Your IP: 18.104.22.168
Total Page views:32607
Unique visitors: 73192
(Since March 17th,
Aside from the technical sessions, the 2012 CDC will feature one plenary lecture, four semi-plenary lectures and the Bode Lecture.
The Bode Lecture will be presented by Prof. Jessy Grizzle
of the EECS Department,
University of Michigan, Ann Arbor.
The Plenary speaker will be
Prof. Kameshwar Poolla
Electrical Engineering & Computer Sciences and
departments, University of California, Berkeley.
The semi-plenary speakers will be
Prof. Magnus Egerstedt of the Georgia Institute of Technology,
Prof. Mustafa Khammash of the Swiss Federal Institute of Technology (ETHZ),
Prof. John Lygeros of the Swiss Federal Institute of Technology (ETHZ) and
Prof. Mario Sznaier of the Northeastern University.
|Bode Lecture, Plenaries and Semi-Plenaries: |
EECS Department, University of Michigan.
Thursday Dec. 13, 8:30-9:30 AM, Haleakala Ballroom
Bode Lecture Title:
Highly Agile and Robust Robotic Bipedal Locomotion Through Nonlinear Geometric Control
Bode Lecture Abstract.
Dynamic models for bipedal robots contain both continuous and discrete elements, with switching events that are spatially driven by unilateral constraints at ground contact and impulse-like forces that occur at foot touchdown. The complexity of the models has led to a host of ad hoc trial-and-error feedback designs. This presentation will show how nonlinear feedback control methods are providing model-based solutions that also enhance the ability of bipedal robots to walk, run, and recover from stumbles. The talk addresses both theoretical and experimental aspects of bipedal locomotion. Videos of the some of the experiments have been covered in the popular press, bringing feedback control to the attention of the general public.
Jessy Grizzle received the Ph.D. in electrical engineering from The
University of Texas at Austin in 1983 and in 1984 held an NSF-NATO
Postdoctoral Fellowship in Paris, France. Since September 1987, he
has been with The University of Michigan, Ann Arbor, where he is a
Professor of Electrical Engineering and Computer Science and is the
Jerry and Carol Levin Professor of Engineering. He jointly holds
sixteen patents dealing with emissions reduction in passenger
vehicles through improved control system design. Professor Grizzle
is a Fellow of the IEEE and IFAC. He received the Paper of the Year
Award from the IEEE Vehicular Technology Society in 1993, the
George S. Axelby Award in 2002 and the Control Systems Technology
Award in 2003. His work on bipedal locomotion has been the object
of numerous plenary lectures and has been featured in The
Economist, Wired Magazine, Discover Magazine, Scientific American,
CNN, ESPN, and many others.
Cadence Distinguished Professor,
Electrical Engineering & Computer Sciences and
University of California, Berkeley.
Monday Dec. 10, 8:30-9:30 AM, Haleakala Ballroom
The Grid with Intelligent Periphery
Pressing environmental problems, energy supply security issues, and nuclear power safety concerns drive the worldwide interest in renewable energy. Renewable energy sources such as wind and solar exhibit variability: they are not dispatchable, exhibit large fluctuations, and are uncertain. Variability is the most important obstacle to deep integration of renewable generation. The current approach is to absorbthis variability in operating reserves. This works at today’s modest penetration levels. But it will not scale. At deep penetration levels (>30%) the levels of necessary reserves are economically untenable, and defeat the net carbon benefit.
So how can we economically enable deep penetration of renewable generation? The emerging consensus is that much this new generation must be placed at hundreds of thousands of locations in the distribution system, and that the attendant variability can be absorbed by the coordinated aggregation and control of distributed resources such as storage, programmable loads, and smart appliances. Tomorrow’s grid will have an intelligent periphery.
We will explore the architectural and algorithmic components for managing this intelligent periphery. Clusters of distributed energy resources are coordinated to efficiently and reliably offer services (ex: bulk power, regulation) in theface of uncertainty (ex: renewables, consumers). We begin by formulating a general class of stochastic dynamic programming problems that arise in the context of coordinated aggregation. We then consider specific real-time scheduling problems for allocating power to resources. We show that no causal optimal policy exists that respects rate constraints (ex: maximum EV charging rates). Next, we explore the benefits of coordinated aggregation in the metric of operating reserves savings. We close by suggesting several challenging problems in monetizing and incentivizing resource participation.
