Bezzo, Nicola

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Now showing 1 - 6 of 6
  • Publication
    Towards Assurance Cases for Resilient Control Systems
    (2014-08-01) Weimer, James; Sokolsky, Oleg; Bezzo, Nicola; Lee, Insup
    The paper studies the problem of constructing assurance cases for embedded control systems developed using a model-based approach. Assurance cases aim to provide a convincing argument that the system delivers certain guarantees, based on the evidence obtained during the design and evaluation of the system. We suggest an argument strategy centered around properties of models used in the development and properties of tools that manipulate these models. The paper presents the case study of a resilient speed estimator for an autonomous ground vehicle and takes the reader through a detailed assurance case arguing that the estimator computes speed estimates with bounded error.
  • Publication
    Towards Synthesis of Platform-Aware Attack-Resilient Control Systems: Extended Abstract
    (2013-04-09) Pajic, Miroslav; Bezzo, Nicola; Weimer, James; Alur, Rajeev; Mangharam, Rahul; Michael, Nathan; Pappas, George J; Sokolsky, Oleg; Tabuada, Paulo; Weirich, Stephanie; Lee, Insup
  • Publication
    Architecture-Centric Software Development for Cyber-Physical Systems
    (2014-10-01) Sokolsky, Oleg; Pajic, Miroslav; Bezzo, Nicola; Lee, Insup
    We discuss the problem of high-assurance development of cyber-physical systems. Specifically, we concentrate on the interaction between the development of the control system layer and platform-specific software engineering for system components. We argue that an architecture-centric approach allows us to streamline the development and increase the level of assurance for the resulting system. The case study of an unmanned ground vehicle illustrates the approach.
  • Publication
    Attack-Resilient Minimum Mean-Squared Error Estimation
    (2014-06-01) Weimer, James; Bezzo, Nicola; Pajic, Miroslav; Sokolsky, Oleg; Lee, Insup
    This work addresses the design of resilient estimators for stochastic systems. To this end, we introduce a minimum mean-squared error resilient (MMSE-R) estimator whose conditional mean squared error from the state remains finitely bounded and is independent of additive measurement attacks. An implementation of the MMSE-R estimator is presented and is shown as the solution of a semidefinite programming problem, which can be implemented efficiently using convex optimization techniques. The MMSE-R strategy is evaluated against other competing strategies representing other estimation approaches in the presence of small and large measurement attacks. The results indicate that the MMSE-R estimator significantly outperforms (in terms of mean-squared error) other realizable resilient (and non-resilient) estimators.
  • Publication
    Resilient Parameter-Invariant Control With Application to Vehicle Cruise Control
    (2013-03-20) Weimer, James; Bezzo, Nicola; Pajic, Miroslav; Pappas, George J.; Sokolsky, Oleg; Lee, Insup
    This work addresses the general problem of resilient control of unknown stochastic linear time-invariant (LTI) systems in the presence of sensor attacks. Motivated by a vehicle cruise control application, this work considers a first order system with multiple measurements, of which a bounded subset may be corrupted. A frequency-domain-designed resilient parameter-invariant controller is introduced that simultaneously minimizes the effect of corrupted sensors, while maintaining a desired closed-loop performance, invariant to unknown model parameters. Simulated results illustrate that the resilient parameter-invariant controller is capable of stabilizing unknown state disturbances and can perform state trajectory tracking.
  • Publication
    Robustness of Attack-Resilient State Estimators
    (2014-04-01) Pajic, Miroslav; Weimer, James; Bezzo, Nicola; Tabuada, Paulo; Sokolsky, Oleg; Lee, Insup; Pappas, George
    The interaction between information technology and physical 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 difeerence 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.