Distributed State Estimation for Linear Systems in Networks With Antagonistic Interactions.

This article deals with the distributed state estimation problem for linear systems in networks with cooperative interactions and antagonistic interactions, where the cooperative interactions and the antagonistic interactions are characterized by the positive weights and the negative weights, respectively. Due to the coexistence of the cooperative interactions and the antagonistic interactions, the existing methods based on the non-negatively weighted graph become not applicable. First, a partition method of the nodes and a decomposition form of the system matrices are introduced. Then a new form of distributed observers is proposed in the network with cooperative interactions and antagonistic interactions. Necessary and sufficient conditions, which guarantee the existence of proposed distributed observers, are presented. In particular, when the communication network is cooperative, the proposed method is suitable for two cases, that is, the nodes in each independent strongly connected component are jointly detectable, or each node is jointly detectable with its neighbors. Finally, three examples are simulated to illustrate the effectiveness of the proposed methods.

Fuzzy Adaptive Finite-Time Resilient Control Against Unknown False Data Injection Attacks for MIMO Nonlinear Switched Systems With Unknown Dead Zone.

This article studies the finite-time adaptive resilient control problem for MIMO nonlinear switched systems with the unknown dead zone. The sensors of the controlled systems suffer from unknown false data injection (FDI) attacks so that all states cannot be directly applied to the design process of the controller. To address this negative impact of FDI attacks, a new coordinate transformation is designed in control design. Moreover, the Nussbaum gain technique is introduced to deal with the difficulty of unknown time-varying weights caused by FDI attacks. Based on the common Lyapunov function method, a finite-time resilient control algorithm is designed by employing compromised state variables, which ensures that all signals of the closed-loop systems are bounded under arbitrary switching rules even in the presence of unknown FDI attacks. Compared with the existing results, the proposed control algorithm not only enables the controlled systems to reach an equilibrium state in a finite time but also removes the assumption that the sign of the attack weights is positive. In the end, a practical simulation example proves that the designed control method is valid.

Optimal Stealthy Attacks With Energy Constraint Against Remote State Estimation.

This article investigates the problem of energy-constrained stealthy attack strategy against remote state estimation for cyber-physical systems. Taking into account the energy constraint, the malicious attacker is required to schedule the off-line generated signals to modify the transmitted data with limited times over a finite-time horizon under the stealthiness condition, which makes the design of attack strategy more complex. Different from the attack schedules which are studied on the basis of prescribed attack signals in the existing results, the attack strategy is presented under the framework of collaborative design to deteriorate the estimation performance to the largest extent, which yet leads to the coupling between the attack schedules and attack signals. To overcome the difficulty without sacrificing the optimality, the attack design problem is solved in two steps. First, analyze the problem with the given attack schedule to derive the optimal attack signals. Then, the optimal schedule is obtained by efficiently solving the nonlinear 0-1 programming problem based on the algorithm of reducing the search space which is designed to eliminate a part of the nonoptimal solutions. To demonstrate the theoretical results, a simulation example is provided.

Clinical efficacy of tetrandrine in artificial stone-associated silicosis: A retrospective cohort study.

