Robust and resilient control design for cyber-physical systems with an application to power systems

The tradeoff between robustness and resilience is a pivotal design issue for modern industrial control systems. The trend of integrating information technologies into control system infrastructure has made resilience an important dimension of the critical infrastructure protection mission. It is desirable that systems support state awareness of threats and anomalies, and maintain acceptable levels of operation or service in the face of unanticipated or unprecedented incidents. In this paper, we propose a hybrid theoretical framework for robust and resilient control design in which the stochastic switching between structure states models unanticipated events and deterministic uncertainties in each structure represent the known range of disturbances. We propose a set of coupled optimality criteria for a holistic robust and resilient design for cyber-physical systems. We apply this method to a voltage regulator design problem for a synchronous machine with infinite bus and illustrate the solution methodology with numerical examples.

[1]  J. Filar,et al.  Competitive Markov Decision Processes , 1996 .

[2]  David J. Hill,et al.  Transient stability enhancement and voltage regulation of power systems , 1993 .

[3]  Kun Ji,et al.  Resilient industrial control system (RICS): Concepts, formulation, metrics, and insights , 2010, 2010 3rd International Symposium on Resilient Control Systems.

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

[5]  Alejandro D. Domínguez-García,et al.  A Generalized Fault Coverage Model for Linear Time-Invariant Systems , 2009, IEEE Transactions on Reliability.

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

[7]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[8]  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.

[9]  Peter W. Sauer,et al.  Power System Dynamics and Stability , 1997 .

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

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

[12]  Lamine Mili Bradley Taxonomy of the Characteristics of Power System Operating States , 2011 .

[13]  David I. Gertman,et al.  Resilient control systems: Next generation design research , 2009, 2009 2nd Conference on Human System Interactions.

[14]  Tamer Basar,et al.  H infintity control of large-scale jump linear systems via averaging and aggregation , 1999 .

[15]  Miles A. McQueen,et al.  Ideal Based Cyber Security Technical Metrics for Control Systems , 2007, CRITIS.

[16]  Onésimo Hernández-Lerma,et al.  Zero-Sum Stochastic Games in Borel Spaces: Average Payoff Criteria , 2000, SIAM J. Control. Optim..

[17]  Quanyan Zhu,et al.  Management of Control System Information SecurityI: Control System Patch Management , 2011 .