CPS Events
Hierarchical Contract Nets and Automatic Assurance Case Environment
Abstract:
An automatic synthesis problem is often characterized by an overall goal or specification to be satisfied, the set of all possible outcomes, called the design space, and an algorithm for the automatic selection of one or more members from the design space that are provably guaranteed to satisfy the overall specification. A key challenge in automatic synthesis is the complexity of the design space.
In the first half of the talk, we introduce a formal model, termed hierarchical contract nets (HCN), and a framework for the efficient automatic synthesis of hierarchical contract nets, based on a library of conditional refinement relations between contracts and contract nets. Assurance cases (ACs) have gained attention in the aerospace, medical, and other heavily-regulated industries as a means for providing structured arguments on why a product, typically a complex cyber-physical system, is dependable (i.e., safe, secure, etc.) for its intended application. Challenges in AC construction stem from the complexity, uniqueness and the heterogeneous nature of the CPS and the supporting evidence, and the need to assess the quality of the argument and evidence.
In the second half of the talk, we present an application of HCN in the DARPA program Automatic Rapid Certification of Software (ARCOS) for an automated AC creation framework that facilitates the synthesis, validation, and confidence assessment of ACs based on dependability argument patterns and confidence patterns capturing domain knowledge.
Speaker Bio:
Dr. Timothy E. Wang is currently at RTX Technologies Research Center (formerly Raytheon/United Technologies Research Center). He earned his B.S., M.S., and PhD all from the Department of Aerospace Engineering at Georgia Institute of Technology. He has been working on various aspects of the modeling, analysis, verification, and validation (V&V) and certification of complex cyber-physical systems. This includes application of formal methods to industrial systems such as Pratt & Whitney engine FADEC, compositional modeling and formal verification of human-machine systems, formal verification of on-board helicopter autonomy, and also machine learning with formal robustness guarantees. He has participated and led several government-sponsored research programs from DARPA, ONR and NASA.
Autonomy for Space Exploration
Abstract: Over the past two decades, several autonomous functions and system-level capabilities have successfully been demonstrated and used in deep-space operations. In spite of that, spacecraft today remain largely reliant on ground in the loop to assess situations and plan next actions, using pre-scripted command sequences. Advances have been made across mission phases including spacecraft navigation; proximity operations; entry, descent, and landing; surface mobility and manipulation; and data handling. But past practices may not be sustainable for future exploration. The ability of ground operators to predict the outcome of their plans seriously diminishes when platforms physically interact with planetary bodies, as has been experienced in two decades of Mars surface operations. This results from uncertainties that arise due to limited knowledge, complex physical interaction with the environment, and limitations of associated models.
In this talk, Dr. Nesnas will share advances in the architecture, development, and deployment of autonomous systems for space applications, highlighting recent advances in entry descent and landing, rover navigation, and extreme terrain mobility. He will also describe progress toward future architecting of autonomous system and summarize anticipated needs based on recommendations from the Planetary Science and Astrobiology Decadal Survey.
Speaker Bio: Issa Nesnas is a principal technologist in the Autonomous Systems Division at the Jet Propulsion Laboratory, where he worked for over 25 years after several years in the robotics industry. He is currently an associate director of Caltech’s CAST (Center for Autonomous Systems and Technologies and JPL’s lead on NASA’s Capability Leadership Team for Autonomous Systems. At JPL, he led the Robotics Mobility and the Robotics Software Systems Groups across a span of thirteen years. His research included architectures for autonomous systems, perception-based navigation and manipulation, and extreme-terrain and microgravity mobility. He has served in multiple roles on three JPL rover missions. He is the recipient of the Magellan Award, JPL’s highest award for an individual scientific or technical accomplishment for his work on extreme terrain mobility.
Issa received a B.E. degree in Electrical Engineering from Manhattan College in 1991, and earned the M.S. and Ph.D. degrees in robotics from the Mechanical Engineering Department at the University of Notre Dame in 1993 and 1995 respectively.
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).
