Optimization of Hybrid Systems with Known Paths

In this paper we study a subset of hybrid systems and present a generalized method for their optimization. We outline Hybrid Cost Automata (HCA), an extension to Hybrid Automata, where discrete and continuous cost expressions are added. The class of hybrid systems with known spatial paths is dened in the context of HCA. This type of system is common in industry where for example AGVs transport goods from one location to another, or manipulators move between joint coordinates. The optimization is performed using Dynamic Programming as a preprocessing step, whereafter Mixed Integer Nonlinear Programming is used for scheduling. A case study of a four robot cell is presented with energy consumption used as a minimization criterion

Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance

Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner. Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''. The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few. This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage. The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling

Intelligent Product Agents for Multi-Agent Control of Manufacturing Systems

The current manufacturing paradigm is shifting toward more flexible manufacturing systems that produce highly personalized products, adapt to unexpected disturbances in the system, and readily integrate new manufacturing system technology. However, to achieve this type of flexibility, new system-level control strategies must be developed, tested, and integrated to coordinate the components on the shop floor. One strategy that has been previously proposed to coordinate the resources and parts in a manufacturing system is multi-agent control. The manufacturing multi-agent control strategy consists of agents that interface with the various components on the shop floor and continuously interact with each other to drive the behavior of the manufacturing system. Two of the most important decision-making agents for this type of control strategy are product agents and resource agents. A product agent represents a single product and a resource agent represents a single resource on the plant floor. The objective of a product agent is to make decisions for an individual product and request operations from the resource agents based on manufacturer and customer specifications. A resource agent is the high-level controller for a resource on the shop floor (e.g., machines, material-handling robots, etc.). A resource agent communicates with other product and resource agents in the system, fulfills product agent requests, and interfaces with the associated resource on the plant floor. While both product agents and resource agents are important to ensure effective performance of the manufacturing system, the work presented in this dissertation improves the intelligence and capabilities of product agents by providing a standardized product agent architecture, models to capture the dynamics and constraints of the manufacturing environment, and methods to make improved decisions in a dynamic system. New methods to explore the manufacturing system and cooperate with other agents in the system are provided. The proposed architecture, models, and methods are tested in a simulated manufacturing environment and in several manufacturing testbeds with physical components. The results of these experiments showcase the improved flexibility and adaptability of this approach. In these experiments, the model-based product agent effectively makes decisions to meet its production requirements, while responding to unexpected disturbances in the system, such as machine failures or new customer orders. The model-based product agent proposed in this dissertation pushes the fields of manufacturing and system-level control closer to realizing the goals of increased personalized production and improved manufacturing system flexibility.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162893/1/ikoval_1.pd

Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing : an empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. (C) 2017 Elsevier Ltd. All rights reserved

Symbolic Verification and Strategy Synthesis for Linearly-Priced Probabilistic Timed Automata

Probabilistic timed automata are a formalism for modelling systems whose dynamics includes probabilistic, nondeterministic and timed aspects including real-time systems. A variety of techniques have been proposed for the analysis of this formalism and successfully employed to analyse, for example, wireless communication protocols and computer security systems. Augmenting the model with prices (or, equivalently, costs or rewards) provides a means to verify more complex quantitative properties, such as the expected energy usage of a device or the expected number of messages sent during a protocol’s execution. However, the analysis of these properties on probabilistic timed automata currently relies on a technique based on integer discretisation of real-valued clocks, which can be expensive in some cases. In this paper, we propose symbolic techniques for verification and optimal strategy synthesis for priced probabilistic timed automata which avoid this discretisation. We build upon recent work for the special case of expected time properties, using value iteration over a zone-based abstraction of the model

