Resilient Distributed Control of Multi-agent Cyber-Physical Systems

Multi-agent cyber-physical systems (CPSs) are ubiquitous in modern infrastructure systems, including the future smart grid, transportation networks, and public health systems. Security of these systems are critical for normal operation of our society. In this paper, we focus on physical layer resilient control of these systems subject to cyber attacks and malicious behaviors of physical agents. We establish a cross-layer system model for the investigation of cross-layer coupling and performance interdependencies for CPSs. In addition, we study a two system synchronization problem in which one is a malicious agent who intends to mislead the entire system behavior through physical layer interactions. Feedback Nash equilibrium is used as the solution concept for the distributed control in the multi-agent system environment. We corroborate our results with numerical examples, which show the performance interdependencies between two CPSs through cyber and physical interactions.

[1]  T. Başar.  Minimax control of switching systems under sampling , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[2]  Quanyan Zhu,et al.  A hierarchical security architecture for smart grid , 2010 .

[3]  Zhu Han,et al.  Smart Grid Communications and Networking: Smart grid and wide-area networks , 2012 .

[4]  Jeannette M. Wing,et al.  Game strategies in network security , 2005, International Journal of Information Security.

[5]  Quanyan Zhu,et al.  Dynamic policy-based IDS configuration , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[6]  H. Chizeck,et al.  Controllability, stabilizability, and continuous-time Markovian jump linear quadratic control , 1990 .

[7]  Quanyan Zhu,et al.  Game-theoretic analysis of node capture and cloning attack with multiple attackers in wireless sensor networks , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[8]  Tamer Başar,et al.  H1-Optimal Control and Related Minimax Design Problems , 1995 .

[9]  T. Basar,et al.  H∞-0ptimal Control and Related Minimax Design Problems: A Dynamic Game Approach , 1996, IEEE Trans. Autom. Control..

[10]  H. Kunreuther,et al.  Interdependent Security , 2003 .

[11]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[12]  Quanyan Zhu,et al.  Network Security Configurations: A Nonzero-Sum Stochastic Game Approach , 2010, Proceedings of the 2010 American Control Conference.

[13]  T. Başar,et al.  Dynamic Noncooperative Game Theory, 2nd Edition , 1998 .

[14]  T. Başar,et al.  H∞-Control of Markovian Jump Systems and Solutions to Associated Piecewise-Deterministic Differential Games , 1995 .

[15]  G. Olsder.  New trends in dynamic games and applications , 1995 .

[16]  Quanyan Zhu,et al.  Game theory meets network security and privacy , 2013, CSUR.

[17]  Tamer Basar,et al.  Feedback Equilibria in Differential Games with Structural and Modal Uncertainties , 1982 .

[18]  Quanyan Zhu,et al.  Robust and resilient control design for cyber-physical systems with an application to power systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[19]  Tomas E. Dy Liacco,et al.  The Adaptive Reliability Control System , 1967 .

[20]  Tansu Alpcan,et al.  Network Security , 2010 .

[21]  Quanyan Zhu,et al.  A dynamic game-theoretic approach to resilient control system design for cascading failures , 2012, HiCoNS '12.