CPS Events
Practical Control System Design via Emulated Industrial Experiments
Abstract
This talk will outline a novel approach to Control Education based on emulated industrial experiments. Several examples will be used as illustrations, including Cross Directional Control in Paper Machines, Continuous Casting Machines, Audio Compression, and Wind power Generation. The talk will be based on a forthcoming book, “Practical Control System Design: Real World Designs Implemented on Emulated Industrial Systems” by Medioli and Goodwin, John Wiley, and sons, to appear.
Speaker Bio
Graham Goodwin is an Emeritus Laureate Professor of Electrical Engineering at the University of Newcastle in Australia. His education includes B.Sc., B.E., and Ph.D. from the University of New South Wales. In 2010 he was awarded the IEEE Control Systems Field Award, in 2011 the ACA Wook Hyuan Kwon Education Award, and in 2013 he received the Rufus T. Oldenburger Medal from the American Society of Mechanical Engineers. He was twice awarded the International Federation of Automatic Control triennial Best Engineering Textbook Prize. In 2021 he was awarded the American Control Council John Ragazzini Education Award. He is a Fellow of IEEE; an Honorary Fellow of the Institute of Engineers, Australia; a Fellow of the International Federation of Automatic Control, a Fellow of the Australian Academy of Science; a Fellow of the Australian Academy of Technology, Science and Engineering; a Member of the International Statistical Institute; a Fellow of the Royal Society, London and a Foreign Member of the Royal Swedish Academy of Sciences. In 2021 he was recognized by the Australian Government by becoming an Officer in the General Division of the Order of Australia. He holds Honorary Doctorates from Lund Institute of Technology, Sweden and the Technion Israel. He is the co-author of eleven books, four edited books, and five hundred papers. He holds 16 International Patents covering rolling mill technology, telecommunications, mine planning, and mineral exploration. His current research interests include power electronics, boiler control systems, and management of type 1 diabetes.
Minkowski, Lyapunov, and Bellman: Inequalities and Equations for Stability and Optimal Control
Abstract:
The classical Lyapunov and Bellman equations, and inequalities, are cornerstone objects in linear systems theory. These equations, and inequalities, are concerned with convex quadratic functions verifying stability in cases of the Lyapunov equation and inequalities as well as optimality and stability in cases of the Bellman equation and inequalities. Rather peculiarly, prior to my work in the area, very little had been known about the related Lyapunov and Bellman equations, and inequalities, within the space of the Minkowski functions of nonempty convex compact subsets containing the origin in their interior. My recent research has provided complete characterizations of the solutions to the Lyapunov and Bellman equations, and inequalities, within the space of the Minkowski functions, referred to as the Minkowski–Lyapunov and Minkowski–Bellman equations, and inequalities, respectively. The talk reports key results underpinning the study of these fundamental equations and inequalities and their generalizations. The talk also renders strong evidence of topological flexibility and theoretical correctness of the developed frameworks and consequent advantages over the traditional Lyapunov and Bellman equations and inequalities.
Speaker Bio:
Saša V. Raković received the Ph.D. degree in Control Theory from Imperial College London. His Ph.D. research was awarded the Eryl Cadwaladr Davies Prize 2005 as the best PhD thesis in the EEE Department of Imperial College. Saša V. Raković has been affiliated with several well known international universities, including, inter alia, Imperial College London, ETH Zürich, Oxford University, UMD at College Park, UT at Austin and Texas A&M University at College Station. He is currently a full Professor at Beijing Institute of Technology, Beijing. His research interests and activity span broad areas of artificial intelligence, autonomy, controls, dynamics, systems, applied mathematics, optimization, and set-valued analysis. His research is driven by mathematical problems that belong to the intersection of artificial intelligence, controls, dynamics, systems, and optimization. Saša V. Raković has, together with William S. Levine, edited a well-received “Handbook of Model Predictive Control” published by Springer Nature. Saša V. Raković has authored 120 publications, most of which are highly cited (i.e., about 8000 citations according to Google Scholar) and are published in leading international journals and proceedings of key conferences in the fields of control theory and engineering. Saša V. Raković’s research has made contributions to theory, computation, and implementation of conventional, robust and stochastic model predictive control, and he is best known as one of the key contributors to the tube model predictive control framework.
Motion planning under constraints and uncertainty using data and reachability
Abstract:
Autonomy in robotics, transportation, and space applications requires resilient, fast, and safe motion planners. Specifically, we need fast motion planners that can safely navigate autonomous systems through cluttered environments, despite the limitations and uncertainty arising from low-cost onboard sensors and simplified mathematical models. Additionally, the generated motion plan must allow the autonomous system to abort its mission without compromising safety in the event of failure.
