Control Seminars @ UCI

Time and place: Friday, October 26, 2018
Time: 10:30-11:30am
McDonnell Douglas Engineering Auditorium

Stochastic Vehicle Routing for Max Entropic Surveillance

Francesco Bullo, Mechanical Engineering, UCSB


This talk addresses the design of efficient surveillance and vehicle-routing strategies for robotic networks in dynamic environments. We focus on how to search an area in a persistent manner – with minimal average time to detection, with unpredictable trajectories and with optimally partitioned workload among multiple vehicles. The technical approach is based on Markov chains, optimization methods, convexity properties, relaxations and coordination strategies. Coauthors: Xiaoming Duan, Mishel George.


Francesco Bullo is a professor in the Department of Mechanical Engineering and the Center for Control, Dynamical Systems and Computation at UC Santa Barbara. His research interests focus on network systems and distributed control with application to robotic coordination, power grids and social networks. He is the coauthor of “Geometric Control of Mechanical Systems” (Springer, 2004) and “Distributed Control of Robotic Networks” (Princeton, 2009); his “Lectures on Network Systems” (CreateSpace, 2018) is available on his website. Bullo received best paper awards for his work in IEEE Control Systems, Automatica, SIAM Journal on Control and Optimization, IEEE Transactions on Circuits and Systems, and IEEE Transactions on Control of Network Systems. He is a fellow of the IEEE and IFAC. He has served on the editorial boards of IEEE, SIAM and ESAIM journals, and he serves as the 2018 IEEE CSS president.

Time and place: Friday, October 19, 2018
Time: 1:00-2:00pm
Engineering Gateway 4211

Stochastic Control of Finite and Infinite Dimensional Systems Under Uncertainty: Theory, Algorithms and Applications

Evangelos Theodorou, Aerospace Engineering, GaTech


In this talk, I will present an overview of projects related to stochastic control and machine learning methods and their applications to dynamical systems represented by stochastic differential and stochastic partial differential equations. These are typically systems that exists in autonomy and robotics as well as in areas of applied physics such as fluid mechanics, plasma physics and turbulence. I will discuss different forms of uncertainty representation that span Gaussian Processes, Polynomial Chaos, Deep Probabilistic Neural Networks and Q-Wiener processes. Finally I will show applications to robotic terrestrial agility, perceptual control, social networks, large scale swarms, and control of stochastic fields, and conclude with future directions.


Evangelos A. Theodorou is an assistant professor with the Guggenheim School of aerospace engineering at Georgia Institute of Technology. He is also affiliated with the Institute of Robotics and Intelligent Machines. Evangelos Theodorou earned his Diploma in Electronic and Computer Engineering from the Technical University of Crete (TUC), Greece in 2001. He has also received a MSc in Production Engineering from TUC in 2003, a MSc in Computer Science and Engineering from University of Minnesota in spring of 2007 and a MSc in Electrical Engineering on dynamics and controls from the University of Southern California(USC) in Spring 2010. In May of 2011 he graduated with his PhD, in Computer Science at USC. After his PhD, he was a Postdoctoral Research Fellow with the department of computer science and engineering, University of Washington, Seattle. Evangelos Theodorou is the recipient of the King-Sun Fu best paper award of the IEEE Transactions on Robotics for the year 2012 and recipient of the best paper award in cognitive robotics in International Conference of Robotics and Automation 2011. He was also the finalist for the best paper award in International Conference of Humanoid Robotics 2010, International Conference of Robotics and Automation 2017 and Robotics Science and Systems 2018. His theoretical research spans the areas of stochastic optimal control theory, machine learning, information theory and statistical physics. Applications involve learning, planning and control in autonomous, robotics and aerospace systems.

Time and place: Friday, April 13, 2018
Time: 10:30-11:30am
McDonnell Douglas Engineering Auditorium

