This paper is concerned with the state estimation problem for a class of discrete-time singularly perturbed systems with distributed time-delays. During the data transmission through a network channel of limited bandwidth, for the sake of collision avoidance and energy saving, a dynamic event-triggered scheme is employed to schedule the data communication from the sensors to the designed estimator. First, for a given singular perturbation parameter (SPP), by constructing a novel Lyapunov-Krasovskii SPP-dependent functional, sufficient conditions are obtained to guarantee the exponentially mean-square ultimate boundedness of the error dynamics of the state estimation. Furthermore, in the case that the SPP does not exceed a predefined upper bound, a design algorithm is developed for the desired state estimator ensuring that the error dynamics is exponentially mean-square ultimately bounded. In this case, by solving certain matrix inequalities, the estimator gain is characterized without needing to know the exact SPP (as long as it stays below the given upper bound). Moreover, the ultimate bound of the error dynamics is estimated. Finally, simulation results are given to confirm the validity and advantages of the proposed design scheme of the state estimator.

In this article, the $H_{\infty }$ control problem is investigated for a class of discrete-time singularly perturbed systems (DTSPSs) with time-delays and sensor saturations. In order to save the network bandwidth with satisfactory communication reliability during the signal transmissions, a dynamic event-triggered mechanism (DETM) is put forward to regulate the data-packet transmissions from the saturated sensors to the proposed controller. By constructing a novel Lyapunov–Krasovskii functional dependent on the singular perturbation parameter (SPP), the dynamics of the DTSPSs under the DETM are rigorously analyzed, and an output-feedback controller design scheme is then developed to ensure that, for any SPP not exceeding a prescribed upper bound, the closed-loop system is asymptotically stable with a guaranteed $H_{\infty }$ performance index. By resorting to the convex programming techniques, the desired controller gain is parameterized in light of the feasible solutions to a set of matrix inequalities. Finally, the effectiveness and merits of the proposed algorithm are demonstrated by a simulation example.

The subject of attack detection for industrial cyber-physical systems (IPCSs) is covered in this paper, which addresses threats from transient covert attacks (TCAs), also referred to as the second version of replay attacks with a specific frequency and short duration. A comprehensive model of the TCAs is built using the active instant and period of the attacks, as well as the dynamics of a virtual system to replicate IPCS function and produce attack signals. Though watermarking-based detection algorithms have been shown to be effective in detecting TCAs, the induced system performance loss is too significant, and as such, our primary goal is to minimize system performance degradation while maintaining the detection rate. Because the active periods of TCAs are substantially shorter than their sleep ones, or even because they are practically always silent, it makes sense that reducing superfluous watermarking will facilitate system performance. So, using an “event-triggered” strategy, a unique recursive watermarking-based detection algorithm is proposed. Here, the trigger modes of watermarking are divided into three types: forced, high probability, and low probability. The design principles are proven via algorithms and criteria, and a theoretical analysis of the detection rate and the system performance loss is also supplied. The advantages of the suggested algorithms are finally demonstrated by numerical simulations of a quadruple-water-tank system and experiments with a permanent magnet synchronous motor on the dSpace platform.

We consider the robust state estimation over sensor networks with non-Gaussian noise, which is often encountered in many applications. Motivated by the fact that the elliptical distribution is the natural extension of the Gaussian distribution and includes a variety of non-Gaussian distributions with heavy-tailed characteristics, we here adopt the elliptical distribution to model the heavy-tailed process and measurement noise. The general state evolution model is used to replace the process equation and the elliptical distribution is denoted as a Gaussian mixture form. Based on that, the posterior estimation of the system state together with the parameters of the process and measurement noises can be inferred by the variational Bayes method. Moreover, the corresponding distributed estimation algorithm is then provided, which enables distributed implementation. The target tracking over sensor networks is used to show the estimation accuracy of the proposed algorithms.

This article is concerned with the event-based security control problem for a class of multi-time-scale cyber physical systems (MTSCPSs) subject to DoS attacks. To tackle the difficulties caused by DoS attacks and the multi-time-scale feature (MTSF), a security control framework is developed to co-design the switched controller and event-triggered mechanism (ETM). An improved dynamic ETM is proposed, with which the closed-loop system can achieve a better control performance than traditional dynamic ETMs in the transient process. A novel $\epsilon$ -dependent Lyapunov-Krasovskii functional is also established, via which sufficient conditions are derived to ensure the $H_{\infty }$ performance index of the closed-loop system and further cope with the ill-conditioned numerical issues generated by the MTSF. Finally, a simulation on a mini mining-tram drive control system is presented to demonstrate the correctness and merits of the proposed algorithm.

This article addresses the replay attack detection issue for a class of industrial process operation optimization (IPOO) cyber-physical systems (CPSs). In contrast to conventional CPSs with a single-loop structure, the IPOO CPSs employ a dual-layer network environment consisting of a wireless network for the setpoint optimization loop and a controller area network (CAN) bus for the device control loop. Consequently, the issue of attack detection is more complicated for the IPOO CPSs, and this motivates our current research. First, a unified model of the IPOO CPS is built, with PI controller controlling the physical plants and linear quadratic Gaussian (LQG) controller managing setpoint optimization. Then, a novel joint watermarking detection mechanism is established with the PI controller watermarking, LQG controller watermarking, and a watermarking compensator. The proposed watermarking compensator with an augmented Kalman filter is utilized to efficiently eliminate false alarms brought on by information interactions of the coupled cyber-layers, enabling the accurate detection and location of replay attacks. Furthermore, a linear relationship is established between watermarking parameters and system performance loss. Finally, simulations with a quadruple water tank system are conducted to verify the effectiveness of the proposed algorithm.