（in alphabetical order by surname）
Title：Using AI and Machine Learning Tools to Solve Optimal Control Problems
Professor Derong Liu, Guangdong University of Technology , China
Abstract: Machine learning is one of the most important branches of artificial intelligence. Researchers have been using AI and Machine Learning techniques in modern control theory. Self-learning control methodologies are a good representative of such efforts. Reinforcement learning (RL) recently has become a major force in the machine learning fields. On the other hand, adaptive dynamic programming (ADP) has now become popular in control communities. Both RL and ADP have roots in dynamic programming and in many ways they are equivalent. Major breakthroughs of ADPRL for optimal control were achieved around 2006, when iterative ADP approaches were introduced. The optimal control of nonlinear systems requires to solve the nonlinear Bellman equation instead of the Riccati equation as in the linear case. The discrete-time Bellman equation is more difficult to work with than the Riccati equation because it involves solving nonlinear partial difference equations. Though dynamic programming has been a useful computational technique in solving optimal control problems, it is often computationally untenable to run it to obtain the optimal solution, due to the backward numerical process required for its solutions, i.e., the well-known “curse of dimensionality”. Self-learning optimal control based on ADPRL provides efficient tools for tackling the following two problems. (1) Nonlinear Bellman equation is solved using iterative ADP approaches which are shown to converge. (2) Neural networks are employed for function approximation in order to obtain forward numerical process. Samples of the vast amount of new developments since five years ago in ADPRL for optimal control will be introduced in this lecture.
Biography: Derong Liu received the Ph.D. degree in electrical engineering from the University of Notre Dame in 1994. He was a Staff Fellow with General Motors Research and Development Center, from 1993 to 1995. He was an Assistant Professor with the Department of Electrical and Computer Engineering, Stevens Institute of Technology, from 1995 to 1999. He joined the University of Illinois at Chicago in 1999, and became a Full Professor of Electrical and Computer Engineering and of Computer Science in 2006. He was selected for the “100 Talents Program” by the Chinese Academy of Sciences in 2008, and he served as the Associate Director of The State Key Laboratory of Management and Control for Complex Systems at the Institute of Automation, from 2010 to 2015. He is now a Full Professor with the School of Automation, Guangdong University of Technology. He has published 19 books. He is the Editor-in-Chief of Artificial Intelligence Review (Springer). He was the Editor-in-Chief of the IEEE Transactions on Neural Networks and Learning Systems from 2010 to 2015. He received the Faculty Early Career Development Award from the National Science Foundation in 1999, the University Scholar Award from University of Illinois from 2006 to 2009, the Overseas Outstanding Young Scholar Award from the National Natural Science Foundation of China in 2008, and the Outstanding Achievement Award from Asia Pacific Neural Network Assembly in 2014. He is a Fellow of the IEEE, a Fellow of the International Neural Network Society, and a Fellow of the International Association of Pattern Recognition.
Personal webpage: http://derongliu.org/
Professor Yaochu Jin, University of Surrey, United Kingdom.
Biography: Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. degree from Ruhr University Bochum, Germany, in 2001.
He is currently a Distinguished Chair Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K., where he heads the Nature Inspired Computing and Engineering Group. He was a Finland Distinguished Professor funded by the Finnish Funding Agency for Innovation (Tekes) and a Changjiang Distinguished Visiting Professor appointed by the Ministry of Education, China. His main research interests include data-driven surrogate-assisted evolutionary optimization, evolutionary learning, interpretable and secure machine learning, and evolutionary developmental systems. His research has been funded by EU, EPSRC, Royal Society, NSFC, and the industry, including Honda, Airbus, and Bosch.
Dr Jin is the Editor-in-Chief of the IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS and Co-Editor-in-Chief of Complex & Intelligent Systems. He is an IEEE Distinguished Lecturer (2013-2015 and 2017-2019) and past Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015). He is the recipient of the 2018 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015 and 2017 IEEE Computational Intelligence Magazine Outstanding Paper Award. He is an IEEE Fellow.
Personal webpage: https://www.surrey.ac.uk/people/yaochu-jin
Professor Kay Chen Tan, City University of Hong Kong, China
Biography: Kay Chen TAN (SM’08-F’14) received the B.Eng. (First Class Hons.) degree in electronics and electrical engineering and the Ph.D. degree from the University of Glasgow, Glasgow, U.K., in 1994 and 1997, respectively. He is a Full Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong SAR. He has published over 300 refereed articles and 10 books. Dr. Tan is the Editor-in-Chief of the IEEE Transactions on Evolutionary Computation, was the Editor-in-Chief of the IEEE Computational Intelligence Magazine from 2010 to 2013, and currently serves as the Editorial Board Member of over 10 journals. He is currently an elected member of IEEE CIS AdCom, an IEEE DLP Speaker, and a Changjiang Chair Professor in China.
