Extending Formal Modeling for Resilient Systems Design

Abstract

Resilience is a much‐needed characteristic in systems that are expected to operate in uncertain environments for extended periods with a high likelihood of disruptive events. Resilience approaches today employ ad hoc methods and piece‐meal solutions that are difficult to verify and test, and do not scale. Furthermore, it is difficult to assess the long‐term impact of such ad hoc “resilience solutions.” This paper presents a flexible contract‐based approach that employs a combination of formal methods for verification and testing and flexible assertions and probabilistic modeling to handle uncertainty during mission execution. A flexible contract (FC) is a hybrid modeling construct that facilitates system verification and testing while offering the requisite flexibility to cope with non‐determinism. This paper illustrates the use of FCs for multi‐UAV swarm control in, partially observable, dynamic environments. However, the approach is sufficiently general for use in other domains such as self‐driving vehicle and adaptive power/energy grids.

Leveraging Digital Twin Technology in Model-Based Systems Engineering

“…Smart buildings are an excellent example of applications that stand to benefit from machine learning capabilities in the digital twin [21,22]. Machine learning uses within a digital twin include: supervised learning (e.g., using neural network) of operator/user preferences and priorities in a simulation -based, controlled experimentation testbed [14]; unsupervised learning of objects and patterns using, for example, clustering techniques in virtual and real-world environments [23,24]; and reinforcement learning of system and environment states in uncertain, partially observable operational environments [25,26].…”