Background: Outbreaks of silicosis have occurred among workers in the artificial stone (AS) industry, and there is currently no effective antifibrosis treatment for silicosis. Design: A retrospective cohort study. Methods: We retrospectively analyzed the clinical data of 89 artificial stone-associated silicosis patients treated in Shanghai Pulmonary Hospital (China). Patients who agreed to be administered tetrandrine entered the observation group and those who disagreed entered the control group. Changes in chest HRCT, pulmonary function, and clinical symptoms of patients in two groups were compared pre- and post-treatment. Results: After treatment for 3-12 months, 56.5%-65.4% of patients in the observation group showed improvements in HRCT imaging, while there was no improvement in the control group (p < 0.05). Disease progression occurred in 0%-17.4% of patients in the observation group after 3-12 months of treatment compared with 44.4%-92.0% of patients in the control group (p < 0.05). After 3 months of treatment, the forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and diffusing capacity of the lung for carbon monoxide (DLco) in the observation group increased by 136.7 ± 189.2 mL (p < 0.05), 124.2 ± 169.9 mL (p < 0.05), and 1.4 ± 2.3 mL/min/mmHg (p > 0.05), respectively, while those in the control group decreased (145.8 ± 356.5; 107.5 ± 272.1; 1.9 ± 3.8). After 6 months of treatment, FVC, FEV1, and DLco in the observation group increased by 207.8 ± 372.2 mL (p > 0.05), 107.8 ± 295.2 mL (p > 0.05) and 0.7 ± 6.0 mL/min/mmHg (p > 0.05), respectively, while those of the control group decreased (383.3 ± 536.7; 215.6 ± 228.9; 1.4 ± 1.7). The incidences of clinical symptoms such as cough, expectoration, dyspnea, chest tightness, and chest pain in the observation group were decreased-after treatment (all p < 0.05), while the incidences of these symptoms increased in the control group, although the change was not statistically significant (all p > 0.05). Conclusion: Tetrandrine can control and delay the progression of AS-associated silicosis fibrosis, with improved chest HRCT imaging and pulmonary function.

Optimal energy constrained deception attacks in cyber-physical systems with multiple channels: A fusion attack approach.

This paper studies the issue of developing the optimal deception attacks on the multiple channels in cyber-physical systems, where the attackers are limited by energy constraints. To fully utilize the eavesdropped data, by linearly combining the innovations from the different channels, a fusion attack model is proposed under the stealthiness condition. According to the statistical characteristics of the correlated stochastic variables and the orthogonality principle, the state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrices of the remote estimators under the proposed attack framework. Moreover, by analyzing the correlations of the decision variables in the objective function, it is shown that the attack parameters and energy allocation strategy can be derived by two steps without loss of optimality, such that the optimal attack scheme is acquired by solving a multivariate semi-definite programming (SDP) problem and a linear 0-1 programming problem respectively. Finally, simulation examples are provided to illustrate the effectiveness of the proposed method.

Secure State Estimation of Nonlinear Cyber-Physical Systems Against DoS Attacks: A Multiobserver Approach.

This article focuses on the problem of secure state estimation for cyber-physical systems (CPSs), whose physical plants are modeled as nonlinear strict-feedback systems. The measured output is sent to the designed observer over a wireless communication network subject to denial-of-service (DoS) attacks. Due to the energy constraints of the attackers, the attack duration is upper bounded. Under DoS attacks, the transmission is prevented, which worsens the estimation accuracy of the existing nonlinear observers significantly. To maintain the estimation performance, a novel multiobserver scheme and a switched algorithm are proposed by introducing the hold-input mechanism and the cascade observer technique. In comparison to the existing results, where the estimation error systems may be unstable during the attack time interval, the estimation error of the designed observer converges exponentially, such that the estimation performance is improved effectively. Finally, the theoretical findings are illustrated by simulation results.

Event-Based Remote State Estimation for Nonlinear Systems: A Box Particle Filtering Method.

This article is concerned with the problem of event-based remote state estimation for nonlinear/non-Gaussian systems on a wireless network with limited bandwidth. To reduce unnecessary data transmissions, a novel event-triggering mechanism is developed by using the least-square technique. Based on this, an event-triggered box particle filtering scheme is designed to realize the minimum mean-squared error estimation at the remote estimator end, in which the posterior probability density functions are calculated separately according to the information of the event-triggered indicator to avoid the problem of excessive estimation error. Different from the existing approaches, the proposed algorithm does not depend on any Gaussian assumptions and reduces the computational complexity under the premise of ensuring the estimation performance. Finally, two simulation examples are performed to demonstrate the validity of the proposed algorithm.

Distributed Event-Triggered Algorithm Designs for Resource Allocation Problems via a Universal Scalar Function-Based Analysis.

