This paper addresses the finite-time event-triggered control problem for nonlinear semi-Markovian switching cyber-physical systems (S-MSCPSs) under false data injection (FDI) attacks. Compared with the traditional time-triggered mechanism, the proposed event-triggered scheme (ETS) can effectively avoid network resource waste. Considering the network-induced delay in the modeling, a closed-loop system model with time delay is established in the unified framework. By the use of a mode-dependent piecewise Lyapunov-Krasovskii functional (LKF), stochastic finite-time stability (SFTS) criteria are established for the resultant closed-loop system. Then, some solvability conditions are established for the desired finite-time controller in light of a linear matrix inequality framework. Finally, an application example of vertical take-off and landing helicopter model (VTOLHM) is provided to demonstrate the effectiveness of the theoretical findings.

Finite-Time Event-Triggered Control for Semi-Markovian Switching Cyber-Physical Systems With FDI Attacks and Applications

Hybrid-triggered-based security controller design for networked control system under multiple cyber attacks

Surveillance in antisubmarine warfare has traditionally been carried out by means of submarines or frigates with towed arrays. These techniques are manpower intensive. Alternative approaches have recently been suggested using distributed stationary and mobile sensors, such as autonomous underwater vehicles (AUVs). In contrast with the use of standard assets, these small, low-power, and mobile devices have limited processing and wireless communication capabilities. However, when deployed in a spatially separated network, these sensors can form an intelligent network achieving high performance with significant features of scalability, robustness, and reliability. The distributed information FUSION (DIFFUSION) strategy, in which the local information is shared among sensors, is one of the key aspects of this intelligent network. In this paper, we propose two DIFFUSION schemes, in which the information shared among sensors consists of: 1) contacts, generated by the local detection stage and 2) tracks, generated by the local tracking stage. In the first DIFFUSION scheme, contacts are combined at each nodes using the optimal Bayesian tracking based on the random finite set formulation. In the second DIFFUSION scheme, tracks are combined using the track-to-track association/fusion procedure, then a sequential decision based on the association events is exploited. A full validation of the DIFFUSION schemes is conducted by the NATO Science and Technology Organization—Center for maritime research and experimentation during the sea trials Exercise Proud Manta 2012–2013 using real data. Performance metrics of DIFFUSION and of local tracking/detection strategies are also evaluated in terms of time-on-target (ToT) and false alarm rate (FAR). We demonstrate the benefit of using DIFFUSION against the local noncooperative strategies. In particular DIFFUSION improves the level of TOT (FAR) with respect to the local tracking/detection strategies. In particular, the TOT is increased over 90%–95% while the FAR is reduced of two order of magnitude. The problem of communication failures, data not available from the collaborative AUV during certain periods of time, is also investigated. The robustness of DIFFUSION with respect to these communication failures is demonstrated, and the related performance results are reported here. In particular, with 75% of communication failures the ToT is over 90%–95% with a relatively small increase of the FAR with respect to the case of perfect communication.

Distributed information fusion in multistatic sensor networks for underwater surveillance

Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: a torus-event-triggering mechanism

For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

This paper investigates the probabilistic-constrained distributed filtering problem for a class of nonlinear stochastic systems with state constraints and cyber attacks. The considered cyber attacks are periodic denial-of-service (DoS) attacks which are modeled by a kind of periodic pulse-width-modulated (PWM) jamming signals. Different from some existing works, by using the proposed filter design method, the probability of the filtering error exceeding a threshold can be guaranteed below a certain level quantitatively. Furthermore, in order to economize the limited bandwidth resources, an event-triggered communication scheme (ETS) is designed for the data transmission. The aim of the problem addressed is to design a time-varying distributed filters such that: 1) the probability of the filtering error restricted to a given ellipsoid is larger than a specified value; and 2) the obtained ellipsoid threshold is minimized in the sense of matrix norm at each time point. To achieve this purpose, a recursive linear matrix inequality method is utilized and sufficient conditions are derived. Moreover, the filter parameters are explicitly determined in terms of the solution to certain matrix inequalities. Finally, the reliability and applicability of the proposed distributed filtering strategy are demonstrated by an illustrative example.

Xinmeng Wang

Papers

Probabilistic-Constrained Distributed Filtering for a Class of Nonlinear Stochastic Systems Subject to Periodic DoS Attacks