Kameshwar Poolla received the Ph.D. degree from the University of Florida, Gainesville in 1984. He has served on the faculty of the University of Illinois, Urbana from 1984 to 1991.
Since then, he has been with the University of California, Berkeley where he is the Cadence Distinguished Professor of Mechanical Engineering and Electrical Engineering & Computer Sciences.
He also serves as the Founding Director of the IMPACT Center for Integrated Circuit manufacturing at the University of California.
Dr. Poolla co-founded OnWafer Technologies which offers metrology based yield enhancement solutions for the semiconductor industry. OnWafer was acquired by KLA-Tencor in 2007.
He has also served as a technology and mergers/acquisitions consultant for Cadence Design Systems.
Dr. Poolla has been awarded a 1988 NSF Presidential Young Investigator Award, the 1993 Hugo Schuck Best Paper Prize, the 1994 Donald P. Eckman Award, the 1998 Distinguished Teaching Award of the University of California, the 2005 and 2007 IEEE Transactions on Semiconductor Manufacturing Best Paper Prizes, and the 2009 IEEE CSS Transition to Practice Award.
His current research interests covers many aspects of the Smart Grid: Renewable Integration, Demand Response, Cybersecurity, Experimental Economics, and Sensor Networks.
Magnus Egerstedt, School of Electrical and Computer Engineering,
Georgia Institute of Technology.
Tuesday Dec. 11, 8:30 - 9:30 AM, Haleakala Ballroom 4-5
Control of Multi-Robot Systems: From Formations to Human-Swarm Interactions
The last few years have seen significant progress in our understanding of how one should structure multi-robot systems. New control, coordination, and communication strategies have emerged and in this talk, we summarize some of these developments. In particular, we will discuss how to go from local rules to global behaviors in a systematic manner in order to achieve distributed geometric objectives, such as achieving and maintaining formations, area coverage, and swarming behaviors. We will also investigate how users can interact with networks of mobile robots in order to inject new information and objectives. The efficacy of these interactions depends directly on the interaction dynamics and the structure of the underlying information-exchange network. We will relate these network-level characteristics to controllability and manipulability notions in order to produce effective human-swarm interaction strategies.
Magnus Egerstedt is a Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology He received the M.S. degree in Engineering Physics and the Ph.D. degree in Applied Mathematics from the Royal Institute of Technology in 1996 and 2000 respectively, and he received a B.A. degree in Philosophy from Stockholm University in 1996. Dr. Egerstedt's research interests include hybrid and networked control, with applications in motion planning, control, and coordination of mobile robots. Magnus Egerstedt is a Fellow of the IEEE, serves as Editor for Electronic Publications for the IEEE Control Systems Society, and is the director of the Georgia Robotics and Intelligent Systems Lab. He received the ECE/GT Outstanding Junior Faculty Member Award in 2005, and the CAREER award from the U.S. National Science Foundation in 2003.
Mustafa Khammash, Swiss Federal Institute of Technology (ETHZ).
Wednesday Dec. 12, 8:30 - 9:30 AM, Haleakala Ballroom 2-3
Cyborg Yeast: Feedback Control of Cell Populations
A hallmark of living cells is their inherent stochasticity. Stochastic molecular noise in individual cells manifests as cell-to-cell variability within a population of genetically identical cells. While experimental tools have enabled the measurement and quantification of variability of populations consisting of millions of cells, new modeling and analysis tools have lead to a substantial improvement in our understanding of the stochastic nature of living cell populations and its biological role. More recently, these developments came together to pave the way for the real-time control of living cells.
In this presentation, we describe novel analytical and experimental work that demonstrates how a computer can be interfaced with living cells and used to control their behavior. We discuss how computer controlled light pulses, in combination with a genetically encoded light-responsive module and a flow cytometer can be configured to achieve precise and robust set-point regulation of gene expression in the noisy environment of the cell. We then address the theoretical, computational, and practical issues concerning the feedback control of single cells as well as cell populations. Aside from its potential applications in biotechnology and therapeutics, this approach opens up exciting opportunities for the development of new control theoretic methods aimed at confronting the unique challenges of manipulating the dynamic behavior of living cells.
Mustafa Khammash is the Professor of Control Theory and Systems Biology at the Department of Biosystems Science and Engineering (D-BSSE) at the Swiss Federal Institute of Technology in Zurich (ETHZ). From 2006 till 2011 he served as the Director of the Center for Control, Dynamical systems, and Computations (CCDC) at the University of California at Santa Barbara (UCSB) where he held a Professor appointment in the Mechanical Engineering department since 2001. Before joining UCSB he was on the faculty of the Electrical Engineering department at Iowa State University, a position he held since completing his Ph.D. at Rice University in 1990.