In this article, we are concerned with distributed algorithm designs for resource allocation problems via event-triggered communication. The target is to search an optimal resource allocation scheme such that the summation of 1) objective functions is minimized. Due to communication efficiency and privacy concerns, distributed algorithms with event-triggered communications are proposed in this article. The communication is only permitted or triggered if variation of gradient of the local objective function exceeds a threshold. By constructing a novel technical lemma and a universal scalar function, the convergence and linear convergence rates are established under some mild assumptions. Extensive numerical experiments on the IEEE 118-bus power system demonstrate that: Compared to the periodic algorithms, such as ADMM and Mirror-P-EXTRA, the proposed algorithms not only remarkably reduce the communication times but also have competitive convergence speed. The latter is striking that it implies there exist useless communications in the periodic algorithms that are censored by the proposed event-triggered strategy.

Optimal Stealth Attack Strategy Design for Linear Cyber-Physical Systems.

This article studies the problem of the optimal stealth attack strategy design for linear cyber-physical systems (CPSs). Virtual systems that reflect the attacker's target are constructed, and a linear attack model with varying gains is designed based on the virtual models. Unlike the existing optimal stealth attack strategies that are designed based on sufficient conditions, necessary and sufficient conditions are, respectively, established to achieve the optimal attack performance while maintaining stealth in virtue of the solvability of certain coupled recursive Riccati difference equations (RDEs). Under those conditions, an optimal stealth attack strategy is constructed by an offline algorithm. A simulation example is applied to verify the effectiveness of the presented technical scheme.

Resilient Event-Triggered Distributed State Estimation for Nonlinear Systems Against DoS Attacks.

This article investigates the resilient event-triggered (ET) distributed state estimation problem for nonlinear systems under denial-of-service (DoS) attacks. Different from the existing results mainly considering linear or specified nonlinear systems, more general nonlinear systems are considered in this study. Moreover, the considered DoS attacks are able to compromise different communication links among estimators independently. In this context, by resorting to the techniques of incremental homogeneity, a nonlinear ET distributed estimation scheme is designed to estimate the states and regulate the data transmission. Under this scheme, the resilient state estimation is achieved by employing a multimode switching estimator, and the problem of efficiency loss of the ET mechanism caused by DoS attacks is solved by designing a dynamic trigger threshold with switched update laws. Then, based on the decay rates of the Lyapunov function corresponding to different communication modes, sufficient conditions are given to guarantee the stability of the estimation error system under DoS attacks. Finally, simulation results are provided to verify the effectiveness of the proposed method.

Event-Triggered Distributed State Estimation for Multiagent Systems Under DoS Attacks.

This article investigates the problem of event-triggered distributed state estimation for linear multiagent systems under denial-of-service (DoS) attacks. In contrast to the previous studies where the agents have access to the measurements of their own states, an estimation algorithm is provided based on the measurements relative to the adjacent agents, and the considered DoS attacks jam each channel independently while the attack durations are constrained. To estimate the states and effectively schedule the information transmissions over the network possibly subject to malicious attacks, a prediction-based switching observer scheme with an event-triggered communication strategy is proposed, and the invalidation problem of the event-triggering mechanism caused by the DoS attacks is solved. Based on the decay rates of the Lyapunov function corresponding to different transmission modes, sufficient conditions for the stability of the estimation error dynamics are presented in the presence of DoS attacks. Finally, the effectiveness of the proposed strategy is demonstrated by simulation results.

Optimal Steady-State Regulator Design for a Class of Nonlinear Systems With Arbitrary Relative Degree.

In this article, we consider the regulator design problem for a class of uncertain multi-input-multioutput (MIMO) nonlinear systems with arbitrary relative degree. The objective is to regulate the output of the nonlinear system to an optimal steady state that solves a constrained optimization problem, without computing the optimal solution in advance. By embedding saddle-point dynamics, both state and output-feedback-based regulators are proposed and the resulting closed-loop systems are modeled in standard singularly perturbed forms. By invoking the singular perturbation analysis, exponential stability is established under some regularity condition. Compared with the existing methods, the proposed regulators can deal with a class of nonlinear systems with uncertainties and arbitrary relative degree. Furthermore, the current results can include some recent works on the distributed optimization problem as special cases. Finally, the effectiveness of the proposed methods is validated through numerical simulations.

