CPS Events
Decentralized Control of Stochastic Dynamical Cyber-Physical Systems
Abstract:
Many important cyber-physical systems of great current interest are decentralized, consisting of many agents, and uncertainties. Designing decentralized control policies is a challenging task because it involves inducing coordination amongst the controllers without knowing all the states of individual agents. We develop new methods to design decentralized control laws for such systems that perform as well as an optimal centralized policy. Three particular systems that we illustrate these methods on are real-time communication networks, video delivery, and the smart grid. Motivated by real-time networking, we consider multihop stochastic networks serving multiple flows in which packets have hard deadlines. We address the design of packet scheduling, transmit power control, and routing policies that maximize any specified weighted average of the timely throughputs, i.e., the throughput of packets delivered within their deadlines, of the multiple flows. We determine a tractable linear program whose solution yields an optimal routing, scheduling, and power control policy, when nodes have average-power constraints. The optimal policy is fully decentralized, with decisions regarding any packet’s transmission scheduling, transmit power level, and routing, based solely on the age and location of that packet. This resolves a fundamental obstacle that arises whenever one attempts to optimally schedule networks.
Bio:
Rahul Singh is part of the Deep Learning Group at Intel. He received the B.Tech. degree in Electrical Engineering from Indian Institute of Technology, Kanpur in 2009, the M.S. degree in Electrical Engineering from the University of Notre Dame, in 2011, and the Ph.D. degree in Electrical and Computer Engineering from the Department of Electrical and Computer Engineering, Texas A&M University, College Station, in 2015. He was a Postdoctoral Researcher at the Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology, and a Data Scientist at Encored, Inc.
Mobile Actuator and Sensor Networks: from CPS to CHS
Abstract:
Robotics will continue to be a hot topic in many years to come in this “big data, cloud computing, machine learning, virtual reality age”. This talk starts by first introducing a new angle for emerging multi-robot control research opportunities - treating robots as a network of moving actuators and/or moving sensors (MAS-net) that can communicate with each other, forming a bigger closed-loop controlled physical system or process, known as CPS (cyber-physical systems). Then I will further discuss the next step towards CHS: cyber-human systems, that extends our horizon of research attacks. I will share my belief that “Cyber-Human Systems” (CHS) will be a hot topic in the next 10-20 years as human (individual, team, society/community), computer (fixed, mobile and surrounds), and environment (physical, mixed and virtual) fuse.
Bio:
YangQuan Chen earned his Ph.D. from Nanyang Technological University, Singapore, in 1998. He had been a faculty of Electrical Engineering at Utah State University from 2000-12. He joined the School of Engineering, University of California, Merced in summer 2012 teaching “Mechatronics”, “Engineering Service Learning” and “Unmanned Aerial Systems” for undergraduates; “Fractional Order Mechanics”, “Nonlinear Controls” and “Advanced Controls: Optimality and Robustness” for graduates. His research interests include mechatronics for sustainability, cognitive process control, small multi-UAV based cooperative multi-spectral “personal remote sensing”, applied fractional calculus in controls, modeling and complex signal processing; distributed measurement and control of distributed parameter systems with mobile actuator and sensor networks.
Innovating Video Content Delivery on Commodity Mobile Devices: From Multi-path to Virtual Reality
Abstract:
More and more users watch videos on their mobile devices. In Q4 2016, mobile videos have eventually surpassed desktop videos in terms of the online viewing time. In this talk, I describe two of my recent projects aiming at improving the performance and reducing the network resource usage for mobile video streaming. First, we develop MP-DASH, a system that strategically leverages multiple network interfaces such as WiFi and LTE on mobile devices to stream videos. Compared to off-the-shelf multipath solutions, MP-DASH reduces the cellular data usage by up to 99% and the radio energy consumption by up to 85% with negligible degradation of the QoE. In the second project, we innovate 360-degree immersive video streaming, an important component of the virtual reality (VR) ecosystem. Our 360-degree streaming system adaptively fetches video contents based on robust prediction of a viewer's head movement, leading to significant network bandwidth reduction and video quality improvement compared to the state-of-the-art.
Bio:
Feng Qian is an assistant professor in the Computer Science Department at University of Minnesota - Twin Cities. His research interests cover the broad areas of mobile systems, VR/AR, computer networking, and system security. He obtained his Ph.D. at the University of Michigan. He is a recipient of several awards including a Key Contributor Award at AT&T Shannon Labs (2014), an NSF CRII Award (2015), a Google Faculty Award (2016), an AT&T VURI Award (2017), an NSF CAREER Award (2018), two best paper awards at ACM CoNEXT (2016 and 2018), and several best paper nominees. The ARO (mobile Application Resource Optimizer) system, his Ph.D. thesis, has been productized by AT&T and is now widely used in industry.
Easy, Hard or Convex? The Role of Sparsity and Structure in Systems Theory
Abstract:
Arguably, one of the hardest challenges faced now by the systems community stems from the exponential explosion in the availability of data, fueled by recent advances in sensing and actuation capabilities. Simply stated, classical techniques are ill equipped to handle very large volumes of (heterogeneous) data, due to poor scaling properties, and to impose the structural constraints required to implement ubiquitous sensing and control. For example, the powerful Linear Matrix Inequality framework developed in the past 20 years and associated semidefinite program based methods have proven very successful in providing global solutions to many control and identification problems. However, in may cases these methods break down when considering problems involving just a few hundred data points. On the other hand, several in-principle non-convex problems (e.g identification of classes of switched systems) can be efficiently solved in cases involving large amounts of data. Thus the traditional convex/non-convex dichotomy may fail to capture the intrinsic difficulty of some problems.
Bio:
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 chair of the IFAC Technical Committee on Robust Control. Past recent service include Program Chair of the 2017 IEEE Conf. on Decision and Control, General Chair of the 2016 IEEE Multi Systems Conference, Chair of the IEEE Control Systems Society Technical Committee on Computational Aspects of Control Systems Design (2013-2017), Executive Director of the IEEE CSS (2007-2011) and member of the Board of Governors of the CSS (2006-2014). He is a distinguished member of the IEEE Control Systems Society and a Fellow of the IEEE for his contributions to robust control, identification and dynamic vision. A list of publications and current research projects can be found at http://robustsystems.coe.neu.edu.
CPSRC Seminar: Observability of Dynamical Systems and Optimal Sensor Placement
Abstract:
In this talk, I will introduce a quantitative measure of partial observability for dynamical systems. For problems with very high dimensions and big data sets, the concept and theory of partial observability are developed using a computational approach. The theory and numerical methods are illustrated using several examples, including optimal sensor placement for data assimilation, PMU placement for power systems, and the detection of swarm coordination strategy of unmanned vehicles.
Bio:
Professor Wei Kang received B.S. and M.S. degrees from Nankai University, China, in 1982 and 1985, respectively, and a Ph.D. degree from the University of California at Davis in 1991, all in mathematics. In 1991-1994, he held a faculty position at Washington University in St. Louis. Since 1994, he has been with the faculty of Applied Mathematics at US Naval Postgraduate School, where he is currently the department chair. He was a Director of American Institute of Mathematics (2008-2011) for business and international collaborations. He was a visiting scientist at Intel (2005) and a consultant of EPRI (2011-2012). He is a fellow of IEEE. Professor Kang’s research interests include computational mathematics and control systems, optimal control and estimation, bifurcations and normal forms, and control system applications.
