Game-Theoretic Learning and Control for Resilience of Complex Adaptive Systems

Many dynamical systems nowadays fall into the category of the so-called cyber-physical systems (CPS). That is, they are systems of high complexity and heterogeneity, consisting of various digital and analog components that communicate with one another through multiple communication channels. For example, something as small as our phone is a CPS, but also something as large as a power grid. Yet, the ability of CPS to incorporate complex structures is also their Achilles heel: it leads to many ports of entry through which they can be potentially infiltrated, rendering them vulnerable to malicious adversaries who seek to create damage. It also leads to a lot of uncertainty, which traditional model-based control methods are unable to handle effectively. Drawing motivation from this reality of things, in this dissertation, we focus on two objectives: creating game- and optimization-based decision-making tools to render cyber-physical systems secure against adversaries; and developing learning-based as well as approximation-free control methods to increase their resilience under environmental and model uncertainty.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Aerospace Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Fotiadis, Filippos

Advisor dc:contributor.advisor

• Vamvoudakis, Kyriakos G.

Committee members dc:contributor.committeemember

• Haddad, Wassim
• Tsiotras, Panagiotis
• Wardi, Yorai
• Jiang, Zhong-Ping

• Cyber-physical Systems
• Learning
• Game Theory

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1853/75682

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/75682