Technical Program

Abstract: We address the problem of optimizing the performance of Connected and Automated Vehicles (CAVs) while satisfying hard safety constraints at all times. Optimal CAV performance involves minimizing travel times, energy, and passenger discomfort while at the same time guaranteeing that safety constraints are always satisfied. Implementing an optimal control solution incurs a high computational cost, which limits it to simple linear dynamics, simple objective functions, and ignoring noise. Control Barrier Functions (CBFs) may be used for safety-critical control at the expense of sub-optimal performance. We present a real-time control framework that combines CAV trajectories generated through optimal control with the computationally efficient CBF method providing safety guarantees. A tractable optimal solution is first obtained for a linear or linearized system, then we optimally track this solution while using CBFs to guarantee the satisfaction of all state and control constraints. This leads to an Optimal Control and CBF (OCBF) framework for on-board decentralized operation. When considering complex objective functions, nonlinear dynamics, noise, and passenger comfort requirements for which analytical optimal control solutions are unavailable, the OCBF approach is adapted to such problems. Simulations confirm that the resulting behaviors are not only provably safe but also perform significantly better than human-driven vehicles.
Bio: Christos G. Cassandras is Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University’s Center for Information and Systems Engineering (CISE). He received degrees from Yale University (B.S., 1977), Stanford University (M.S.E.E., 1978), and Harvard University (S.M., 1979; Ph.D., 1982). In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, distributed optimization in network systems, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 450 refereed papers in these areas, and six books. He has guest-edited several technical journal issues and serves on several journal Editorial Boards. In addition to his academic activities, he has worked extensively with industrial organizations on various systems integration projects and the development of decision-support software. He has most recently collaborated with The MathWorks, Inc. in the development of the discrete event and hybrid system simulator SimEvents. Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and has also served as Editor for Technical Notes and Correspondence and Associate Editor. He was the 2012 President of the IEEE Control Systems Society (CSS). He has also served as Vice President for Publications and on the Board of Governors of the CSS, as well as on several IEEE committees, and has chaired several conferences. He has been a plenary/keynote speaker at numerous international conferences, including the American Control Conference in 2001, the IEEE Conference on Decision and Control in 2002 and 2016, and the 20th IFAC World Congress in 2017, and has also been an IEEE Distinguished Lecturer. He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize and a 2014 prize for the IBM/IEEE Smarter Planet Challenge competition (for a “Smart Parking” system and for the analytical engine of the Street Bump system respectively), the 2014 Engineering Distinguished Scholar Award at Boston University, several honorary professorships, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC.

Abstract: Dynamical traffic flow networks sometimes allow for fully distributed control architectures that, despite relying on local information only, have probable performance guarantees in terms of certain global objectives. Such architectures are particularly appealing because of their inherent scalability (linear complexity in the network size) and resilience (capacity to autonomously adapt to disruptions in the system). In this talk, I will revise some of these results focusing in particular on two settings. On the one hand, I will present recent work on throughput optimal distributed scheduling in vertical queuing networks with applications to traffic signal control in urban networks. On the other hand, I will discuss how dynamic feedback tolls can be designed in order to guarantee system optimality (e.g., minimal average travel time) in a multi-scale model of transportation networks whereby the traffic flow dynamics are intertwined with those of the routing choices.
Joint work with: G. Nilsson and R. Maggistro
Bio: Giacomo Como is an Associate Professor at the Department of Mathematical Sciences, Politecnico di Torino, Italy, and at the Automatic Control Department of Lund University, Sweden. He received the B.Sc., M.S., and Ph.D. degrees in Applied Mathematics from Politecnico di Torino, in 2002, 2004, and 2008, respectively. He was a Visiting Assistant in Research at Yale University in 2006-2007 and a Postdoctoral Associate at the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, from 2008 to 2011. He currently serves as Associate Editor of the IEEE Transactions on Network Science and Engineering and of the IEEE Transactions on Control of Network Systems, and as chair of the IEEE-CSS Technical Committee on Networks and Communication Systems. He was the IPC chair of the IFAC Workshop NecSys 2015 and a semi-plenary speaker at the International Symposium MTNS 2016 and the SICE ISCS 2017. He is a recipient of the 2015 George S. Axelby Outstanding Paper Award. His research interests are in dynamics, information, and control in network systems with applications to cyber-physical systems, infrastructure networks, and social and economic networks.