Wind Farm Modeling and Control for Power Grid Support

Dennice Gayme, Johns Hopkins University


Traditional wind farm modeling and control strategies have focused on layout design and maximizing wind power output. However, transitioning into the role of a major power system supplier necessitates new models and control designs that enable wind farms to provide the grid services that are often required of conventional generators. This talk introduces a model-based wind farm control approach for tracking a time-varying power signal, such as a power grid frequency regulation command. The underlying time-varying wake model extends commonly used static models to account for wake advection and lateral wake interactions. We perform numerical studies of the controlled wind farm using a large eddy simulation (LES) with actuator disks as a wind farm model with local turbine thrust coefficients (synthetic pitch) as the control actuation. Our results show that embedding this type of dynamic wake model within a model-based receding-horizon control framework leads to a controlled wind farm that qualifies to participate in markets for correcting short-term imbalances in active power generation and load on the power grid (frequency regulation). Accounting for the aerodynamic interactions between turbines within the proposed control strategy yields large increases in efficiency over prevailing approaches by achieving commensurate up-regulation with smaller derates (reductions in wind farm power set points). This potential for derate reduction has important economic implications because smaller derates directly correspond to reductions in the loss of bulk power revenue associated with participating in regulation markets.


Dennice F. Gayme is an assistant professor in mechanical engineering and the Carol Croft Linde Faculty Scholar at the Johns Hopkins University. She earned her bachelor's degree from McMaster University in 1997 and a master's degree from the UC Berkeley in 1998, both in mechanical engineering. She received her doctorate in control and dynamical systems in 2010 from the California Institute of Technology. Her research interests are in modeling, analysis and control for spatially distributed and large-scale networked systems in applications such as wall-bounded turbulent flows, wind farms, power grids and vehicular networks. She was a recipient of the JHU Catalyst Award in 2015, a 2017 ONR Young Investigator award, and an NSF CAREER award in 2017.

Time and place: Wednesday, March 14, 2018
Time: 11-12 noon
Seminar Room 3008 - Calit2

Towards Computationally Scalable Vision-Based Navigation

Patricio Vela, Georgia Tech


Navigation by autonomous vehicles or other forms of unmanned autonomous systems is a rapidly developing area within Robotics. Advances in technology and manufacturing mean that it is possible to deploy robots that span a couple orders of magnitude in size and available on-board computation. Our lab is interested in identifying a common vision-based navigation framework that can scale across the diversity of autonomous platforms envisioned by roboticists. Central to this vision is a means to minimally process visual information while maximally extracting task relevant information. We propose to employ a perception space representation which aligns with Marr’s 2.5D sketch, and to integrate it with best practice solutions in the perceive-plan-act robotics pipeline. Furthermore, we explore how learning-based strategies can provide constant-time outputs compatible with this pipeline. In achieving both objectives we can approach the goal of realizing computationally scalable visual navigation.


Patricio A. Vela is an associate professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. Dr. Vela's research focuses on geometric perspectives to control theory and computer vision, particularly how concepts from control and dynamical systems theory can serve to improve computer vision algorithms used in the decision-loop. More recent efforts expanding his research program involve studying the role of machine learning in adaptive control and autonomous robotics, and investigating how modern advances in adaptive and optimal control theory may improve locomotion effectiveness for biologically-inspired robotics. These efforts support a broad program to understand important research challenges associated with autonomous robotic operation in uncertain environments. Dr. Vela received a B.S. and a Ph.D. from the California Institute of Technology.

Time and place: Wednesday, March 7, 2018,1:30-2:30pm
EH 2430 (Colloquia Room)

Electronic Traps for Mechanical Waves: A framework for piezo-enabled tunability of elastic waves

Stefano Gonella, University of Minnesota


One of the main challenges in the design of versatile engineering devices is achieving tunability, i.e., the ability to tune a system's response to an evolving operating environment. In the context of vibration control, for example, this can lead to the design of semi-active mechanical filters. The opportunities are even broader in the realm of wave control, where one can engineer or boost the spectral and spatial wave manipulation capabilities of a mechanical system. The piezoelectric route to tunable structures relies on the use of piezoelectric elements to actively modify their inherent mechanical response. Of particular interest are methods involving shunts, whereby piezoelectric patches are passively connected to appropriately designed electric circuits, to yield a modification of the effective properties of the material and a correction of the global behavior of the host medium. This presentation focuses on the special class of resistive-inductive (RL) circuits, which de facto act as electro-mechanical resonators. When properly tuned, these resonators interact with a propagating wavefield by selectively distilling one or more frequencies from the signal and consequently attenuating and distorting the wave. Heterogeneous configurations involving multiple populations of resonators can be realized according to a plethora of (possibly random) spatial arrangements to achieve polychromatic and broadband effects, de facto behaving as tunable electromechanical rainbow materials. In two- dimensional lattice domains, the same paradigm can be used to actively manipulate the inherent frequency-selective spatial patterns exhibited by propagating wavefields. This approach ultimately results in the design of programmable structures and materials that can be used as tunable filters, mechanical signal jammers and directional actuators and sensors.