Dr. Khammash currently works in the areas of control theory, systems biology, and synthetic biology. His research strives to understand the role of dynamics, feedback, and randomness in biology, and to develop the tools needed to aid in this understanding. Khammash is a Fellow of the IEEE, IFAC, and the Japan Society for the Promotion of Science. He is the recipient of the National Science Foundation Young Investigator Award, the Iowa State University Foundation Early Achievement in Research and Scholarship Award, the ISU College of Engineering Young Faculty Research Award, and the Ralph Budd Best Engineering PhD Thesis Award.
Automatic Control Laboratory, ETH Zurich.
Tuesday Dec. 11, 8:30 - 9:30 AM, Haleakala Ballroom 2-3
Estimation and Identification of Population Systems
Systems comprising populations of subsystems are common in engineering and the natural sciences.
This is the case, for example, with cultures of cell populations, highway transportation systems and recent
approaches to electrical power demand response using populations of small loads. Population systems offer novel
challenges from a systems and control perspective. For example, population systems may exhibit a combination of
natural stochasticity in the evolution of individuals, as well as variability in the dynamics across individuals,
giving rise to a wide distribution of behaviors. Measurements can often only be taken at the population level,
providing a snapshot of this distribution across individuals. Similarly, control signals are often available at
the population level, leaving control engineers with the task of steering the entire population distribution using
only macroscopic commands.
This talk will touch upon modeling, analysis and system identification issues arising in population systems.
We will investigate how modeling and analysis methods can be extended to account for stochasticity both at the
individual and at the population level. We will also discuss how state estimation and system identification can
be carried out using population level measurements, despite these diverse sources of variability.
The developments will be motivated by and applied to specific problems in systems biology.
The potential of the methods, however, is not restricted to biology and extends to numerous problems in engineering
John Lygeros is a Professor of Computation and Control at the Swiss Federal Institute of Technology (ETH) Zurich, Switzerland and is currently serving as the Head of the Automatic Control Laboratory. He completed a B.Eng. degree in electrical engineering in 1990 and an M.Sc. degree in Systems Control in 1991, both at Imperial College of Science Technology and Medicine, London, U.K.. In 1996 he obtained a Ph.D. degree from the Electrical Engineering and Computer Sciences Department, University of California, Berkeley. During the period 1996-2000 he held a series of research appointments at the National Automated Highway Systems Consortium, Berkeley, the Laboratory for Computer Science, M.I.T., and the Electrical Engineering and Computer Sciences Department at U.C. Berkeley. Between 2000 and 2003 he was a University Lecturer at the Department of Engineering, University of Cambridge, U.K., and a Fellow of Churchill College. Between 2003 and 2006 he was an Assistant Professor at the Department of Electrical and Computer Engineering, University of Patras, Greece. In July 2006 he joined the Automatic Control Laboratory at ETH Zurich, first as an Associate Professor, and since January 2010 as Full Professor. His research interests include modeling, analysis, and control of hierarchical, hybrid, and stochastic systems, with applications to biochemical networks, automated highway systems, air traffic management, power grids and camera networks. John Lygeros is a Fellow of the IEEE, and a member of the IET and the Technical Chamber of Greece.
Dennis Picard Trustee Professor,
Electrical and Computer Engineering Department,
Wednesday Dec. 12, 8:30 - 9:30 AM, Haleakala Ballroom 4-5
Taming the Upcoming Data Deluge: A Systems and Control Perspective
The past few years have witnessed a revolution in data collection capabilities:
The development of low cost, ultra low power sensors capable of harvesting
energy from the environment has rendered ubiquitous sensing feasible. When
coupled with a parallel growth in actuation capabilities, these developments
open up the possibility of new control applications that can profoundly impact
society, ranging from zero-emissions buildings to ``smart" grids and managed
aquifers to achieve long term sustainable use of scarce resources. A major
road-block to realizing this vision stems from the curse of dimensionality. To
successfully operate in these scenarios, controllers will need to timely
extract relevant, actionable information from the very large data streams
generated by the ubiquitous sensors. However, existing techniques are
ill-equipped to deal with this "data avalanche."
