RD2C Initiative

Initiative

Resilience Through Data-Driven, Intelligently Designed Control (RD2C) Initiative

We’re advancing scientific understanding of cyber-physical systems and seeking to develop novel sensing and control approaches that will advance the resilience of our critical infrastructures.

Illustration by Cortland Johnson | Pacific Northwest National Laboratory

Americans rely on critical infrastructures (CIs) to protect the nation, maintain a strong economy, and enhance quality of life. These infrastructures—the electrical power grid, transportation systems, information networks, and more—are evolving and modernizing. They have become increasingly complex, connected, and vulnerable to adverse conditions, such as cyber and physical attacks and faults.

As a result of this complexity and uncertainty, cyber-physical systems (CPSs) that provide sensing and control for CIs need to be more deeply understood and modeled. Sensing and control approaches and algorithms need to evolve to intelligently adapt and achieve increased security and resilience.

The RD2C laboratory-directed research initiative seeks to develop resilient, adaptive, and intelligent sensing and control algorithms through a high-fidelity observational understanding and characterization of CPSs under adverse conditions.

Specifically, RD2C aims to tackle two key science challenges:

Multimodalities and complex interdependencies in CPSs give rise to emergent and ensemble behaviors under adverse conditions that are difficult to observe and characterize in real-world or demonstrated normal conditions.
Current sensing and control solutions lack contextual awareness and are not robust or adaptive enough to assure resilience under adverse conditions in increasingly complex applications for future CIs.

The initiative, stewarded by Pacific Northwest National Laboratory’s National Security Directorate, will advance the phenomenology of CPSs and will create solutions to help enable rapid, intelligent, and adaptive infrastructure control decisions at multiple layers and timescales to increase resilience.