Stefano Gonella received Ph.D. and M.S. degrees in aerospace engineering from the Georgia Institute of Technology in 2007 and 2005, respectively. Previously, he received a Laurea, also in aerospace engineering, from Politecnico di Torino, Italy, in 2003. He joined the University of Minnesota in 2010, after 3 years of post-doctoral experience at Northwestern University. His research interests revolve around the modeling, simulation and experimental reconstruction of wave phenomena in complex structures and materials, with emphasis on smart cellular solids, phononic crystals and programmable acoustic metamaterials. He is also interested in the development of methodologies for structural and material diagnostics through the mechanistic adaptation of concepts of machine learning and computer vision.

Time and place: Friday, February 9, 2018 - 10:30 a.m.
McDonnell Douglas Engineering Auditorium (MDEA)

Active and passive control: from thought experiment to Formula One racing

Malcolm C. Smith, University of Cambridge


The talk will explain the origins and applications of a new mechanical device (the “inerter”) which rapidly became a standard component in Formula One Racing and IndyCars and is now being considered for applications from railway suspensions to buildings. The origins of the idea in a seemingly innocent question in mathematical control theory in the author's research will be described. The broader context of the idea - namely the close link between control and network synthesis, and the re-opening of research in classical circuits - will be described in a tutorial manner. The talk will conclude with an account of the manner in which the inerter entered the popular press by reason of a Formula One “spy scandal”.


Malcolm C. Smith is Professor of Control Engineering and Head of the Control Group in the Department of Engineering at the University of Cambridge. His research interests are in the areas of robust control, nonlinear systems, electrical and mechanical networks, and automotive applications. He is well-known for his invention of the inerter mechanical device currently used in Formula One motor racing and elsewhere. He received degrees in mathematics and control engineering from Cambridge University, England. He was subsequently a Research Fellow at the German Aerospace Centre, Oberpfaffenhofen, a Visiting Assistant Professor and Research Fellow with the Department of Electrical Engineering at McGill University, Montreal, and an Assistant Professor with the Department of Electrical Engineering, Ohio State University, Columbus, before returning to Cambridge in 1990 as a Lecturer in Engineering. Professor Smith is a Fellow of the IEEE and the Royal Academy of Engineering. He received the 1992 and 1999 George Axelby Best Paper Awards, in the IEEE Transactions on Automatic Control, both times for joint work with T.T. Georgiou. He received the 2009 Sir Harold Hartley Medal of the Institute of Measurement and Control for outstanding contributions to the technology of measurement and control.

Time and place: Friday, February 2, 2018 - 10:30 a.m.
McDonnell Douglas Engineering Auditorium (MDEA)

Scott Moura
Professor, Director of eCAL, University of California, Berkeley

Identification, Control and Fault Diagnostics of PDE Battery Electrochemistry Models

Abstract Batteries are ubiquitous. However, today’s batteries are expensive, range-limited, power-restricted, too quick to die, too slow to charge, and susceptible to safety issues. For this reason, model-based battery management systems (BMS) are of extreme interest. In this talk, we discuss eCAL’s recent research with electrochemical-based BMS, which are modeled by nonlinear partial differential equations (PDEs). Specifically, we discuss optimal experiment design for parameter identification, optimal safe-fast charging control, and fault diagnostics. Finally, we close with exciting new perspectives for next-generation battery systems.

Bio Scott Moura is an assistant professor in civil and environmental engineering at UC Berkeley and director of eCAL. He received his doctorate from the University of Michigan in 2011, a master's degree from the University of Michigan in 2008, and a bachelor's degree from UC Berkeley in 2006 - all in mechanical engineering. He was a postdoctoral scholar at UC San Diego in the Cymer Center for Control Systems and Dynamics and a visiting researcher in the Centre Automatique et Systèmes at MINES ParisTech in Paris, France. He is a recipient of the O. Hugo Shuck Best Paper Award, Carol D. Soc Distinguished Graduate Student Mentoring Award, Hellman Faculty Fellows Award, UC Presidential Postdoctoral Fellowship, National Science Foundation Graduate Research Fellowship, University of Michigan Distinguished ProQuest Dissertation Honorable Mention, University of Michigan Rackham Merit Fellowship and Distinguished Leadership Award. Moura has received multiple conference best paper awards, as an advisor and student. His research interests include control and estimation theory for PDEs, optimization, machine learning, batteries, electric vehicles and the smart grid.