This talk discusses the central role that systems theory can play in
developing computationally tractable, scalable methods for extracting
actionable information that is very sparsely encoded in high dimensional data
streams. The key insight is the realization that actionable information can be
often represented with a small number of invariants associated with an
underlying dynamical system. Thus, in this context, the problem of actionable
information extraction can be reformulated as identifying these invariants
from (high dimensional) noisy data, and thought of as a generalization of
sparse signal recovery problems to a dynamical systems framework. While in
principle this approach leads to generically nonconvex, hard to solve
problems, computationally tractable relaxations (and in some cases exact
solutions) can be obtained by exploiting a combination of elements from convex
analysis and the classical theory of moments. These ideas will be illustrated
using examples from several application domains, including autonomous
vehicles, computer vision, systems biology and economics. We will conclude the
talk by exploring the connection between hybrid systems identification,
information extraction, and machine learning, and point out to new research
directions in systems theory motivated by these problems.
Mario Sznaier is currently the Dennis Picard Chaired Professor at the Electrical
and Computer Engineering Department, Northeastern University, Boston. Prior to
joining Northeastern University, Dr. Sznaier was a Professor of Electrical
Engineering at the Pennsylvania State University and also held visiting
positions at the California Institute of Technology. His research interest
include robust identification and control of hybrid systems, robust
optimization, and dynamical vision. Dr. Sznaier is currently serving as an
associate editor for the journal Automatica and as a member of the Board of
Governors of the IEEE Control Systems Society. Additional recent service
includes CSS Executive Director (2007-2011), Program Chair of the 2009 IFAC
Symposium on Robust Control Design, and Program vice-chair of the 2008 IEEE
Conf. on Decision and Control. Dr. Sznaier was a plenary speaker at the 2009
and 2010 Int. Conference on the Dynamics of Information Systems, the 2012 IFAC
Symposium on Robust Control Design, 2012 IFAC
Symposium on System Identification and the 2012 Mediterranean Control
Conference. A list of publications and current research projects can be found
Tutorials: We will have five tutorials throughout the four conference days. Tutorials have been
arranged by invitation and will correspond to the following hot topics:
Control of Nonlinear Delay Systems|
Wednesday, 2:00 - 4:00, Haleakala Ballroom 5
(University of California, San Diego, USA)
Delays are ubiquitous in applications, such as automotive control systems, networked control systems, traffic control, and biological systems. The presence of delays can severely degrade the performance of a control system. Major progress has been made by a community of researchers over the past decade in developing methodologies for control of nonlinear systems with actuator, measurement, or internal delays. However, most of the results have dealt with constant delays. In this tutorial we present a unifying methodology, called predictor feedback, for compensating either constant, or non-constant delays in nonlinear control systems. Predictor feedback, which is an infinite-dimensional extension of classical backstepping, achieves compensation of the delay, that is, after the control signal reaches the state of the plant, the state evolves as if there were no delay.
The goal of the tutorial is to introduce the audience to the concepts of nonlinear time-varying and state-dependent predictor feedback and infinite-dimensional backstepping. Using these two concepts the problem of compensation of non-constant delays, affecting either the input or the state, is solved. The first talk focuses on nonlinear systems with input delays, whereas the second talk addresses nonlinear systems with simultaneous input and state delays and robustness to delay perturbations.
- "Control of nonlinear systems with input delays", M. Krstic, University of California, San Diego, USA (70 min).
- "Control of nonlinear systems with state delays and robustness to delay perturbations", N. Bekiaris-Liberis, University of California, San Diego, USA (50 min).
Event-triggered and Self-triggered Control|
Tuesday, 2:00 - 4:00, Haleakala Ballroom 5
(University of California, Los Angeles, USA)
Classical sampled-data control is based on periodic sensing and actuation. Due to recent developments in computer and communication technologies, a new type of large scale resource-constrained wireless embedded control systems
is emerging. It is desirable in these systems to limit the sensor and control communication to instances when the system needs attention. This tutorial session will provide an introduction to such event and self-triggered control systems. Event-triggered control is reactive and generates sensor sampling and control actuation when, for instance, the plant state deviates more than a certain threshold from a desired value. Self-triggered control, on the other hand, is proactive and computes the next sampling or actuation instance ahead of time. The basics of these control strategies will be presented together with a discussion on the differences between state feedback and output feedback for event-triggered control. The implementation of event- and self-triggered control using existing wireless communication technology and applications to wireless control in the process industry will also be discussed.
