JIANG
Prof Li QIU

11:20 pm - 11:30 pm
(Hong Kong Time/GMT +8)

Round-up

Presented by
Prof S. Joe QIN
Dean and Chair Professor of Data Science, School of Data Science, City University of Hong Kong, Hong Kong
Director, Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong
Director, Centre for Systems Informatics Engineering, City University of Hong Kong, Hong Kong

FAQ

Due to the travel restrictions posted by HKSAR government, and most of our speakers are from overseas, the workshop would be conducted online.


This workshop series is free of charge.

This workshop is open to all public members who are interested in control systems, data science, industry 4.0 and relevant disciplines.

Please register the workshop series online at IFAC workshop website https://www.cityu.edu.hk/sdsc_web/ifac2021/ . Then, click to the tab of the module that you are interested to register. You will then be directed to the registration page.

Yes. Please send your questions to us at IFAC workshop website https://www.cityu.edu.hk/sdsc_web/ifac2021/ or to HKIDS at hkids@cityu.edu.hk. We will direct your questions to speakers and hosts of the workshop.
OR you can also ask your question at the public chatroom during the Workshop, we will collect the questions and direct them to the moderator at the panel discussion session

Workshop videos and sharing materials will be uploaded at the website of School of Data Science and Hong Kong Institute for Data Science . We need permission from speakers before posting the workshop videos and sharing materials. 

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