Modular supervisory controller for complex systems

Automation for the oil and gas industry is driven by the need to improve efficiency, productivity, consistency, and personnel safety, while reducing cost. Fully automated systems alleviate the physical toll on human operators and allow them to focus on monitoring unsafe well events and machinery maintenance. Complex systems like drilling rigs and snubbing units require supervisory controllers that can safely coordinate equipment and processes, overcome interoperability challenges and allow for functional scalability without sacrificing safety, security, and consistency of operations. The primary objective of this report is to explore the feasibility of developing a modular supervisory controller architecture which addresses these concerns by modifying and extending existing architectures. Such modifications include the use of non-homogeneous models in sub-system modules, including discrete event models for control and physics-based models for collision avoidance, addition of a system compilation module (Meta Module) to identify simple design errors, and implementation of an algorithm for synthesis of modules and filters to replace missing sub-systems. This report discusses the implementation results of the modular supervisory control architecture (modMFSM) on a simplified two-machine drilling system for assessment of design practices. Simulations for three test cases were executed to assess the ability of the controller to correctly perform error-free operations, detect and react to possible collisions, and adapt to missing equipment. The report then discusses the possibilities of extending the modMFSM architecture to control large complex systems such as drilling rigs, using snubbing operations as an example.Mechanical Engineerin

DoMAIns: Domain-based Modeling for Ambient Intelligence

Ambient Intelligence and Smart Home Automation systems are currently emerging as feasible and ready to exploit solutions to support more intelligent features inside future and current homes. Thanks to increased availability of off-the-shelf components and to relatively easy to implement solutions we are experiencing a steady evolution of households, causing an ever-increasing users’ awareness of the capabilities of such innovative environments. To foster effective adoption of Smart Home Automation technologies in our home environments, traditional architectural and plant design must be complemented by sound design methodologies and tools, supporting the whole environment design cycle, including for example modeling, simulation and emulation, as well as, when feasible, formal model-checking and verification. Several research efforts have already addressed the design of expressive modeling tools, mostly based on Semantic Web technologies, as well as of suitable platforms for adding interoperation and rule-based intelligence to home environments. This paper proposes a new modeling methodology designed to fit the different phases of Intelligent Environments design, with a particular focus on validation and verification of the whole system. Carefully designed separation of modeled entities permits to exploit the DoMAIns framework during all phases of the environment design, from early abstract conception to the final in-field deployment. The DoMAIns design methodology is applied to a sample use case that involves comprehensive modeling and simulation of a Bank Security Booth, including the environment, the control algorithms, the automation devices and the user. Results show that the approach is feasible and that can easily handle different types of environment modeling, required in the different design phases, and for each of them it may support simulation, emulation, or other verification techniques

DESIGN OF CRUCIAL ELEMENTS FOR INDUSTRIAL PLANTS, OFFSHORE PLATFORMS AND UNDERWATER FACILITIES

The paper proposes some specific models to be used in design of crucial elements for Industrial Plants that previously were not easy to be addressed by simulation due their functional complexity. As examples of these kinds of elements are proposed autonomous systems for fire fighting and/or emergencies for on-shore and off shore plants as well as equipment for underwater operations. The paper proposes use of MS2G Simulation Paradigm (Modeling, interoperable Simulation and Serious Games) as solution in these specific cases to test concepts and capabilities

Correctness of services and their composition

We study correctness of services and their composition and investigate how the design of correct service compositions can be systematically supported. We thereby focus on the communication protocol of the service and approach these questions using formal methods and make contributions to three scenarios of SOC.Wir studieren die Korrektheit von Services und Servicekompositionen und untersuchen, wie der Entwurf von korrekten Servicekompositionen systematisch unterstützt werden kann. Wir legen dabei den Fokus auf das Kommunikationsprotokoll der Services. Mithilfe von formalen Methoden tragen wir zu drei Szenarien von SOC bei

Correctness of services and their composition

We study correctness of services and their composition and investigate how the design of correct service compositions can be systematically supported. We thereby focus on the communication protocol of the service and approach these questions using formal methods and make contributions to three scenarios of SOC.Wir studieren die Korrektheit von Services und Servicekompositionen und untersuchen, wie der Entwurf von korrekten Servicekompositionen systematisch unterstützt werden kann. Wir legen dabei den Fokus auf das Kommunikationsprotokoll der Services. Mithilfe von formalen Methoden tragen wir zu drei Szenarien von SOC bei

Design Time Methodology for the Formal Modeling and Verification of Smart Environments

Smart Environments (SmE) are intelligent and complex due to smart connectivity and interaction of heterogeneous devices achieved by complicated and sophisticated computing algorithms. Based on their domotic and industrial applications, SmE system may be critical in terms of correctness, reliability, safety, security and other such vital factors. To achieve error-free and requirement-compliant implementation of these systems, it is advisable to enforce a design process that may guarantee these factors by adopting formal models and formal verification techniques at design time. The e-Lite research group at Politecnico di Torino is developing solutions for SmE based on integration of commercially available home automation technologies with an intelligent ecosystem based on a central OSGi-based gateway, and distributed collaboration of intelligent applications, with the help of semantic web technologies and applications. The main goal of my research is to study new methodologies which are used for the modeling and verification of SmE. This goal includes the development of a formal methodology which ensures the reliable implementation of the requirements on SmE, by modeling and verifying each component (users, devices, control algorithms and environment/context) and the interaction among them, especially at various stages in design time, so that all the complexities and ambiguities can be reduced

A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd