Event-triggered control of networked switched systems under network attacks

This paper is concerned with the event-triggered control (ETC) problem for switched systems in networked environments, which are suffered from denial-of-service (DoS) attack and deception attack. Considering that the packet in networked switched systems contains the sampled state and switching signal, DoS attack prevents the packet transmission to cause the zero input, while deception attack falsifies the packet to cause the asynchronous control input with nonlinear disturbance. First, the probabilistic characterization of invalid packet transmissions caused by two different attack models is constructed, namely the ratio of the number of packet transmissions suffering from network attacks to the total number of packet transmissions. Second, a suitable ETC scheme is designed to determine the next triggering instant, which guarantees that the value of Lyapunov function cannot exceed a given restriction and updates the control input at switching instant. After that, the average dwell time and control gains are designed to guarantee the almost sure asymptotic stability of such systems. Finally, a numerical simulation is given to verify the effectiveness of the theoretical results.

A Generalized Stochastic Framework for Analyzing Rock Slopes Stability and Designing Reliable Remedial Measures with Limited Investigation Data: Theory and Field Implementation

Availability of limited data for rock properties is a very frequently encountered issue for the rock slopes along Himalayan highways due to problems like high costs, manual efforts, geological complexities, difficult terrain etc. involved in rock testing and investigation. Under these conditions, support estimation for rock slides mitigation using traditional deterministic and reliability approaches becomes highly questionable due to inaccuracy in the estimated statistical parameters of rock properties. To resolve this issue, this article proposes a computationally efficient methodology which utilizes Advanced Re-Sampling Reliability Approach (ARRA) along with deterministic approach and Target Reliability Approach (TRA) to estimate required support for rock slides mitigation when limited field and laboratory investigation data is available, with acceptable accuracy and confidence. Proposed methodology was used to design the support measures to mitigate two massive rock slides along a rock-slide prone highway i.e. Rishikesh-Badrinath National Highway (NH-58) in India. It was observed from the analysis that availability of limited test data induces high uncertainty in the statistical parameters (mean and standard deviation) and probability distribution of rock properties. Support estimation carried out using traditional deterministic and reliability approaches with this inaccurate probabilistic characterization of rock properties, can lead to inaccurate support estimates for potential rock slides in the presence of limited data; however these methods when coupled with ARRA can lead to significant improvement in computational efficiency and the designer’s confidence for the estimated support.

Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks

The increasing risk of flooding requires obtaining generalized knowledge for the implementation of distinct and innovative intervention strategies, such as nature-based solutions. Inclusion of ecosystems in flood risk management has proven to be an adaptive strategy that achieves multiple benefits. However, obtaining generalizable quantitative information to increase the reliability of such interventions through experiments or numerical models can be expensive, laborious, or computationally demanding. This paper presents a probabilistic model that represents interconnected elements of vegetated hydrodynamic systems using a nonparametric Bayesian network (NPBN) for seagrasses, salt marshes, and mangroves. NPBNs allow for a system-level probabilistic description of vegetated hydrodynamic systems, generate physically realistic varied boundary conditions for physical or numerical modeling, provide missing information in data-scarce environments, and reduce the amount of numerical simulations required to obtain generalized results—all of which are critically useful to pave the way for successful implementation of nature-based solutions.