Keynote Speakers

    1. Prof. Feng Ding, Jiangnan University, China.
    Feng Ding was born in Guangshui, Hubei Province, China. He received the B.Sc. degree from the Hubei University of Technology (Wuhan, China) in 1984, and the M. Sc. and Ph.D. degrees in automatic control both from the Department of Automation, Tsinghua University (Beijing, China) in 1991 and 1994, respectively. He has been a Professor in the School of Internet of Things Engineering, Jiangnan University (Wuxi, China) since 2004. He has published over 512 papers and over 256 SCI indexed papers on system identification and parameter estimation. Sum of Cited Times is more than 10000, and Sum of Cited Times without self-citations is more than 7000.
    Prof. Ding has published the book Adaptive Control Systems (Tsinghua University Press, Beijing, 2002), and the book Modern Control Theory (Tsinghua University Press, Beijing, 2017). He is publishing The Academic Monograph Series on System Identification: The 1st book System Identification -- New Theory and Methods (Science Press, Beijing, 2013), The 3rd book System Identification -- Performances Analysis for Identification Methods (Science Press, Beijing, 2014), The 4th book System Identification -- Auxiliary Model Identification Idea and Methods (Science Press, Beijing, 2017), The 6th book System Identification -- Multi-Innovation Identification Theory and Methods (Science Press, Beijing, 2016).

    Topic: New Idea, Theory, Principle and Methods for System Identification

    Control science casts the glory of our age, and the computer chips with high integration are the masterpiece of automation science and technology. The brilliant achievements of control science across time and space -- the abilities of the computing and information processing of electronic equipment and electronic products completely have changed and are changing the way of life, information society, and are beautifying our life.
    This topic is to introduce some new idea, theory, principle, concept and methods of system identification, which involve the auxiliary model identification idea, the multi-innovation identification theory, the hierarchical identification principle, the coupling identification concept and methods. These can be applied to linear-parameter systems, bilinear-parameter systems, multi-linear-parameter systems, bilinear systems, nonlinear systems and generate numerous and various identification methods.

    2. Prof. Dimitri Lefebvre, University of Normandy, Le Havre, France
    Dimitri Lefebvre graduated from the Ecole Centrale of Lille (France) in 1992. He received his PhD in Automatic Control and Computer Science from University of Sciences and Technologies, Lille in 1994, and an HDR from University of Franche Comté, Belfort, France in 2000. Since 2001, he has been a Professor at Institute of Technology and Faculty of Sciences, University Le Havre, France. He is with the Research Group on Electrical Engineering and Automatic Control (GREAH) and from 2007 to 2012 he was the head of the group. His current research interests include Petri nets, learning processes, adaptive control, fault detection and diagnosis and their applications to automated manufacturing and electrical engineering.

    Topic: Some advances in perception, modeling and control for discrete event systems

    In recent years, the tremendous growth of computer technology has led to the proliferation of highly complex dynamical systems, in particular in Industry 4.0 and Internet of Things. Such systems exhibit behaviors determined by the asynchronous occurrence of certain events and are termed Discrete Event Systems (DES). Examples of DES are encountered in many traditional application domains, such as automated manufacturing, computer networks, transportation, as well as in emerging areas like healthcare, communication and information processing, and management of technical, human and financial resources.
    A significant research effort has been devoted to DES in order to address a series of difficult problems that are often combinatorial in nature, and require advances in exploration and optimization methodologies. Such problems concern, on the one hand, information perception and processing, and in the other hand, decision making and control design.
    The aim of this talk is first to give some basic modeling notions to represent DES with automata or Petri nets in order to address a large variety of perception and control problems. Then, some practical issues are detailed in the domain of automated manufacturing. Fault detection and diagnosis methods are introduced in order to ensure safe operations. Resources allocation and scheduling issues are also presented and discussed to improve performance. Finally, a list of open questions and future challenges is proposed.

    3. Prof. Jawhar Ghommam, Sultan Qaboos University, Oman
    Jawhar Ghommam got the BSc degree in Computer and Control Engineering from the National Institute and Applied Sciences and Technology (INSAT) in 2003 in Tunis. He's got the DEA (MSc) degree from the university of Montpelier at the Laboratoire d'Informatique, Robotique et Micro-électronique (LIRMM, France) in 2004 and later on in 2008 a PhD in Control Engineering degree jointly from the National Engineering School of Sfax and the university of Orleans. From 2008 to 2017 he was with the National Institute of Applied Sciences and Technology, where he hold a tenured Associate Professor at the Department of Physics and Instrumentation. In January 2018 he joined the Department of Electrical and Computer Engineering at Sultan Quaboos University in Oman. He is a member of the Control and Energy Management Lab and also an Associate Researcher at the GREPCI-Lab, Ecole de Technologie Superieure, Montreal,QC, Canada. His Research interests include fundamental motion control concepts for nonholonomic/underactuated vehicle systems, nonlinear and adaptive control; intelligent and autonomous control of networked unmanned systems, team cooperation, consensus achievement, and sensor networks. He serves as a regular referee and associate editors for many international journals in the field of Control and Robotics.