Personal webpage: http://www6.cityu.edu.hk/stfprofile/kaytan.htm
Title：Data-Driven Intelligent Optimization Scheduling
Professor Ling Wang，Tsinghua University，China
Biography: Ling Wang received the B.Sc. and Ph.D. degrees from Tsinghua University, Beijing, China, in 1995 and 1999, respectively, and now is a tenured Full Professor in Tsinghua Univ. His research interests mainly include intelligent optimization, scheduling and applications. He has authored 5 academic books and more than 160 SCI-indexed papers. His publications have attracted over 16K Google Scholar Citations. He is the Editor-in-Chief of International J of Automation and Control, the Associate Editor of IEEE Trans on Evolutionary Computation, Swarm and Evolutionary Computation, and the Editorial Board Member of Memetic Computing, J of Optimization, Control Theory & Applications, Control and Decision, Control Engineering, System Engineering and Electronics. Prof. Wang received the National Natural Science Award of China (2014), the Natural Science Award of the Ministry of Education (MOE) of China (both 2003 and 2007), the Science and Technology Awards of both Beijing City (2008) and Yunnan Province (2017). He also received the Best Paper Awards of ACTA AUTOMATICA SINICA (2014), Control Theory & Applications (2016), and Control and Decision (2017). He was the recipient of the National Natural Science Fund for Distinguished Young Scholars of China (2015), the New Century Excellent Talent in University by the MOE of China (2009), the Young Scientist Award of CAA (2016), and IEEE ICIC Outstanding Leadership Award (2018).
Personal webpage: http://www.cfins.au.tsinghua.edu.cn/personalhg/wangling/homepage_wangling.htm
Title：Evolutionary Deep Learning and Applications to Image Classification
Professor Mengjie Zhang, Victoria University of Wellington,New Zealand
Biography: Mengjie Zhang is a Fellow of Royal Society of New Zealand, a Fellow of IEEE, and currently Professor of Computer Science at Victoria University of Wellington, where he heads the interdisciplinary Evolutionary Computation Research Group. He is a member of the University Academic Board, a member of the University Postgraduate Scholarships Committee, a member of the Faculty of Graduate Research Board at the University, Associate Dean (Research and Innovation) in the Faculty of Engineering, and Chair of the Research Committee of the Faculty of Engineering and School of Engineering and Computer Science.
His research is mainly focused on artificial intelligence (AI), machine learning and big data, particularly in evolutionary computation and learning (using genetic programming, particle swarm optimisation and learning classifier systems), feature selection/construction and big dimensionality reduction, computer vision and image processing, job shop scheduling and resource allocation, multi-objective optimisation, classification with unbalanced data and missing data, and evolutionary deep learning and transfer learning. Prof Zhang has published over 500 research papers in refereed international journals and conferences in these areas. He has been serving as an associated editor or editorial board member for over ten international journals including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the Evolutionary Computation Journal (MIT Press), Genetic Programming and Evolvable Machines (Springer), Applied Soft Computing, IEEE Transactions on Emergent Topics in Computational Intelligence, Natural Computing, and Engineering Applications of Artificial Intelligence, and as a reviewer of over 30 international journals. He has been involving major AI and EC conferences such as GECCO, IEEE CEC, EvoStar, IJCAI, AAAI，PRICAI, PAKDD, AusAI, IEEE SSCI and SEAL as a Chair. He has also been serving as a steering committee member and a program committee member for over 100 international conferences. Since 2007, he has been listed as one of the top ten (currently No. 4) world genetic programming researchers by the GP bibliography(http://www.cs.bham.ac.uk/~wbl/biblio/gp-html/index.html)
Prof Zhang is a Past Chair of the IEEE CIS Intelligent Systems Applications Technical Committee, the IEEE CIS Emergent Technologies Technical Committee and the IEEE CIS Evolutionary Computation Technical Committee, a vice-chair of the IEEE CIS Task Force on Evolutionary Feature Selection and Construction, a vice-chair of the IEEE CIS Task Force on Evolutionary Computer Vision and Image Processing, and the founding chair of the IEEE Computational Intelligence Chapter in New Zealand.
Personal homepage: https://www.victoria.ac.nz/engineering/about/staff/mengjie-zhang
(To be continued)