Dynamic and real-time adaptive configuration of Cyber-Physical Systems (CPSs) results in increased complexity due to a variety of heterogeneous and interdependent variables and creates unique challenges. For example, 1) Emergent Behavior: How do we ensure that system constituents dynamically and adaptively collaborate to produce a consistent repeatable functionality while supporting the capability to upgrade the individual entities through technology infusion; 2) Scale: How do we ensure scalability of these systems by managing complexity; 3) Risk Management: How do we evaluate and manage the risks associated with the connection and interdependencies of heterogenous systems.Design and development of this new generation of CPSs can be viewed through the lens of System-of-Systems (SoS) methodology which is designed to analyze and assess the evolving topologies created by interactions within a large complex system operating in dynamic and uncertain environment. In this paper, we propose the use of several SoS tools and techniques for the analysis and design of nextgeneration CPSs. Our SoS methodologies address features such as diversity of component systems, complex hierarchical structures, dynamic and emergent behavior, and interactions between components. Therefore, they are suitable to treat some of the challenging features of cyber-physical systems. However, it is necessary to modify these methodologies to address specific aspects of CPSs. Constraints and metrics from SoS methodology, applied to the design space, will support decision on component systems and the topology of their connections, and provide a set of -good designs‖, with desired characteristics.
This article provides a process for system architecting that incorporates a holistic approach for architecture design space characterization by integrating decision alternatives in functional, physical, and allocational design spaces and accounting for interactions. System architects are faced with numerous decisions for system form, functions, and operations when defining a system architecture. Systems designers are tasked with selecting design options which provide the necessary functionality in support of the architecture. Since modern systems, especially system-of-systems, are composed of interacting and interwoven functions and elements, it is imperative to holistically evaluate variations in the system architecture and system design, and discover interactions among and between architecture decisions and design decisions. In this article, this design space characterization is made an integral part of the system architecting process and a set-theoretic framework is developed for managing an extensive design space. The design space characterization problem is formulated as identification of the significant decisions variables and quantification of their impact on the system objectives. A Design of Experiments framework-utilizing Analysis of Variation (ANOVA) and Range Tests-is presented to holistically characterize system architecture design space including the interactions between system form, function, operations, and design decisions. K E Y W O R D Sdesign of experiments, design space exploration, system architecting, system-of-systems architecting, trade-off analysis Systems Engineering.
System‐of‐Systems capability is inherently tied to the participation and performance of the constituent systems and the network performance which connects the systems together. It is imperative for the SoS stakeholders to quantify the SoS capability and performance to any uncertain variations in the system participation and network outages so that the system participation is incentivized and network design optimized. However, given the independent operations, management, and objectives of constituent systems, along with an increasing number of systems that collectively become a part of SoS, it becomes difficult to obtain a closed analytical function for SoS performance characterization. In this paper, we investigate and compare two machine learning techniques, Artificial Neural Network and Parametric Bayesian Estimation, to obtain a predictive model of the SoS given the uncertainty in the constituent system participation and the network conditions. We demonstrate our approach on a smart grid SoS application example and describe how the two machine learning techniques enable SoS robustness and resilience analysis by quantifying the uncertainty in the model and SoS operations. The results of smart grid example establish the value of SoS uncertainty quantification (UQ) and show how smart grid operators can utilize UQ models to maintain the desired robustness as operating conditions evolve and how the designers can incorporate low‐cost networks into the SoS while maintaining high performance and resilience.
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