Time and place: Friday, February 26, 2018 - 2:00-3:00pm
MAE Conference Hall, Engineering Gateway, 4th floor

Izchak Lewkowicz
Professor, Electrical Engineering
Ben Gurion University Be’er Sheva, Israel

Dissipative Systems & Convex Inverible Cones

Abstract This is an ongoing research for quite a few years. It focuses on the fact that physical dissipativity (continuous-time) and the mathematical notion of Convex Invertible Cones, are intimately related. In this talk, we try to substantiate this observation and as time permits, to point at applications.

Tuesday, November 7, 2017
Jordan Berg
NSF, CMMI Program Director
Professor and Co-Director of Nano Tech Center
Mechanical Engineering
Texas Tech University

Vibrational Control, Stability Maps, and Averaging

Time and place: Tuesday, November 7, 2017 - 10:30 a.m
CALIT2 Auditorium

Abstract Most control systems engineers would say that feedback is required to stabilize an unstable plant. However it has been long known that an appropriately designed open-loop periodic input can also modify stability. This technique, sometimes called “vibrational control,” is a unique control method that may succeed where conventional feedback is infeasible. However, vibrational control has not had widespread success in applications since it was introduced in the 1980’s. In this talk we will attempt to show that this is in part because vibrational control has largely been studied by the control community in the context of averaging theory, where the frequency of the stabilizing signal is not known in advance, but is required to be “sufficiently large.” This talk presents vibrational control in the context of the classical Hill’s equation, which allows some important limitations of standard averaging methods to be clearly observed. This talk proposes an alternative framework for vibrational control design and analysis, based on the stability map. This approach has been successful outside of the control community for design of quadrupole mass filters and quadrupole ion traps.

Bio: Jordan M. Berg received the BSE and MSE degrees in Mechanical and Aerospace Engineering from Princeton University in 1981 and 1984. He worked in the Attitude Control Analysis group at RCA Astro-Electronics in East Windsor, NJ, from 1983 to 1986. He received the PhD in Mechanical Engineering and Mechanics, and the MS in Mathematics and Computer Science from Drexel University in 1992. He held postdoctoral appointments at the Air Force Research Labs in Dayton, OH, and the Institute for Mathematics and Its Applications in Minneapolis, MN. Since 1996 he has been at Texas Tech University, where he is Professor of Mechanical Engineering and Co-Director of the Nano Tech Center. As a Fulbright Scholar in 2008 he held visiting faculty appointments at the University of Ruhuna and University of Peradeniya in Sri Lanka. He is a Professional Engineer in the State of Texas and a Fellow of the ASME. His research interests include nonlinear and geometric control, and the modeling, simulation, design, and control of nano- and microsystems. In 2014 he joined the Civil, Mechanical, and Manufacturing Innovation Division of the Engineering Directorate of the National Science Foundation under an IPA agreement. He currently serves as a Program Officer for the Dynamics, Control and Systems Diagnostics program, the National Robotics Initiative, and the C3 Soft Robotics EFRI topic.

Friday, November 3, 2017
Mario A. Rotea
Professor and Erik Jonsson Chair
Department Head of Mechanical Engineering
University of Texas, Dallas

Optimization and Control of Wind Energy Systems

Time and place: Friday, November 3, 2017 - 10:30 a.m
McDonnell Douglas Engineering Auditorium (MDEA)

Abstract Wind technology is a major player in utility-scale renewable energy for the production of electricity around the globe. Many countries share the strategic goal of increasing the penetration of wind energy into the electric grids. In the U.S. alone, the goal is to increase from 82 gigawatts GW of wind power, supplying 5-6 percent of the electricity demand in 2016, to 400 GW of wind power contributing 35 percent of the electricity by 2050. Attaining this goal would require a continued decrease in the cost of wind power. Arguably, advanced modeling and simulation, flow monitoring and advanced controls are key to reducing the cost of wind energy. This talk will provide an overview of the work being done at the University of Texas, Dallas, in these areas. It will show how the convergence of high-fidelity simulations, reduced-order models, field measurements (blending LiDAR technology with SCADA and met tower data) and advanced controls may yield increases in annual energy production and reliability of wind turbines and wind farms, which are important factors in reducing the cost of wind energy.