Abstract: Motivated by fleet management in autonomous mobility, we consider the classical problem of minimizing total congestion in integral multicommodity routing, for which we provide the first polynomial time algorithm with optimal approximation. Perhaps surprisingly, we additionally show that efficiently computed taxation mechanisms also yield the same optimal approximation achieved by the best polynomial time algorithm, even if the latter has dictatorial control over the agents’ actions. It follows that no other tractable approach geared at incentivizing desirable system behavior can improve upon this result, regardless of whether it is based on taxations, coordination mechanisms, information provision, or any other principle. In short: judiciously chosen taxes achieve optimal approximation. Three technical contributions underpin this conclusion. First, we show that minimizing the total congestion is NP-hard to approximate within a given expression depending solely on the class of admissible resource costs. Second, we design a tractable taxation mechanism whose efficiency (price of anarchy) matches the hardness factor. As these results extend to coarse correlated equilibria, any no-regret algorithm inherits these same performances, allowing us to devise polynomial time algorithms with optimal approximation.

Joint work with: Martin Gairing (University of Liverpool)
Bio: Dario Paccagnan is an Assistant Professor (Lecturer) at the Department of Computing, Imperial College London since the Fall 2020. Before that, he was a postdoctoral fellow with the Cen- ter for Control, Dynamical Systems and Computation, University of California, Santa Barbara. He obtained his PhD from the Automatic Control Laboratory, ETH Zurich, Switzerland, in 2018. He received a B.Sc. and M.Sc. in Aerospace Engineering from the University of Padova, Italy, in 2011 and 2014, and a M.Sc. in Mathematical Modelling and Computation from the Technical University of Denmark in 2014; all with Honors. Dario’s interests are at the interface between game theory and control theory, with a focus on the design of behavior-influencing mechanisms for socio-technical systems. Dario was a finalist for the 2019 EECI best PhD thesis award and was recognized with the SNSF Early Postdoc Mobility Fellowship, the SNSF Doc Mobility Fellowship, and the ETH medal for his doctoral work.

Abstract: We consider a two-road dynamic routing game where the state of one of the roads (the ”risky road”) is stochastic and may change over time. This generates room for experimentation. A central planner may wish to induce some of the (finite number of atomic) agents to use the risky road even when the expected cost of travel there is high in order to obtain accurate information about the state of the road. Since agents are strategic, we show that in order to generate incentives for experimentation the central planner however needs to limit the number of agents using the risky road when the expected cost of travel on the risky road is low. In particular, because of congestion, too much use of the risky road when the state is favorable would make experimentation no longer incentive compatible. We characterize the optimal incentive compatible recommendation system, first in a two-stage game and then in an infinite-horizon setting. In both cases, this system induces only partial, rather than full, information sharing among the agents (otherwise there would be too much exploitation of the risky road when costs there are low).
Joint work with: Emily Meigs, Asuman Ozdaglar, Daron Acemoglu (MIT)
Bio: Francesca Parise is an Assistant Professor of Electrical and Computer Engineering at Cornell University. Previously to that, she was a postdoctoral researcher at the Laboratory for Information and Decision Systems at MIT, she defended her PhD at the Automatic Control Laboratory, ETH Zurich, Switzerland in 2016 and she received the B.Sc. and M.Sc. degrees in Information and Automation Engineering in 2010 and 2012, respectively, from the University of Padova, Italy, where she simultaneously attended the Galilean School of Excellence. Francesca’s main research interest is in control, network and game theory. She has worked on a broad set of topics, including distributed multi-agent systems, social and economic network analysis, contagion models, aggregative games and opinion dynamics. Francesca was recognized as an EECS rising star in 2017 and is the recipient of the Guglielmo Marin Award from the “Istituto Veneto di Scienze, Lettere ed Arti”, the SNSF Early Postdoc Fellowship, the SNSF Advanced Postdoc Fellowship and the ETH Medal for her doctoral work.

