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This paper investigates the critical-time criteria as a security metric for controlled systems subject to sharp input anomalies (attack, fault), characterized by having high impact in a reduced amount of time (e.g. denial-of-service, attack by upper saturation). The critical-time is the maximal time-horizon for which a system can be considered to be safe after the occurrence of an anomaly. This metric is expected to be useful for risk analysis and treatment (prevention, detection, mitigation). In this work, the computational problem of the critical-time for uncertain linear systems and several classes of sharp input anomalies, depending on the input channel and the set of abnormal signal values, is formulated based on the quadratic constraints (QC) framework, representing sets by the intersection of QC inequalities and equalities. An iterative LMI-based algorithm is then proposed to provide an under-estimate of the critical-time. Finally, the potential of the critical-time as a metric for defense design is illustrated and discussed on the quadruple-tank case study through different relevant scenarios.

Verification and safety assessment of neural network controlled systems (NNCSs) is an emerging challenge. To provide guarantees, verification tools must efficiently capture the interplay between the neural network and the physical system within the control loop. In this paper, a compositional approach focused on inclusion preserving long term symbolic dependency modeling is proposed for the analysis of NNCSs. First of all, the matrix structure of symbolic zonotopes is exploited to efficiently abstract the input/output mapping of the loop elements through (inclusion preserving) affine symbolic expressions, thus maintaining linear dependencies between interacting blocks. Then, two further extensions are studied. Firstly, symbolic polynotopes are used to abstract the loop elements behaviour by means of polynomial symbolic expressions and dependencies. Secondly, an original input partitioning algorithm takes advantage of symbol preservation to assess the sensitivity of the computed approximation to some input directions. The approach is evaluated via different numerical examples and benchmarks. A good trade-off between low conservatism and computational efficiency is obtained., Comment: 14 pages, 6 figures, 3 tables

The paper deals with joint state and parameter estimation for nonlinear continuous-time systems. Based on a guaranteed LPV approximation, the set adaptive observers design problem is solved avoiding the exponential complexity obstruction usually met in the set-membership parameter estimation. Potential application to fault diagnosis is considered. The efficacy of the proposed set adaptive observers is demonstrated on several examples.