Probabilistic Design of Systems of Cyber-Physical Systems

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Cyber-physical systems are engineered systems that rely on a tight integration of computational algorithms and physical components. Such systems are becoming increasingly important in modern society. Examples include smart home and office appliances, robotics, modern manufacturing equipment, medical devices, and automobiles. In many cases, these systems are connected to Internet and form an interconnected system, often referred to as the Internet of Things, with advanced capabilities to sense the environment, exchange information, and interact with humans. Given the rising importance of systems of cyber-physical systems in engineering, business, and social environments, improvements in their design can have major impacts on society. One challenge in designing such systems is to ensure the system is resilient, meaning that it can recover from major disruptions when a portion of the system fails to function or communicate. Another challenge is how to take social behaviors into consideration in their design so that people will feel comfortable to use such systems because they constantly collect and share our information. The goal of this research is to understand how to systematically design networked systems of cyber-physical systems such that they are resilient and trustable. This will include new techniques for measuring key properties of complex systems as well as techniques to design them with better performance. Results will be shared, in part, through open-source software created during this research to help engineers design better systems. Web-based learning modules about cyber-physical systems intended for the general public also will be created and shared broadly. The objective of this project is to create and demonstrate a probabilistic design methodology for networked and deeply interdependent cyber-physical systems that incorporates meta-modeling and discrete-event fine-grained modeling. The methodology will enable the design of system-of-system architecture that addresses functionalities of information gathering and exchange under the challenges of resiliency, adaptability, and trust. An information-based quantitative performance measure for resilience will be created. Mathematical models will be formulated to analyze the information interdependency between individuals. Quantitative measures of trustworthiness will be developed to support trust-based strategic network design and optimization. These novel approaches will be demonstrated and evaluated in a design study of highly networked manufacturing machines in supply chains.