Abstract: In this talk I will discuss my work on autonomous vehicles, with an emphasis on accounting for interactions with external counterparts at both the vehicle- and system-levels. Specifically, I will first discuss a decision-making framework that enables an autonomous vehicle to proactively interact with human agents (e.g., pedestrians and human drivers) to infer their intents and to use such information to produce safe and efficient driving behaviors. I will then turn the discussion to the planning and operational aspects of using autonomous vehicles in future mobility systems, specifically in the context of autonomous mobility-on-demand (AMoD) systems. The emphasis will be on how to characterize and harness the interaction between AMoD and other infrastructures, such as the electric power and public transit networks.
Bio: Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. He is currently on a partial leave of absence at NVIDIA serving as Director of Autonomous Vehicle Research. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America’s 20 most highly promising investigators under the age of 40. His work has been recognized with best paper nominations or awards at the European Control Conference, at the IEEE International Conference on Intelligent Transportation Systems, at the Field and Service Robotics Conference, at the Robotics: Science and Systems Conference, at the ROBOCOMM Conference, and at NASA symposia. He is currently serving as an Associate Editor for the IEEE Control Systems Magazine.

Abstract: This paper proposes a general framework for constructing feedback controllers that drive complex dynamical systems to “efficient” steady-state (or slowly varying) operating points. Efficiency is encoded using generalized equations which can model a broad spectrum of useful objectives, such as optimality or equilibria (e.g. Nash, Wardrop, etc.) in noncooperative games. The core idea of the proposed approach is to directly implement iterative solution (or equilibrium seeking) algorithms in closed loop with physical systems. Sufficient conditions for closed-loop stability and robustness are derived; these also serve as the first closed-loop stability results for sampled- data feedback-based optimization. Numerical simulations of smart building automation and game-theoretic robotic swarm coordination support the theoretical results.
Bio: Florian Dorfler is an Associate Professor at the Automatic Control Laboratory at ETHZurich and the Associate Head of the Department of Information Technology and Electrical Engineering. He received his Ph.D. degree in Mechanical Engineering from the University of California at Santa Barbara in 2013, and a Diploma degree in Engineering Cybernetics from the University ofStuttgart in 2008. From 2013 to 2014 he was an Assistant Professor at the University of CaliforniaLos Angeles. His primary research interests are centered around control, optimization, and system theory with applications in network systems, in particular electric power grids. He is a recipient of the distinguished young research awards by IFAC (Manfred Thoma Medal 2020) and EUCA (EuropeanControl Award 2020). His students were winners or finalists for Best Student Paper awards at theEuropean Control Conference (2013, 2019), the American Control Conference (2016), the Conference on Decision and Control (2020), the PES General Meeting (2020), and the PES PowerTech Conference(2017). He is furthermore a recipient of the 2010 ACC Student Best Paper Award, the 2011 O. Hugo Schuck Best Paper Award, the 2012-2014 Automatica Best Paper Award, the 2016 IEEE Circuits andSystems Guillemin-Cauer Best Paper Award, and the 2015 UCSB ME Best PhD award.

Abstract: The power grid is undergoing a major transformation driven by the increased penetration of renewable energy resources like wind and solar. The pervasive uncertainty in the supply of power from these intermittent forms of generation creates tremendous challenges for the reliable management of the grid. In this talk, I will highlight several important operational and market design challenges where uncertainty in renewable generation has rendered traditional approaches for control and optimization obsolete, and discuss a variety of open problems where new algorithmic tools for learning and optimization are needed.
Bio: Eilyan Bitar is an Associate Professor in the School of Electrical and Computer Engineering at Cornell University. His current research is focused on the design and analysis of robust and stochastic optimization algorithms, focusing primarily on problems that entail sequential decision-making in uncertain and adversarial environments. Over the course of his career, he has explored a variety of application domains spanning electricity markets, power systems, and electrified transportation systems. He received his BS (2006) and PhD (2011) from UC Berkeley. Prior to joining Cornell, he spent one year as a Postdoctoral Fellow at the California Institute of Technology and UC Berkeley.He is a recipient of the NSF Faculty Early Career Development.