    Topic: Control and Navigation of Marine Vehicles

    Water is one of the most precious and valuable resources in Oman. As climate change is continuing to negatively impact our environment, it is important that we take a closer look at the quality of water in the ocean that surrounds the region. Nevertheless, there are many difficulties and limitations in current ocean observation systems. This talk is expose the audience differencing techniques that aim at employing multi-aquatic robot platforms which address the monitoring and collection of large amount of data over a large area in the sea. The main issue will be to talk about the cooperative motion of a fleet of Autonomous Surface Vessel (ASV) towards seeking the highest concentration of water contaminant in a given region. A decentralized approach will be presented to address the control formation strategy of the ASVs and to show how ASVs can individually create a local estimate of the formation based on a local measurement of the scalar field value of the pollutant in the water and how possibly they can create their own control action using that estimate. New algorithms will be proposed enabling the ASV to circumnavigate the source of contamination/pollution.

    4. Prof. Ahmed Chemori, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, France
    Ahmed Chemori received his M.Sc. and Ph.D. degrees, both in automatic control from Polytechnic Institute of Grenoble, France, in 2001 and 2005 respectively. During the year 2004/2005 he has been a Research and Teaching assistant at Laboratoire de Signaux et Systèmes (LSS - Centrale Supelec) and University Paris 11. Then he joined Gipsa-Lab (Former LAG) as a CNRS postdoctoral researcher. He is currently a senior research scientist in Automatic control and Robotics for the French National Center for Scientific Research (CNRS), at the Montpellier Laboratory of Computer Science, Robotics and Microelectronics (LIRMM). His research interests include nonlinear (adaptive and predictive) control and their real-time applications in different fields of robotics (underactuated robotics, parallel robotics, underwater robotics, humanoid robotics and wearable robotics). He is the author of more than 100 scientific publications, including international journals, patents, book chapters and international conferences.

    Topic: Control of Complex Robotic Systems Challenges, Design and Experiments

    Robotics was initially and for a long time guided by needs in industry. Indeed, the early years of robotics was largely focused on robot manipulators, used mainly for simple and repetitive automation tasks. The first industrial robot manipulator appeared in 1961 in the assembly lines of General Motors. The early control systems for robot manipulators were designed to control independently each axis of the robot as a Single-Input-Single-Output (SISO) linear system. Linear automatic control theory was then extensively used in this basic solution, where the coupling dynamics between the different axes of the robot were often neglected and the robot model significantly simplified. Beyond these issues, the main barriers to progress were the high cost of computation, the lack of good sensors, and the lack of fundamental understanding of robot dynamics. However, the progress of robotics and automation as well as their associated innovative applications has required the consideration of more and more complex tasks needing high performances. These challenging tasks required a deeply understanding of complex nonlinear dynamics of robots. Besides, it has also motivated the development of new theoretical advances in different control fields (robust, adaptive, nonlinear, etc.), which has consequently enabled more sophisticated applications. Nowadays, robotic control systems are highly advanced, including manipulation robotics, underwater robotics, aerial robotics, mobile robotics, medical robotics, parallel robotics, wearable robotics, humanoid robotics and more others. In this lecture the main challenges related to control of complex robotic systems will be emphasized, and illustrated through different applications in robotics. For each of these fields, the motivations and the need of developing advanced control schemes will be first highlighted. Then some proposed advanced control solutions will be introduced and illustrated through real-time experiments.

    5. Prof. Weicun Zhang, University of Science and Technology Beijing
    Weicun Zhang received his M.S. degreein Automatic Control (1989) from Beijing Institute of Technology and the in Control Theory and Applications (1993) from Tsinghua University, P.R. China. He joined the School of Automation and Electrical Engineering,University of Science and Technology Beijing, in 2002, as associate professor.From March 1997 to May 1998, he was a visiting research fellow in Industrialand Operations Engineering Department, University of Michigan at Ann Arbor,From September 2006 to August 2007, he was a visiting professor in Departmentof Electrical and Computer Engineering, Seoul National University, South Korea. His research interests include:self-tuning adaptive control, multiple model adaptive control and estimation. As representative research work, he established a Virtual Equivalent System(VES) theory for unified analysis (stability, convergence, and robustness) of ageneral self-tuning control system, which is independent of specific controlstrategy and parameter estimation algorithm. He proofed the stability ofweighted multiple model adaptive control system with the help of VES.