Modeling and formal verification of probabilistic reconfigurable systems

In this thesis, we propose a new approach for formal modeling and verification of adaptive probabilistic systems. Dynamic reconfigurable systems are the trend of all future technological systems, such as flight control systems, vehicle electronic systems, and manufacturing systems. In order to meet user and environmental requirements, such a dynamic reconfigurable system has to actively adjust its configuration at run-time by modifying its components and connections, while changes are detected in the internal/external execution environment. On the other hand, these changes may violate the memory usage, the required energy and the concerned real-time constraints since the behavior of the system is unpredictable. It might also make the system's functions unavailable for some time and make potential harm to human life or large financial investments. Thus, updating a system with any new configuration requires that the post reconfigurable system fully satisfies the related constraints. We introduce GR-TNCES formalism for the optimal functional and temporal specification of probabilistic reconfigurable systems under resource constraints. It enables the optimal specification of a probabilistic, energetic and memory constraints of such a system. To formally verify the correctness and the safety of such a probabilistic system specification, and the non-violation of its properties, an automatic transformation from GR-TNCES models into PRISM models is introduced. Moreover, a new approach XCTL is also proposed to formally verify reconfigurable systems. It enables the formal certification of uncompleted and reconfigurable systems. A new version of the software ZIZO is also proposed to model, simulate and verify such GR-TNCES model. To prove its relevance, the latter was applied to case studies; it was used to model and simulate the behavior of an IPV4 protocol to prevent the energy and memory resources violation. It was also used to optimize energy consumption of an automotive skid conveyor.In dieser Arbeit wird ein neuer Ansatz zur formalen Modellierung und Verifikation dynamisch rekonfigurierbarer Systeme vorgestellt. Dynamische rekonfigurierbare Systeme sind in vielen aktuellen und zukünftigen Anwendungen, wie beispielsweise Flugsteuerungssystemen, Fahrzeugelektronik und Fertigungssysteme zu finden. Diese Systeme weisen ein probabilistisches, adaptives Verhalten auf. Um die Benutzer- und Umgebungsbedingungen kontinuierlich zu erfüllen, muss ein solches System seine Konfiguration zur Laufzeit aktiv anpassen, indem es seine Komponenten, Verbindungen zwischen Komponenten und seine Daten modifiziert (adaptiv), sobald Änderungen in der internen oder externen Ausführungsumgebung erkannt werden (probabilistisch). Diese Anpassungen dürfen Beschränkungen bei der Speichernutzung, der erforderlichen Energie und bestehende Echtzeitbedingungen nicht verletzen. Eine nicht geprüfte Rekonfiguration könnte dazu führen, dass die Funktionen des Systems für einige Zeit nicht verfügbar wären und potenziell menschliches Leben gefährdet würde oder großer finanzieller Schaden entstünde. Somit erfordert das Aktualisieren eines Systems mit einer neuen Konfiguration, dass das rekonfigurierte System die zugehörigen Beschränkungen vollständig einhält. Um dies zu überprüfen, wird in dieser Arbeit der GR-TNCES-Formalismus, eine Erweiterung von Petrinetzen, für die optimale funktionale und zeitliche Spezifikation probabilistischer rekonfigurierbarer Systeme unter Ressourcenbeschränkungen vorgeschlagen. Die entstehenden Modelle sollen über probabilistische model checking verifiziert werden. Dazu eignet sich die etablierte Software PRISM. Um die Verifikation zu ermöglichen wird in dieser Arbeit ein Verfahren zur Transformation von GR-TNCES-Modellen in PRISM-Modelle beschrieben. Eine neu eingeführte Logik (XCTL) erlaubt zudem die einfache Beschreibung der zu prüfenden Eigenschaften. Die genannten Schritte wurden in einer Softwareumgebung für den automatisierten Entwurf, die Simulation und die formale Verifikation (durch eine automatische Transformation nach PRISM) umgesetzt. Eine Fallstudie zeigt die Anwendung des Verfahren

Modeling and Intelligent Control for Spatial Processes and Spatially Distributed Systems