In this talk, I will describe some recent efforts to address these challenges using reachability and data. First, I will discuss a scalable multi-agent motion planner that combines reinforcement learning and constrained control theory to generate fast and safe motion plans under uncertainty. Second, I will present Safely, a single-agent motion planner for a robot with limited onboard sensing capabilities. Safely uses data, stochastic reachability, and sensitivity analysis to prescribe a safe motion plan under uncertainty and identifies trajectory-relevant obstacles for the sensing-constrained robot to sense at each time step. I will also present the results from hardware experiments for both of these works. If time permits, I will also discuss a stochastic reachability-based approach for abort-safe spacecraft rendezvous under actuation and navigational uncertainty.
Speaker Bio:
Abraham Vinod is a Research Scientist at Mitsubishi Electric Research Laboratories (MERL). He received his Bachelor’s and Master’s degrees in Electrical Engineering at the Indian Institute of Technology Madras (IITM) and his Ph.D. degree in Electrical Engineering from the University of New Mexico. Before joining MERL, he held a postdoctoral position at the University of Texas at Austin (UT Austin). His research broadly focuses on learning, planning, and decision-making under uncertainty for autonomous systems. His work won the Best Student Paper Award at the 2017 ACM Hybrid Systems: Computation and Control Conference, the Best Paper Award finalist at the 2018 ACM Hybrid Systems: Computation and Control Conference, and his Master's thesis won the Prof. Achim Bopp Prize (IITM).
A Tutorial on Real-Time Computing Issues for Control Systems
Abstract:
This talk presents a tutorial (scheduled for presentation at the 2023 American Control Conference) on the elements of computation in a real-time control system. Unlike conventional computation or even computation in digital signal processing systems, computation in a feedback loop must be sensitive to issues of latency and noise around the loop. This presents some fundamental requirements, limitations, and design constraints not seen in other computational applications. The logic of presenting such a tutorial is that while the computer technology changes at a rapid pace, the principles of how we match that technology to the constraints of a feedback loop remain consistent over the years. We will discuss the different computational chains in a feedback system, ways to conceptualize the effects of time delay and jitter on the system, and present a three-layer-model for programming real-time computations. The tutorial also presents some filter and state- space structures that are useful for real-time computation.
Speaker Bio:
Dr. Daniel (Danny) Abramovitch earned degrees in Electrical Engineering from Clemson (BS) and Stanford (MS and Ph.D.), doing his doctoral work under the direction of Gene Franklin. He has spent most of his career at Hewlett-Packard Labs and Agilent Labs, moving to Agilent’s Mass Spectrometry Division in 2014 to work on improved real-time computational architectures for mass spectrometers. Danny is a Fellow of the IEEE and has held leadership positions at multiple American Control Conferences, including serving as Program Chair in 2013 and General Chair in 2016. Since then, he has led outreach efforts from the controls field including a highly popular set of “practical methods” workshops. He has helped organize conference tutorial sessions on topics as varied as disk drives, atomic force microscopes, phase-locked loops, laser interferometry, computation control systems, and how business models and mechanics affect control design. He is the holder of over 25 patents and 65 reviewed technical papers. Danny has spent his years in industrial research working with mechatronic control problems (optical and hard disks, atomic force microscopes) and instrumentation systems, from Agilent’s award winning first 40bps BERT to the award winning Ultivo Tandem Quad Mass Spectrometer. A consistent theme has been the need to modernize the connectivity between test benches, instrumentation, and CAD software. The need to have personally connected the pieces “from the physics to the web page” has given him a highly utilitarian view of the foundational work that needs to be done to make physical systems truly data driven. Over the past decade he has focused much of his effort on how to teach the principles, limitations, and requirements of feedback systems to people outside the traditional controls community including high school and college STEM students, scientists and practicing engineers, as well as the general public.
Toward Safe and Reliable Autonomous Vehicle System Design
Abstract
Autonomous vehicles (AVs) are considered promising to save lives, prevent injuries, and reduce costs associated with car accidents. However, to accomplish these in practice, safety and reliability are fundamental goals of computing system and architecture design. In this talk, I will go through our recent exploration and lessons learned across the stack of AV applications development and characterization, system and architecture level design, and safety modeling and testing methodologies. We show that substantial research is yet to be done in order to achieve safe and reliable autonomous vehicle system design.
Bio
Jishen Zhao is an Associate Professor in the Computer Science and Engineering Department at University of California, San Diego. Her research is at the boundary of computer architecture and systems, particularly on memory systems, machine learning for systems, and reliability. Before joining UCSD, she was an assistant professor at UCSC and a research scientist at HP Labs. She is a recipient of NSF CAREER award, AWS AI Amazon Research Award (ARA), many best paper awards, and a member of MICRO Hall of Fame.