    Topic: On the stability of weighted multiple model adaptive control----Virtual Equivalent System Approach

    The research on weighted multiple model adaptive estimation and control appeared around 1960’s to 1970’s, where multiple Kalman filter-based models were studied to improve the accuracy of the state estimate in estimation and control problems. In spite of decades of theoretical and experimental research, it is widely accepted that the closed-loop stability of WMMAC system is difficult to prove.
    In this talk, I will give a history survey of adaptive control including self-tuning control (STC) and weighted multiple model adaptive control (WMMAC) at first; after that I will briefly introduce the proof of the closed-loop stability of the WMMAC system based on virtual equivalent system (VES) concept.

    6. Prof. Quanmin Zhu, University of the West of England, United Kingdom
    Quanmin Zhu obtained his MSc in Harbin Institute of Technology, China in 1983 and PhD in Faculty of Engineering, University of Warwick, UK in 1989. His main research interest is in the area of nonlinear system modelling, identification, and control. His other research interest is in investigating electrodynamics of acupuncture points and sensory stimulation effects in human body, modelling of human meridian systems, and building up electro-acupuncture instruments. He has published over 200 papers on these topics, edited five Springer books and one book for the other publisher, and provided consultancy to various industries. Currently Professor Zhu is acting as Editor of International Journal of Modelling, Identification and Control, Editor of International Journal of Computer Applications in Technology, Member of Editorial Committee of Chinese Journal of Scientific Instrument, and editor of Elsevier book series of Emerging Methodologies and Applications in Modelling, Identification and Control. He is the founder and president of series annual International Conference on Modelling, Identification and Control.

    Topic:Dealing with nonlinearities in dynamic system, modelling, identification and control---linear approaches


    7. Dr Jing Na, Kunming University of Science & Technology
    Dr Jing Na is currently a Professor with the Faculty of Electrical & Mechanical Engineering at Kunming University of Science & Technology, and also a Marie Curie Fellow with the University of Bristol, UK. He received the B.S. and Ph.D. degrees from the School of Automation, Beijing Institute of Technology, China, in 2004 and 2010, respectively. From January 2011 to December 2012, he was a Monaco/ITER Postdoctoral Fellow with the ITER Organization, France. Since 2010, he has been with the Kunming University of Science & Technology, where he was promoted to be a full Professor in 2013. He has hold also visiting positions with the Universitat Politecnica de Catalunya, Spain (6 months in 2008), and with the University of Bristol, UK (12 months in 2009). Dr Na is currently an Associate Editor of the Neurocomputing, and the International Journal of Modelling, Identification and Control. He has served as an international program committee Chair of ICMIC 2017, and IPC member of many international conferences (e.g., IEEE CASE, IEEE CIS&RAM, IFAC ICONS, etc). He has organized/co-organized several special issues on Complexity, Discrete Dynamics in Nature and Society, and invited session in several prestigious conferences (e.g. IEEE CDC, UKACC, CCC).
    His current research interests include parameter estimation, adaptive optimal control, and nonlinear control with application to vehicle systems, servo mechanisms and energy conversion plants (e.g. engine, wave energy convertors, etc.). He has published more than 100 peer reviewed journal and conference papers. Dr Na has been awarded the Best Application Paper Award of the 3rd IFAC International Conference on Intelligent Control and Automation Science (IFAC ICONS 2013), and the 2017 Hsue-shen Tsien Paper Award.

    Topic: Adaptive Parameter Estimation and Control via Parameter Error: A New Framework

    Adaptive parameter estimation and adaptive control have been well developed for uncertain systems to improve modeling and control performance. However, the well-known parameter estimation and adaptive control methods have been mainly designed based on the gradient algorithms (with appropriate modifications) with prediction error or control error. Hence, the parameter estimation convergence and the online verification of the required persistent excitation (PE) condition are generally difficult with this framework. In this talk, we will introduce a novel robust, fast adaptive parameter estimation framework, where the estimation error between the unknown parameters and their estimates are explicitly obtained and then use to drive online adaptation algorithms. This new adaptation even allows to achieve finite-time parameter estimation, and can be easily incorporated into adaptive control designs to achieve tracking and parameter estimation simultaneously. We will introduce an intuitive and numerically feasible approach to online verify the PE condition. Finally, several practical application of this new adaptation to in-car parameters, adaptive control design and approximate dynamic programing for robotics, vehicles, wave energy converters (WECs) and other realistic systems will be presented.