Abstract: Residential loads have great potential to enhance the efficiency and reliability of electricity systems via demand response (DR) programs. One major challenge in residential DR is to handle the unknown and uncertain customer behaviors, which is further influenced by time-varying environmental factors. In this talk, we study automated control method for regulating air conditioner (AC) loads in residential demand response (DR) by model it as multi-period stochastic optimization and learning problem. Machine learning techniques including Gaussian process and logistic regression are employed to learn the unknown thermal dynamics model and customer opt-out behavior model, respectively. Based on the Thompson sampling framework, we propose an online DR control algorithm to learn customer behaviors and make real-time AC control schemes. This algorithm considers the influence of various environmental factors on customer behaviors and is implemented in a distributed fashion to preserve the privacy of customers. Numerical simulations demonstrate the control optimality and learning efficiency of the proposed algorithm.

Joint work with Xin Chen, Yingying Li, Yutong Nie, Jun Shimada
Bio: Na Li is a Gordon McKay professor in Electrical Engineering and Applied Mathematics atHarvard University. She received her Bachelor degree in Mathematics from Zhejiang University in 2007and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013.She was a postdoctoral associate at Massachusetts Institute of Technology 2013-2014. Her research lies in control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to real-world cyber-physical societal system. She received NSF career award(2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award(2019), Donald P.Eckman Award (2019), McDonald Mentoring Award (2020), along with some other awards.

Abstract: Future sustainable transportation systems will be automated, connected, and electrified. This transition requires completely new paradigms for smart infrastructure built upon data, control, and optimization. In this talk, we highlight two projects on this theme: The ARPA-E NEXTCAR project and the SlrpEV project. The NEXTCAR project seeks to reduce vehicle energy consumption by 20% by leveraging connectivity (i.e. data) and automation (i.e. control). We specifically high- light theory and on-road experiments of eco-driving through signalized intersections that leverages infrastructure-to-vehicle communication. SlrpEV (Smart LeaRning Research Pilot for Electric Vehicles) seeks to resolve critical obstacles for public & workplace EV charging stations via novel pricing and power scheduling that leans and adapts to user preferences to minimize costs, emissions, and increase accessibility. We close with broad perspectives on building a smart transportation and energy infrastructure that advances both sustainability and equity.
Bio: Scott Moura is the Clare and Hsieh Wen Shen Endowed Distingiushed Professor in Civil & Environmental Engineering and Director of the Energy, Controls, & Applications Lab (eCAL) at theUniversity of California, Berkeley. He is also a faculty member at the Tsinghua-Berkeley ShenzhenInstitute. He received the B.S. degree from the University of California, Berkeley, CA, USA, and the M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2006, 2008, and 2011, respectively, all in mechanical engineering. From 2011 to 2013, he was a Post-Doctoral Fellow at the Cymer Center for Control Systems and Dynamics, University of California, San Diego. In 2013, he was a Visiting Researcher at the Centre Automatique et Systemes, MINES ParisTech, Paris, France. His research interests include control, optimization, and machine learning for batteries, electrified vehicles, and distributed energy resources. Dr. Moura is a recipient of the National Science Foundation (NSF)CAREER Award, Carol D. Soc Distinguished Graduate Student Mentor Award, the Hellman Fellow-ship, the O. Hugo Shuck Best Paper Award, the ACC Best Student Paper Award (as advisor), theACC and ASME Dynamic Systems and Control Conference Best Student Paper Finalist (as student and advisor), the UC Presidential Postdoctoral Fellowship, the NSF Graduate Research Fellowship, the University of Michigan Distinguished ProQuest Dissertation Honorable Mention, the University ofMichigan Rackham Merit Fellowship, and the College of Engineering Distinguished Leadership Award.

