KEYNOTE/PLENARY SPEAKERS AND TALKS

Richard D. Braatz is the Gilliland Professor at the Massachusetts Institute of Technology (MIT) where he does research in control theory and its application to biomedical systems, pharmaceuticals manufacturing, and nanotechnology. He received MS and PhD degrees from the California Institute of Technology and was a Professor at the University of Illinois at Urbana-Champaign and a Visiting Scholar at Harvard University before moving to MIT. He has consulted or collaborated with numerous companies including IBM, United Technologies Corporation, Avery-Dennison, DuPont, Novartis, Merck, Eli Lilly Pharmaceuticals, Bristol-Myers Squibb, and Abbott Laboratories. Honors include the AACC Donald P. Eckman Award, the Curtis W. McGraw Research Award from the Engineering Research Council, the Antonio Ruberti Young Researcher Prize, and best paper awards from IEEE- and IFAC-sponsored control journals. He is a Fellow of IEEE, IFAC, and the American Association for the Advancement of Science.

Abstract:
An overview is provided on advances in the control of molecular purity, crystal structure, and particle size distribution of pharmaceutical crystals. A nonlinear feedback control strategy is described that is robust to orders-of-magnitude variations in the crystallization kinetics, which enables the same feedback controller to apply to completely different pharmaceutical compounds by just updating the drug solubility. The control strategy enables the manufacture of large drug crystals of uniform size and specified crystal structure, regardless of whether the crystal structure is thermodynamically stable or metastable. The control strategy forces the closed-loop operations to be within a tightly constrained trajectory bundle that connects the initial states to the desired final states. A modification of the methodology is able to achieve a target crystal size distribution by employing continual manipulation of seeds manufactured using a dual-impinging jet mixer. The methodology has been evaluated in theoretical, simulation, and experimental studies for a wide variety of pharmaceutical compounds. The presentation ends with a discussion of directions towards the simultaneous control of multiple properties, by the integration of feedback control with process design.

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Prof. Dario Floreano is the Director of the Laboratory of Intelligent Systems at Ecole Polytechnique Fédérale de Lausanne Switzerland (EPFL) and Director of the Swiss National Center of Robotics, a national strategic initiative which brings together all major robotics labs in Switzerland. His research focuses on the convergence of biology, artificial intelligence, and robotics. He published three books on the topics of evolutionary robotics, bio-inspired artificial intelligence, and bio-mimetic flying robots with MIT Press and Springer Verlag. 1988 M.A. University of Trieste; 1991 M.S. University of Stirling; 1995 PhD University of Trieste; 1998, Sony Computer Science Laboratories; since 2000 professor positions at EPFL; 2001 co-founder of the International Society of Artificial Life, Inc.; 2003-2008 Board of Governors of the International Society of Neural Networks; 2009 Advisory Board of the Future Emerging Technologies of the European Commission; 2009 co-founder of senseFly Ltd.

Abstract:
I will present the long-term vision, approach, and results obtained so far to let robots live in the 3rd dimension. Taking inspiration from nature, I will start by describing how robots could take off the ground by jumping and gliding. I will then move on to autonomous flight in cluttered environments and on the issue of perception and control for small flying systems in indoor environments. This will lead to the next step resulting in outdoor flying robots that can autonomously regulate altitude, steering, and landing using only perceptual cues. I will then expand the perspective by describing how multiple robots could fly in swarm formation in outdoor environments by means of evolved controllers and how these achievements could possibly lead to fleets of personal aerial vehicles in the not-so-far future. I will close the talk by going back indoor with current work on radically new concepts of flying robots that collaborate with teams of terrestrial and climbing robots and of flying robots designed to survive and even exploit collisions.

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Frank L. Lewis, Fellow IEEE, Fellow IFAC, Fellow U.K. Institute of Measurement & Control, PE Texas, U.K. Chartered Engineer, is Distinguished Scholar Professor, Distinguished Teaching Professor, and Moncrief-O’Donnell Chair at University of Texas at Arlington’s Automation & Robotics Research Institute. He obtained the Bachelor's Degree in Physics/EE and the MSEE at Rice University, the MS in Aeronautical Engineering from Univ. W. Florida, and the Ph.D. at Ga. Tech. He works in feedback control, intelligent systems, distributed control systems, and sensor networks. He is author of 6 U.S. patents, 216 journal papers, 330 conference papers, 14 books, 44 chapters, and 11 journal special issues. He received the Fulbright Research Award, NSF Research Initiation Grant, ASEE Terman Award, Int. Neural Network Soc. Gabor Award 2009, U.K. Inst Measurement & Control Honeywell Field Engineering Medal 2009. Received Outstanding Service Award from Dallas IEEE Section, selected as Engineer of the year by Ft. Worth IEEE Section. Listed in Ft. Worth Business Press Top 200 Leaders in Manufacturing. Received the 2010 IEEE Region 5 Outstanding Engineering Educator Award and the 2010 UTA Graduate Dean’s Excellence in Doctoral Mentoring Award. Elected to UTA Academy of Distinguished Teachers 2012. He served on the NAE Committee on Space Station in 1995. He is an elected Guest Consulting Professor at South China University of Technology and Shanghai Jiao Tong University. Founding Member of the Board of Governors of the Mediterranean Control Association. Helped win the IEEE Control Systems Society Best Chapter Award (as Founding Chairman of DFW Chapter), the National Sigma Xi Award for Outstanding Chapter (as President of UTA Chapter), and the US SBA Tibbets Award in 1996 (as Director of ARRI’s SBIR Program).