- "Introduction to event-triggered and self-triggered control", P. Tabuada, University of California, Los Angeles, USA (40 min).
- "Output-based event-triggered control", W.P.M.H. Heemels, Eindhoven University of Technology, The Netherlands (40 min).
- "Wireless event-triggered control", K.H. Johansson, Royal Institute of Technology, Sweden (40 min).
Fundamentals of Economic Model Predictive Control |
Tuesday, 4:30 - 6:30, Haleakala Ballroom 3
James B. Rawlings (University of Wisconsin-Madison, USA)
The goal of most current advanced control systems is to guide a
process to a target setpoint rapidly and reliably. Model predictive
control has become a popular technology in many applications because
it can handle large, multivariable systems subject to hard constraints
on states and inputs. The optimal steady-state setpoint is usually
provided by some other information management system that determines,
among all steady states, which is the most profitable. For an
increasing number of applications, however, this hierarchical
separation of information and purpose is no longer optimal or
desirable. A recently proposed alternative to the hierarchical
decomposition is to take the economic objective directly as the
objective function of the control system. In this approach, known as
economic MPC, the controller optimizes directly in real time the
economic performance of the process, rather than tracking to a
setpoint. The purpose of this tutorial is to explain how to design
these kinds of control systems and what kinds of closed-loop
properties one can achieve with them. We cover the following issues:
asymptotic average performance; closed- loop stability and
convergence, strong duality and dissipativity; designing terminal
costs, terminal regions, and terminal periodic constraints. Several
examples are included to illustrate these results.
- "Introduction to economic MPC: average performance, stability, and terminal penalties", James B. Rawlings, University of Wisconsin-Madison, USA (40 min).
- "Dissipativity, periodic terminal constraints, and average constraints in economic MPC", David Angeli, Imperial College of London, UK (60 min).
- "Conclusions and open research issues in economic MPC", James B. Rawlings, University of Wisconsin-Madison, USA (20 mins).
Information Structures in Optimal Decentralized Control|
Monday, 2:00 - 4:00, Haleakala Ballroom 3
Nuno C. Martins
(University of Maryland, College Park, USA)
This tutorial provides a comprehensive characterization of information structures in team decision
problems and their impact on the tractability of team optimization. Solution methods for team decision
problems are presented in various settings where the discussion is structured in two foci: The first is
centered on solution methods for stochastic teams admitting state-space formulations. The second focus is
on norm-optimal control for linear plants under information constraints.
- "Team Decision Theory: Characterization of Information Structures, Basic Concepts and Solution Methods", Aditya Mahajan, McGIll University, Canada
and Serdar Yuksel, Queen's University, Canada (1 hour).
- "Decentralized Control: Stabilizability, Invariance Principles and Parametrizations for Norm-Optimal Design", Nuno C. Martins and Michael C. Rotkowitz, University of Maryland, College Park, USA (1 hour).
Synchronization in Coupled Oscillators: Theory and Applications|
Thursday, 2:00 - 4:00, Haleakala Ballroom 5
(University of California, Santa Barbara, USA)
The emergence of synchronization in networks of coupled oscillators is a
pervasive topic in various scientific disciplines ranging from engineering,
physics, and chemistry to social and biological networks. Coupled
oscillator networks possess fascinating dynamic behavior, are instructive
examples of complex dynamical networks, and have only recently been studied
with control theoretical tools. This tutorial session will discuss a broad
range of models, motivating applications, analysis and control design
methods for the synchronization of coupled oscillators.
- "Exploring Synchronization in Complex Oscillator Networks" by Florian
Dorfler and Francesco Bullo, University of California, Santa Barbara, USA
- "Kick Synchronization versus Diffusive Synchronization" by Alexandre Mauroy, University of California, Santa Barbara, USA,
Pierre Sacré, University of Liège, Belgium, Rodolphe
J. Sepulchre, University of Liège, Belgium
- "Synchronization and Pattern Formation in Diffusively Coupled Systems" by
Murat Arcak, University of California, Berkeley, USA
We suggest that you use Firefox or Chrome instead of Internet Explorer, to prevent copyright upload issues to IEEE.
Key dates (2012)
|Submission Site Open:||January 4|
|Initial Paper |
|Workshop Proposals Due:||May 10|
|Paper and Workshop|
|Final Submission Open:||August 1|
|Registration Opens:||August 1|
|Accepted Papers Due:||September 5|
Click here to see the complete list of sponsors and exhibitors