Dynamical systems are often characterized by their time-dependent evolution, named temporal dynamics. The space-dependent evolution of dynamical systems, named spatial dynamics, is another important domain of interest for many engineering applications. By studying both the spatial and temporal evolution, novel modeling and control applications may be developed for many industrial processes. One process of special interest is additive manufacturing, where a three-dimensional object is manufactured in a layer-wise fashion via a numerically controlled process. The material is printed over a spatial domain in each layer and subsequent layers are printed on top of each other. The spatial dynamics of the printing process over the layers is named the layer-to-layer spatial dynamics. Additive manufacturing provides great flexibility in terms of material selection and design geometry for modern manufacturing applications, and has been hailed as a cornerstone technology for smart manufacturing, or Industry 4.0, applications in industry. However, due to the issues in reliability and repeatability, the applicability of additive manufacturing in industry has been limited. Layer-to-layer spatial dynamics represent the dynamics of the printed part. Through the layer-to-layer spatial dynamics, it is possible to represent the physical properties of the part such as dimensional properties of each layer in the form of a heightmap over a spatial domain. Thus, by considering the spatial dynamics, it is possible to develop models and controllers for the physical properties of a printed part. This dissertation develops control-oriented models to characterize the spatial dynamics and layer-to-layer closed-loop controllers to improve the performance of the printed parts in the layer-to-layer spatial domain. In practice, additive manufacturing resources are often utilized as a fleet to improve the throughput and yield of a manufacturing system. An additive manufacturing fleet poses additional challenges in modeling, analysis, and control at a system-level. An additive manufacturing fleet is an instance of the more general class of spatially distributed systems, where the resources in the system (e.g., additive manufacturing machines, robots) are spatially distributed within the system. The goal is to efficiently model, analyze, and control spatially distributed systems by considering the system-level interactions of the resources. This dissertation develops a centralized system-level modeling and control framework for additive manufacturing fleets. Many monitoring and control applications rely on the availability of run-time, up-to-date representations of the physical resources (e.g., the spatial state of a process, connectivity and availability of resources in a fleet). Purpose-driven digital representations of the physical resources, known as digital twins, provide up-to-date digital representations of resources in run-time for analysis and control. This dissertation develops an extensible digital twin framework for cyber-physical manufacturing systems. The proposed digital twin framework is demonstrated through experimental case studies on abnormality detection, cyber-security, and spatial monitoring for additive manufacturing processes. The results and the contributions presented in this dissertation improve the performance and reliability of additive manufacturing processes and fleets for industrial applications, which in turn enables next-generation manufacturing systems with enhanced control and analysis capabilities through intelligent controllers and digital twins.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169635/1/baltaefe_1.pd

Energy- and labor-aware production scheduling for sustainable manufacturing : a case study on plastic bottle manufacturing

Among the potential roadmaps towards sustainable production, the emerging energy-cost-aware production scheduling philosophy is considered as one promising direction. Therein, sustainability objectives, e.g., minimization of energy consumption/cost of production processes and stabilization of the electricity grid, can be achieved by manufacturing enterprises in a low-cost manner. However, these sustainability goals should be integrated with conventional production constraints besides the due date, e.g., reasonable labor cost based on work shifts, no production at weekends, and changeovers for different product types. This paper formulates a mixed-integer linear programming model for energy-and labor-cost-aware production scheduling at the unit process level, considering all the aforementioned constraints. A state-based energy model is used to reveal the energy consumption behavior of a process over time. It thus enables fine-grained energy-aware production scheduling. A case study is conducted for a blow molding process in a Belgian plastic bottle manufacturer. The measured power data enables to build an empirical energy model. The production scheduling is performed under real-time electricity pricing data. As a result, production loads are automatically shifted to the optimal periods. The optimal idle mode is automatically selected between production loads (powering off, idle, etc.). A schedule of joint energy cost and labor cost minimization is demonstrated to reduce 12% and 5% of total cost, compared to schedules that minimize energy and labor cost, respectively. In conclusion, although the labor wage is usually higher during periods with lower electricity price, energy and labor costs can be jointly optimized as a single objective to help factories minimize the production expenditure. (C) 2017 The Authors. Published by Elsevier B.V