Abstract:
This talk will discuss some new adaptive control structures for learning online the solutions to optimal control problems and multi-player differential games. Techniques from reinforcement learning are used to design a new family of adaptive controllers based on actor-critic mechanisms that converge in real time to optimal control and game theoretic solutions. Continuous-time systems are considered. A new sort of distributed game is defined- namely multi-agent graphical games, where the interactions between players are restricted by a distributed communication topology.
Optimal feedback control design has been responsible for much of the successful performance of engineered systems in aerospace, industrial processes, vehicles, ships, robotics, and elsewhere since the 1960s. Optimal control design is performed offline by solving optimal design equations including the algebraic Riccati equation and the Game ARE. It is difficult to perform optimal designs for nonlinear systems since they rely on solutions to complicated Hamilton-Jacobi-Bellman or HJI equations. Finally, optimal design generally requires that the full system dynamics be known.
Optimal Adaptive Control. Adaptive control has provided powerful techniques for online learning of effective controllers for unknown nonlinear systems. In this talk we discuss online adaptive algorithms for learning optimal control solutions for continuous-time linear and nonlinear systems. This is a novel class of adaptive control algorithms that converge to optimal control solutions by online learning in real time. In the linear quadratic (LQ) case, the algorithms learn the solution to the ARE by adaptation along the system motion trajectories. In the case of nonlinear systems with general performance measures, the algorithms learn the (approximate smooth local) solutions of HJ or HJI equations. The algorithms are based on actor-critic reinforcement learning techniques.
Multi-Player Differential Games. New algorithms will be presented for solving online non zero-sum multi-player games for continuous-time systems. We use an adaptive control structure motivated by reinforcement learning policy iteration. The result is an adaptive control system with multiple tuned control loops that learns based on the interplay of agents in a game, to deliver true online gaming behavior.
Graphical Games. A new formulation for control of multi-agent cooperative systems is given. A novel form of game among agents in a communication graph is formulated where each agent is allowed to interact only with its neighbors. A new notion of Nash equilibrium is defined that is suitable for graphical games.

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Iven Mareels got the PhD in Systems Engineering from the Australian National University, Canberra, Australia in 1987. Since 1996, he is a Professor of Electrical and Electronic Engineering in the Department of Electrical and Electronic Engineering, the University of Melbourne. In June 2007, he became Dean of the School of Engineering, the University of Melbourne. He was a recipient of a 2008 Clunies Ross Award, Academy of Technological Sciences and Engineering. In 2007 he received the inaugural Vice-Chancellor's Knowledge Transfer Excellence award from the University of Melbourne, for his work in large scale irrigation systems with Rubicon Systems Australia. In 2005, he was named IEEE CSS Distinguished Lecturer, and in 1994 received the Vice-Chancellor's Award for Excellence in Teaching from the Australian National University. He was a co-editor in chief of Systems & Control Letters till December 2007. He is Fellow of the Academy of Technological Sciences and Engineering, Australia, a Fellow of the Institute of Electrical and Electronics Engineers (USA), a member of the Society for Industrial and Applied Mathematics, a Fellow of the Institute of Engineers Australia. He is a Member of the Royal Flemish Belgian Academy of Sciences and Humanities. He is registered as a Corporate Professional Engineer and he is a member of the Engineering Executives chapter of Engineers Australia. He is a founding member of the Asian Control Association. Iven Mareels has published 5 books, in excess of 120 journal publications and 230 conference publications. He holds 6 international patents. He has supervised to completion more than 30 PhD students, 10 ME students and is currently supervising 5 PhD students, mainly in bio-signals and bio-control related to brain research.

Abstract:
Epilepsy is a debilitating brain disorder of relatively high prevalence (almost 2%). For nearly a quarter of people suffering from epilepsy (which is really a family of mental illnesses) there is no real satisfactory treatment. An ability to predict reliably the onset of an epileptic episode could dramatically improve the quality of life for many epilepsy sufferers through the use of fast acting drugs, or electrical brain tissue stimulation to prevent seizures from developing fully.
In the context of epileptic episode identification and prediction the electroencephalogram (EEG) signal has played a central role for over 5 decades. Yet, despite all the exploration of the electroencephalogram (EEG) signal, to date no acceptable prediction algorithm, that is patient independent, and has an acceptably low false alarm rate, has been identified. In contrast, the problem of identifying epileptic episodes through post processing of EEG traces has progressed well. Some of this work will be briefly reviewed.
After the review of the signal basics of EEG, we discuss why from our point of view the EEG signal is in fact by itself not informative enough to yield a reliable prediction of the next epileptic event. The main rationale is based on an information theoretic analysis of the EEG signal, which reveals that the classical EEG information content falls short of what is required for prediction in general, and epilepsy prediction in particular. Simply put, the classic EEG does not have sufficient signal value and time resolution, nor sufficient time to be able to provide a reliable prediction of the next epileptic episode. This is due to the complexity of the underlying dynamics.
What is worse, the quest for a "sufficiently rich" EEG signal may be futile. This observation, although not proven in full, is demonstrated using a rather simple abstraction of the prediction problem at hand and showing that even this much simplified problem will not yield to EEG observations.
Finally, we introduce a promising alternative observation technique, based on active electrical stimulation, that may overcome some of the deficiencies identified in the passive or classic EEG.

References:
(1) Andrea Varsavsky, Iven Mareels, Mark Cook, Epileptic Seizures and the EEG, CRC Press, 2011
(2) Epilepsy: The intersection of neurosciences, biology, mathematics, engineering, and physics edited by Ivan Osorio, Hitten Zaveri, Mark Frei, Susan Arthurs, CRC press, 2011
(3) O'Sullivan-Greene, E. (2011). Re-evaluating electroencephalography for epileptic seizure prediction. PhD thesis, Engineering, Department of Electrical & Electronic Engineering, Melbourne School of Engineering and National ICT Australia, The University of Melbourne.


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