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2020-05-04
Chen, Jiaojiao, Liang, Xiangyang.  2019.  L2 Control for Networked Control Systems Subject to Denial-of-Service Attacks. 2019 International Conference on Computer Network, Electronic and Automation (ICCNEA). :502–505.
This paper focuses on the issue of designing L2 state feedback controller for networked control systems subject to unknown periodic denial-of-service (DoS) jamming attacks. Primarily, a resilient event-triggering mechanism is introduced to counteract the influence of DoS jamming attacks. Secondly, a switching system model of NCSs is set up. Then, the criteria of the exponential stability analysis is obtained by the piecewise Lyapunov functional approach based on the model. Thirdly, a co-design approach of the trigger parameters and L2 controller is developed. Lastly, a practical system is used for proving the efficiency of the proposed approach.
2015-05-06
Pajic, M., Weimer, J., Bezzo, N., Tabuada, P., Sokolsky, O., Insup Lee, Pappas, G.J..  2014.  Robustness of attack-resilient state estimators. Cyber-Physical Systems (ICCPS), 2014 ACM/IEEE International Conference on. :163-174.

The interaction between information technology and phys ical world makes Cyber-Physical Systems (CPS) vulnerable to malicious attacks beyond the standard cyber attacks. This has motivated the need for attack-resilient state estimation. Yet, the existing state-estimators are based on the non-realistic assumption that the exact system model is known. Consequently, in this work we present a method for state estimation in presence of attacks, for systems with noise and modeling errors. When the the estimated states are used by a state-based feedback controller, we show that the attacker cannot destabilize the system by exploiting the difference between the model used for the state estimation and the real physical dynamics of the system. Furthermore, we describe how implementation issues such as jitter, latency and synchronization errors can be mapped into parameters of the state estimation procedure that describe modeling errors, and provide a bound on the state-estimation error caused by modeling errors. This enables mapping control performance requirements into real-time (i.e., timing related) specifications imposed on the underlying platform. Finally, we illustrate and experimentally evaluate this approach on an unmanned ground vehicle case-study.