Bio: Mario A. Rotea is the holder of the Erik Jonsson Chair in engineering and computer science at the University of Texas, Dallas, where he is also the department head of mechanical engineering. Rotea spent 17 years at Purdue University as a professor of aeronautics and astronautics, developing and teaching methods for the analysis and design of control systems. He also worked for the United Technologies Research Center as senior research engineer on advanced control systems for helicopters, gas turbines and machine tools. Rotea was the head of the Mechanical and Industrial Engineering Department at the University of Massachusetts, Amherst, where he expanded the department in the area of wind energy and applications of industrial engineering to the health care sector. His career includes terms as director of the control systems program and division director of engineering education and centers at the National Science Foundation. Rotea is cofounder of WindSTAR, an NSF Industry-University Cooperative Research Center aimed at bringing together academia and industry to advance wind energy through industry-relevant research and education. Rotea joined UT Dallas in 2009 to serve as professor and inaugural head of the then newly created mechanical engineering department. He directed the department’s rapid growth, increasing student enrollment from 10 students to more than 1,100 in 2017. Rotea is a fellow of the IEEE for contributions to robust and optimal control of multivariable systems. Rotea graduated with a degree in electronic engineering from the University of Rosario. He received a master’s degree in electrical engineering and his doctorate in control science and dynamical systems from the University of Minnesota.

October 17, 2017
Bin Hu
University of Wisconsin, Madison

Dissipativity theory for optimization and machine learning research

Time and place: October 17, 2017, 11:30am
McDonnell Douglas Engineering Auditorium (MDEA)

Abstract Empirical risk minimization (ERM) is a central topic for machine learning research, and is typically solved using first-order optimization methods whose convergence proofs are derived in a case-by-case manner. In this talk, we will present a simple routine which unifies the analysis of such optimization methods including gradient descent method, Nesterov's accelerated method, stochastic gradient descent (SGD), stochastic average gradient (SAG), SAGA, Finito, stochastic dual coordinate ascent (SDCA), stochastic variance reduction gradient (SVRG), and SGD with momentum. Specifically, we will view all these optimization methods as dynamical systems and then use a unified dissipativity approach to derive sufficient conditions for convergence rate certifications of such dynamical systems.The derived conditions are all in the form of linear matrix inequalities (LMIs). We solve these resultant LMIs and obtain analytical proofs of new convergence rates for various optimization methods (with or without individual convexity). Our proposed analysis can be automated for a large class of first-order optimization methods under various assumptions. In addition, the derived LMIs can always be numerically solved to provide clues for constructions of analytical proofs

Bio: Bin Hu received his B.Sc in Theoretical and Applied Mechanics from the University of Science and Technology of China, and received the M.S. in Computational Mechanics from Carnegie Mellon University. He received the Ph.D in Aerospace Engineering and Mechanics at the University of Minnesota, advised by Peter Seiler. He is currently a postdoctoral researcher in the optimization group of Wisconsin Institute for Discovery at the University of Wisconsin-Madison. He is working with Laurent Lessard and closely collaborating with Stephen Wright. He is interested in building connections between control theory and machine learning research. His current research focuses on tailoring robust control theory (integral quadratic constraints, dissipation inequalities, jump system theory, etc) to unify the study of stochastic optimization methods (stochastic gradient, stochastic average gradient, SAGA, SVRG, Katyusha momentum, etc) and their applications in related machine learning problems (logistic regression, deep neural networks, matrix completion, etc).

May 19, 2017
Mihailo Jovanovic
Ming Hsieh Department of Electrical Engineering
Director, Center for Systems and Control
Viterbi School of Engineering
University of Southern California

Controller Architectures: Tradeoffs between Performance and Complexity

This talk describes the design of controller architectures that achieve a desired tradeoff between performance of distributed systems and controller complexity. Our methodology consists of two steps. First, we design controller architecture by incorporating regularization functions into the optimal control problem and, second, we optimize the controller over the identified architecture. For large-scale networks of dynamical systems, the desired structural property is captured by limited information exchange between physical and controller layers and the regularization term penalizes the number of communication links. In the first step, the controller architecture is designed using a customized proximal augmented Lagrangian algorithm. This method exploits separability of the sparsity-promoting regularization terms and transforms the augmented Lagrangian into a form that is continuously differentiable and can be efficiently minimized using a variety of methods. Although structured optimal control problems are, in general, nonconvex, we identify classes of convex problems that arise in the design of symmetric systems, undirected consensus and synchronization networks, optimal selection of sensors and actuators, and decentralized control of positive systems. Examples are provided to demonstrate the effectiveness of the framework.

