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Mitsubishi Electric

National Electric





2010 American Control Conference -- ACC2010
Baltimore, Maryland, USA
June 30 - July 2, 2010



Wednesday, June 30, 2010


Graham Goodwin

Graham C. Goodwin, The University of Newcastle


Physical systems typically operate in continuous time whereas modern controllers and signal processing devices operate in discrete time. Hence sampling arises as a cornerstone problem in essentially all aspects of modern systems science. This presentation will review various aspects of sampling of signals and systems. It will be argued that careful consideration must be given to sampling if meaningful results are to be obtained when interconnecting a physical system to a computer for the purpose of data storage, signal processing, or control. The presentation will also take the opportunity to dispel several common misconceptions about sampling and sampled data systems.

This presentation is based on a paper coauthored by Juan Carlos Agüero, Mauricio Cea, and Juan Yuz.

Biographical Sketch:
Graham Goodwin obtained a B.Sc. (Physics), B.E. (Electrical Engineering), and Ph.D from the University of New South Wales. He is currently Professor Laureate of Electrical Engineering at the University of Newcastle, Australia and is Director of an Australian Research Council Centre of Excellence for Complex Dynamic Systems and Control. He holds Honorary Doctorates from Lund Institute of Technology, Sweden, and the Technion, Israel. He is the co-author of eight books, four edited books, and many technical papers. Graham is the recipient of the Control Systems Society 1999 Hendrik Bode Lecture Prize, a Best Paper award from the IEEE Transactions on Automatic Control, a Best Paper award from the Asian Journal of Control, and 2 Best Engineering Textbook Awards from the International Federation of Automatic Control in 1984 and 2005. In 2008 he received the Quazza Medal from the International Federation of Automatic Control. He is an IEEE Fellow, an Honorary Fellow of the Institute of Engineers (Australia), a Fellow of the International Federation of Automatic Control, a Fellow of the Australian Academy of Science, a Fellow of the Australian Academy of Technology, Science and Engineering, a Member of the International Statistical Institute, a Fellow of the Royal Society, London, and a Foreign Member of the Royal Swedish Academy of Sciences.

Thursday, July 1, 2010


Russell H. Taylor, John Hopkins University

Medical Robotics and Computer-Integrated Surgery

The impact of Computer-Integrated Surgery (CIS) on medicine in the next 20 years will be as great as that of Computer-Integrated Manufacturing on industrial production over the past 20 years. A novel partnership between human surgeons and machines, made possible by advances in computing and engineering technology, will overcome many of the limitations of traditional surgery. By extending human surgeons’ ability to plan and carry out surgical interventions more accurately and less invasively, CIS systems will address a vital national need to greatly reduce costs, improve clinical outcomes, and improve the efficiency of health care delivery. As CIS systems evolve, we expect to see the emergence of two dominant and complementary paradigms: Surgical CAD/CAM systems will integrate accurate patient-specific models, surgical plan optimization, and a variety of execution environments permitting the plans to be carried out accurately, safely, and with minimal invasiveness. Surgical Assistant systems will work cooperatively with human surgeons in carrying out precise and minimally invasive surgical procedures. Over time, these will merge into a broader family of systems that couple information to action in interventional medicine.

The overall information flow associated with CIS systems is illustrated in Figure 1. These systems combine images and other information about an individual patient with “atlas” information about human anatomy to help clinicians plan how to treat the patient. In the operating room, the patient-specific plan and model are updated using images and other real-time information. The system has a variety of means, including robots and “augmented reality” displays to assist the surgeon in carrying out the procedure safely and accurately. The same technology will be used to assist in subsequent patient follow-up and in enabling statistical quality control to help improve the overall efficacy and safety of surgery and interventions.

CIS research inherently involves three synergistic areas: a) modeling and analysis of patients and surgical procedures in order to support more effective planning, execution assistance, and follow-up of surgical procedures; b) interface technology, including robots and sensors, connecting the “virtual reality” of computer models and surgical plans to the “actual reality” of the operating room, patients, and surgeons; and c) systems science to develop improved techniques for ensuring the safety and reliability of systems, for characterizing expected performance in the presence of uncertainty, for analysis of how subsystems and components will interact, and for system performance validation. Examples are shown in Figure 2.

This talk will explore these themes, with examples drawn from our own research and elsewhere.

Biographical Sketch:
Russell H. Taylor received his Ph.D. in Computer Science from Stanford in 1976. He joined IBM Research in 1976, where he developed the AML robot language and managed the Automation Technology Department and (later) the Computer-Assisted Surgery Group before moving in 1995 to Johns Hopkins, where he is as a Professor of Computer Science with joint appointments in Mechanical Engineering, Radiology, and Surgery, and is Director of the NSF Engineering Research Center for Computer-Integrated Surgical Systems and Technology. He is the author of more than 230 refereed publications, a Fellow of the IEEE (1994), AIMBE (1999), MICCAI Society (2009), and the Engineering School of the University of Tokyo (2009). He is a recipient of the Maurice E. Mller Award for Excellence in Computer Assisted Surgery (2000) and of the IEEE Robotics and Automation Society Pioneer Award (2008), as well as several earlier awards for his work at IBM.




