Distributed estimation techniques forcyber-physical systems

Nowadays, with the increasing use of wireless networks, embedded devices and agents with processing and sensing capabilities, the development of distributed estimation techniques has become vital to monitor important variables of the system that are not directly available. Numerous distributed estimation techniques have been proposed in the literature according to the model of the system, noises and disturbances. One of the main objectives of this thesis is to search all those works that deal with distributed estimation techniques applied to cyber-physical systems, system of systems and heterogeneous systems, through using systematic review methodology. Even though systematic reviews are not the common way to survey a topic in the control community, they provide a rigorous, robust and objective formula that should not be ignored. The presented systematic review incorporates and adapts the guidelines recommended in other disciplines to the field of automation and control and presents a brief description of the different phases that constitute a systematic review. Undertaking the systematic review many gaps were discovered: it deserves to be remarked that some estimators are not applied to cyber-physical systems, such as sliding mode observers or set-membership observers. Subsequently, one of these particular techniques was chosen, set-membership estimator, to develop new applications for cyber-physical systems. This introduces the other objectives of the thesis, i.e. to present two novel formulations of distributed set-membership estimators. Both estimators use a multi-hop decomposition, so the dynamics of the system is rewritten to present a cascaded implementation of the distributed set-membership observer, decoupling the influence of the non-observable modes to the observable ones. So each agent must find a different set for each sub-space, instead of a unique set for all the states. Two different approaches have been used to address the same problem, that is, to design a guaranteed distributed estimation method for linear full-coupled systems affected by bounded disturbances, to be implemented in a set of distributed agents that need to communicate and collaborate to achieve this goal

Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners (Second Edition)

Probabilistic Risk Assessment (PRA) is a comprehensive, structured, and logical analysis method aimed at identifying and assessing risks in complex technological systems for the purpose of cost-effectively improving their safety and performance. NASA's objective is to better understand and effectively manage risk, and thus more effectively ensure mission and programmatic success, and to achieve and maintain high safety standards at NASA. NASA intends to use risk assessment in its programs and projects to support optimal management decision making for the improvement of safety and program performance. In addition to using quantitative/probabilistic risk assessment to improve safety and enhance the safety decision process, NASA has incorporated quantitative risk assessment into its system safety assessment process, which until now has relied primarily on a qualitative representation of risk. Also, NASA has recently adopted the Risk-Informed Decision Making (RIDM) process [1-1] as a valuable addition to supplement existing deterministic and experience-based engineering methods and tools. Over the years, NASA has been a leader in most of the technologies it has employed in its programs. One would think that PRA should be no exception. In fact, it would be natural for NASA to be a leader in PRA because, as a technology pioneer, NASA uses risk assessment and management implicitly or explicitly on a daily basis. NASA has probabilistic safety requirements (thresholds and goals) for crew transportation system missions to the International Space Station (ISS) [1-2]. NASA intends to have probabilistic requirements for any new human spaceflight transportation system acquisition. Methods to perform risk and reliability assessment in the early 1960s originated in U.S. aerospace and missile programs. Fault tree analysis (FTA) is an example. It would have been a reasonable extrapolation to expect that NASA would also become the world leader in the application of PRA. That was, however, not to happen. Early in the Apollo program, estimates of the probability for a successful roundtrip human mission to the moon yielded disappointingly low (and suspect) values and NASA became discouraged from further performing quantitative risk analyses until some two decades later when the methods were more refined, rigorous, and repeatable. Instead, NASA decided to rely primarily on the Hazard Analysis (HA) and Failure Modes and Effects Analysis (FMEA) methods for system safety assessment

Set-based state estimation and fault diagnosis using constrained zonotopes and applications

This doctoral thesis develops new methods for set-based state estimation and active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii) discrete-time nonlinear systems whose trajectories satisfy nonlinear equality constraints (called invariants), (iii) linear descriptor systems, and (iv) joint state and parameter estimation of nonlinear descriptor systems. Set-based estimation aims to compute tight enclosures of the possible system states in each time step subject to unknown-but-bounded uncertainties. To address this issue, the present doctoral thesis proposes new methods for efficiently propagating constrained zonotopes (CZs) through nonlinear mappings. Besides, this thesis improves the standard prediction-update framework for systems with invariants using new algorithms for refining CZs based on nonlinear constraints. In addition, this thesis introduces a new approach for set-based AFD of a class of nonlinear discrete-time systems. An affine parametrization of the reachable sets is obtained for the design of an optimal input for set-based AFD. In addition, this thesis presents new methods based on CZs for set-valued state estimation and AFD of linear descriptor systems. Linear static constraints on the state variables can be directly incorporated into CZs. Moreover, this thesis proposes a new representation for unbounded sets based on zonotopes, which allows to develop methods for state estimation and AFD also of unstable linear descriptor systems, without the knowledge of an enclosure of all the trajectories of the system. This thesis also develops a new method for set-based joint state and parameter estimation of nonlinear descriptor systems using CZs in a unified framework. Lastly, this manuscript applies the proposed set-based state estimation and AFD methods using CZs to unmanned aerial vehicles, water distribution networks, and a lithium-ion cell.Comment: My PhD Thesis from Federal University of Minas Gerais, Brazil. Most of the research work has already been published in DOIs 10.1109/CDC.2018.8618678, 10.23919/ECC.2018.8550353, 10.1016/j.automatica.2019.108614, 10.1016/j.ifacol.2020.12.2484, 10.1016/j.ifacol.2021.08.308, 10.1016/j.automatica.2021.109638, 10.1109/TCST.2021.3130534, 10.1016/j.automatica.2022.11042

The 1993 Goddard Conference on Space Applications of Artificial Intelligence

This publication comprises the papers presented at the 1993 Goddard Conference on Space Applications of Artificial Intelligence held at the NASA/Goddard Space Flight Center, Greenbelt, MD on May 10-13, 1993. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Cooperative Monitoring to Diagnose Multiagent Plans

Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are inde-pendent of each other. In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other. The paper presents a novel methodology, named Cooperative Weak-Committed Moni-toring (CWCM), enabling agents to cooperate while monitoring their own actions. Coop-eration helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondetermin-istic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environ-ment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures. An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced. 1

Circular Production and Maintenance of Automotive Parts:An Internet of Things (IoT) Data Framework and Practice Review

The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research sets out an agenda and framework for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose

Causality Analysis and Fault Ascription in Component-Based Systems

This article introduces a general framework for fault ascription, which consists in identifying, within a multi-component system, the components whose faulty behavior has caused the failure of said system. Our framework uses configuration structures as a general semantical model to handle truly concurrent executions, partial and distributed observations in a uniform way. We define a set of expected properties for counterfactual analysis, and present a refined analysis that conforms to our requirements. This contrasts with current practice of evaluating definitions of counterfactual causality a posteriori on a set of toy examples. As an early study of the behavior of our analysis under abstraction we establish its monotony under refinement.Cet article introduit un cadre général pour l’attribution de fautes qui consiste à identifier, dans un système à composants, les composants dont le comportement incorrect a causé le dysfonctionnement du système. Nous définissons un ensemble de propriétés attendues de l’analyse contrefactuelle, et nous présentons une analyse raffinée qui satisfait ces besoins. Ceci contraste avec la pratique courante d’évaluer les définitions de causalité contrefactuelle a posteriori sur un ensemble d’exemples jouets. Nous établissons la monotonie de notre analyse sous différentes notions de raffinement