Time and place: May 19, 2017, 2:00pm
McDonnell Douglas Engineering Auditorium (MDEA)

May 10, 2017
Christian Grussler
Lund University

Low-Rank Inducing Norms with Optimality Interpretations

This talk is on optimization problems which are convex apart from a sparsity/rank constraint. These problems are often found in the context of compressed sensing, linear regression, matrix completion, low-rank approximation and many more. Today, one of the most widely used methods for solving these problems is so-called nuclear norm regularization. Despite the nice probabilistic guarantees of this method, this approach often fails for problems with structural constraints.

In this talk, we will present an alternative by introducing the family of so-called low-rank inducing norms as convexifiers. Each norm is the convex envelope of a unitarily invariant norm plus a rank constraint. Therefore, they have several interesting properties, which will be discussed throughout the talk. They:

i. Give a simple deterministic test if the solution to the convexified problem is a solution to a specific non-convex problem.

ii. Often finds solutions where the nuclear norm fails to give low-rank solutions.

iii. Allow us to analyze the convergence of non-convex proximal splitting algorithms with convex analysis tools.

iv. Provide a more efficient regularization than the traditional scalar multiplication of the nuclear norm.

v. Leads to a different interpretation of the nuclear norm than the one that is traditionally presented.

In particular, all the results can be generalized to so-called atomic norms.

Time and place: May 10, 2017, 11:00am
EG 3161

January 19, 2017
Dr. Ge Chen (
Academy of Math & System Science, Chinese Academy of Sciences Beijing 100190, P.R. China

Analysis and control on collective behavior of some random complex systems

Abstract: Complex systems exist almost everywhere in nature, and human social and economic systems, and so have generated considerable interest in researchers from various fields. A central issue of complex system study is an understanding of how local interactions among the elements lead to collective behavior of the whole group. However, the analysis of complex systems is usually difficult and the existing almost unique method is to construct a Lyapunov function. We first based on the Lyapunov method, put forward some quantitative analysis and optimization methods which applied to some biological and engineering systems. Also, we broke through the Lyapunov method and proposed a new analysis method which applied to some biological and social systems.

Time and place: May 19, 2017, 2:00pm
McDonnell Douglas Engineering Auditorium (MDEA)

November 4, 2016
Professor Brad Paden
University of California, Santa Barbara, and LaunchPoint Technologies Inc.

Adventures in Mechatronics

This talk aims to illustrate the creativity, challenge, and professional enjoyment associated with the invention, design, and control of mechatronic systems. Example systems include a life-saving locomotive bumper, a magnetically-coupled MEMS sensor, an electromagnetic launch system, an energy storage system, a magnetic bearing system, a pediatric maglev artificial heart, a guided-catheter system, and a high-speed switching mechanism. While modeling, control and optimization are essential ingredients in mechatronic systems, the large design and application spaces of mechatronic systems compel us to place a high value on innovation at the level of system architectures - this point is illustrated throughout the talk.

Time and place: November 4, 2016, 10:30am
McDonnell Douglas Engineering Auditorium (MDEA)

September 13, 2016
Professor Na Li
Harvard University

Distributed Energy Management with Limited Communication

A major issue in future power grids is how intelligent devices and independent producers can respectively change their power consumption-production to achieve near maximum efficiency for the power network. Limited communications between devices and producers necessitates an approach where the elements of the network can act in an autonomous manner with limited information-communications yet achieve near optimal performance. In this talk, I will present our recent work on distributed energy management with limited communication. In particular, I will show how we can extract information from physical measurements and recover information from local computation. We will also investigate the minimum amount of communication for achieving the optimal energy management and study how limited communication affects the convergence rate of the distributed algorithms. We will conclude the talk with a discussion on challenges and opportunities on distributed optimization and control for future grids.

Time and place: Tuesday, September 13, 2016 2:00pm-3:00pm
McDonnell Douglas Engineering Auditorium (MDEA)