Abstract: The operation of energy systems heavily relies on data, where most agents would benefit from also accommodating data (or more generally information) for other agents. There does not exist, however, a general framework that would allow incentivizing information sharing, with the general objective of improving energy system operation in a liberalized market environment. So far, data has largely been taken for granted as a free and highly accessible commodity in energy systems operations, which is in glaring contrast to the growing concern over privacy both on small individual energy user levels, and on large corporate or even national levels. We hence propose to explore designs for data marketplaces that would be relevant for energy systems. As a special case, emphasis is placed on data markets linked to specific analytics tasks e.g. regression as a support to forecasting (may be least- squares or quantile regression for instance). Our proposal specifically focuses on yielding the right market properties, e.g., to incentivize data sellers to provide high-quality data while being given the freedom to set their individual return threshold based on privacy. Meanwhile, the data buyer balances the trade-off between the payment to the data sellers and their own gain from the additional data. Those proposals are made within both batch and online learning setups, to generally accommodate different types of analytics tasks within energy system operations.
Bio: Pierre Pinson is a Professor of Operations Research at the Technical University of Den-mark (DTU, Dept. of Technology, Management and Economics). He is an IEEE Fellow and anISI/Clarivate highly-cited researcher (2019 & 2020). He is the Editor-in-Chief of the International Journal of Forecasting. His main focus areas cover forecasting, optimization and game theory, with power and energy systems being a relevant application area. He has published extensively in some of the leading journals in Statistics, Operations Research, Meteorology and Energy Engineering. He has been a visiting researcher at the University of Oxford (Mathematical Institute), the University ofWashington in Seattle (Dpt. of Statistics), the European Center for Medium-range Weather Forecasts (ECMWF, UK), a visiting professor at Ecole Normale Superieure (Rennes, France) and a Simons fellow at the Isaac Newton Institute (Cambridge, UK).

Abstract: Making use of modern black-box ML/AI tools is potentially transformational for online optimization and control. However, such machine-learned algorithms typically do not have formal guarantees on their worst-case performance, stability, or safety. So, while their performance may improve upon traditional approaches in "typical" cases, they may perform arbitrarily worse in scenarios where the training examples are not representative due to, e.g., distribution shift or unrepresentative training data. This represents a significant drawback when considering the use of AI tools for energy systems and autonomous cities, which are safety-critical. A challenging open question is thus: Is it possible to provide guarantees that allow black-box AI tools to be used in safety-critical applications. In this talk, I will introduce recent work that aims to develop algorithms that make use of black-box AI tools to provide good performance in the typical case while integrating the "untrusted advice" from these algorithms into traditional algorithms to ensure formal worst-case guarantees. Specifically, we will discuss the use of black-box untrusted advice in the context of online convex body chasing, online non-convex optimization, and linear quadratic control, identifying both novel algorithms and fundamental limits in each case.
Bio: Adam Wierman is a Professor in the Department of Computing and Mathematical Sciences(CMS) at the California Institute of Technology. He received his Ph.D., M.Sc. and B.Sc. in ComputerScience from Carnegie Mellon University in 2007, 2004, and 2001, respectively, and has been a faculty at Caltech since 2007. Adam’s research strives to make the networked systems that govern our world sustainable and resilient. He develops new mathematical tools in machine learning, optimization, control, and economics and applies these tools to design new algorithms and markets that can be deployed in data centers, the electricity grid, transportation systems, and beyond. He is best known for his work spearheading the design of algorithms for sustainable data centers and is a recipient of multiple awards, including the ACM SIGMETRICS Rising Star award, the IEEE Communications SocietyWilliam R. Bennett Prize, multiple teaching awards, and is a co-author of papers that have received“best paper” awards at a wide variety of conferences across computer science, power engineering, and operations research.