Mario Milanese

Mario Milanese, Politecnico di Torino

Control as a Key Technology for a Radical Innovation in Wind Energy Generation

The talk discusses the role of systems and control methodologies in the development of a radical innovation in wind energy technology that may represent a quantum leap in the problem of significantly reducing the global dependence from fossil fuels. Simulation and experimental results are presented regarding a new class of wind energy generators, denoted as Kitenergy, which employ power kites to capture high altitude wind power. A realistic kite model, which includes the kite aerodynamic characteristics and the effects of line weight and drag forces, is used to describe the system dynamics. Nonlinear model predictive control techniques, together with an efficient implementation based on set membership function approximation theory, are employed to maximize the energy obtained, while satisfying input and state constraints. Two different configurations of Kitenergy technology are investigated through numerical simulations, the yo-yo configuration and the carousel configuration, respectively. The analysis of the Kitenergy Capacity Factor based on wind experimental data and the power density (MW/Km2) of Kitenergy yo-yo wind farms are also presented. Experimental data, collected using the small-scale Kitenergy yo-yo prototype built at Politecnico di Torino, are compared to simulation results. The good matching between simulation and real measured data increases the confidence with the presented simulation results, which show that high altitude wind energy generation with controlled power kites has the potential of obtaining renewable energy from a source largely available almost everywhere, with production costs lower than those of fossil sources, thus representing a quantum leap in the problem of significantly reducing the global dependence from fossil fuels in a relatively short time.

Biographical Sketch:
Mario Milanese graduated in electronic engineering at Politecnico di Torino, Torino, Italy, in 1967. From 1968 he was assistant professor at Politecnico di Torino and from 1972 associate professor at the University of Torino. Since 1980, he has been a full professor of system theory at Politecnico di Torino. From 1982 to 1987, he was head of the Dipartimento di Automatica e Informatica at the Politecnico di Torino.

Mario’s research interests include identification, prediction, filtering and control of complex systems with applications to biomedical, automotive, aerospace, financial, environmental, and energy problems. He is the author of more than 250 papers on these topics in international journals and conference proceedings. He is Co-Editor of the books Robustness in Identification and Control, Plenum Press, 1989 and Bounding Approaches to System Identification, Plenum Press, 1996. He is inventor of 5 patents on the control of semiactive suspensions and on the control of tethered airfoils for wind energy generation. He has been responsible of several projects with industrial companies and European and national organizations on modeling and control in automotive, aerospace, biomedical, and renewable energy fields. Honors include the International Huspy Award for Artificial Intelligence in Medicine in 1984 and the Japan Society for the Promotion of Sciences Fellowship in 1999.


Friday, July 2, 2010


Naomi Leonard, Princeton University

Cooperative Control and Mobile Sensor Networks in the Ocean

The recent proliferation of autonomous vehicles and advanced sensing technologies has unleashed a pressing and challenging demand for design of adaptive and sustainable observation and prediction systems to improve understanding of natural dynamics and human-influenced changes in the environment. A central problem is designing motion planning and control for networks of sensor-equipped autonomous vehicles that yield efficient collection of information-rich data. I will present collaborative work in formulating provable cooperative strategies for mobile sensor networks to follow features and to cover areas with time-varying spatially-distributed fields.

While the tools have general applicability, I will describe application to design of an underwater glider network for autonomous ocean sampling and present results from two major field experiments in Monterey Bay, California. In the most recent field experiment, the coordinated vehicle network ran autonomously for 24 days straight, working in combination with real-time data-assimilating ocean models to observe and predict conditions over a large coastal region.

Biographical Sketch:
Naomi Ehrich Leonard is the Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty member of the Program in Applied and Computational Mathematics at Princeton University where she has been since 1994. Her research is in nonlinear control and dynamics with current interests in collective motion and decision making in multi-agent systems, mobile sensor networks and adaptive ocean sampling, collective behavior in fish schools and bird flocks, and decision dynamics in mixed teams of humans and robots. She became an IEEE Fellow in 2007 and received the Mohammed Dahleh Award (2005), John D. and Catherine T. MacArthur Foundation Fellowship (2004), Automatica Prize Paper award (1999), ONR Young Investigator Award (1998), and NSF CAREER Award (1995). She received the B.S.E. degree in mechanical engineering from Princeton University in 1985. From 1985 to 1989, she worked as an engineer in the electric power industry. She received the M.S. and Ph.D. degrees in electrical engineering from the University of Maryland in 1991 and 1994.

Paulo Tabuada, University of California at Los Angeles



Paulo Tabuada, University of California at Los Angeles

Bisimulation: From Differential Equations to Finite-State Machines and Back

The notion of bisimulation was originally introduced in the 1980s to study the equivalence of software processes. This talk shows that a suitable variation of this notion can be used to establish an equivalence between differential equations and finite-state machines. Using this equivalence as a bridge between control theory and computer science, it is shown how it is possible to synthesize correct-by-design embedded control software. This approach contrasts with the current paradigm where the emphasis is placed on the verification of already designed software rather than on the design process. Along the way, well established systems and control theoretic results are revisited and new results are discovered.

Biographical Sketch:
Paulo Tabuada received his “Licenciatura” degree in Aerospace Engineering from the Instituto Superior Tecnico, Lisbon, Portugal in 1998 and Ph.D. degree in Electrical and Computer Engineering in 2002 from the Institute for Systems and Robotics, which is a private research institute associated with Instituto Superior Tecnico. He was a postdoctoral researcher at the University of Pennsylvania from 2002-2003. After spending three years at the University of Notre Dame as an Assistant Professor, he joined the Electrical Engineering Department at the University of California at Los Angeles. Paulo Tabuada was the recipient of the Francisco de Holanda prize in 1998, for the best research project with an artistic or aesthetic component, awarded by the Portuguese Science Foundation. He received a NSF CAREER award in 2005 and the Donald P. Eckman Award from the American Automatic Control Council in 2009. He co-chaired the International Conference Hybrid Systems: Computation and Control (HSCC'09), and currently serves as an associate editor for the IEEE Embedded Systems Letters. His latest book, on verification and control of hybrid systems, was published by Springer in 2009. His research interests include modeling, analysis, and control of real-time, embedded, networked, and distributed systems; geometric control theory; and mathematical systems theory.