Event-Triggered Distributed State Estimation for Cyber-Physical Systems Under DoS Attacks.

This article investigates the event-triggered distributed state estimation problem for a class of cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. First, an observer-based event-triggered transmission scheme is proposed to improve the transmission efficiency, and the corresponding distributed Kalman filter is designed to estimate the system states. Under the collective observability condition, a relationship between estimation error covariance, attack intensity, and transmission efficiency is established by utilizing the covariance intersection fusion method and the property of matrix congruent transformation rank. The important features that distinguish our work from others are that the considered DoS attacks compromise each channel independently and do not have to satisfy the probabilistic property of the packet loss process. Furthermore, an event-triggered communication scheme is considered to improve the utilization of network resources between filters, and a sufficient condition for the parameter design is given which takes into account the influence of DoS attacks. Finally, simulation results are provided to verify the effectiveness of the proposed methods.

Supervisory Nonlinear State Observers for Adversarial Sparse Attacks.

This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. Compared with the existing results, the attacked sensor set can be changed by adversaries against secure estimation. To address the more erratic attacks, a novel supervisory state observer is proposed, which employs a bank of candidate nonlinear subobservers and a switching logic administrated by a monitoring function to select the active subobserver at every instant of time. Based on the stability analysis of switched systems, it is proven that the supervisory observer asymptotically converges to a neighborhood of the true system state in the presence of the sensor attacks and bounded disturbances. A simulation example is given to substantiate the theoretical results.

Secure State Estimation With Switched Compensation Mechanism Against DoS Attacks.

This article is concerned with the secure state estimation problem for cyber-physical systems under intermittent denial-of-service (DoS) attacks. Based on a switching scheme and the cascade observer technique, a novel resilient state observer with a switched compensation mechanism is designed. Moreover, a quantitative relationship between the resilience against DoS attacks and the design parameters is revealed. Compared with the existing results, where only the boundedness of the estimation error is guaranteed under DoS attacks, the exponential convergence of the estimation error is achieved by employing the proposed observer scheme, such that the estimation performance is improved. More specifically, in the disturbance-free case, it is proven that the state estimation error converges exponentially to 0 despite the existence of DoS attacks. Finally, simulation results are provided to illustrate the effectiveness and merits of the proposed methods.

Remote State Estimation for Nonlinear Systems via a Fading Channel: A Risk-Sensitive Approach.

This article investigates the problem of remote state estimation for nonlinear systems via a fading channel, where the packet losses may occur over the sensor-to-estimator communication network. The risk-sensitive (RS) approach is introduced to formulate the estimation problem with intermittent measurements such that an exponential cost criterion is minimized. Based on the reference measure method, the closed-form expression of the nonlinear RS estimator is derived. Moreover, stability conditions for the designed estimator are established by extending the contraction analysis of the linear cases. In contrast to the linear cases, a novel cost function is designed to obtain the finite-dimensional nonlinear estimate, which counteracts the linearization errors by treating them as model uncertainties. Simulation results illustrate that the proposed nonlinear estimator achieves better estimation qualities compared with the existing nonlinear minimum mean square error methods.

Kullback-Leibler Divergence-Based Optimal Stealthy Sensor Attack Against Networked Linear Quadratic Gaussian Systems.

This article concentrates on designing optimal stealthy attack strategies for cyber-physical systems (CPSs) modeled by the linear quadratic Gaussian (LQG) dynamics, where the attacker aims to increase the quadratic cost maximally and keeping a certain level of stealthiness by simultaneously intercepting and modifying the transmitted measurements. In our work, a novel attack model is developed, based on which the attacker can launch strictly stealthy or ϵ -stealthy attacks. To remain strictly stealthy, the attacker only needs to solve an off-line semidefinite program problem. In such a case, the attack performance is optimal but limited. To achieve a higher desired attack effect than that of the strictly stealthy attack, the attacker sometimes needs to sacrifice the stealthy level. Thus, the ϵ -stealthy attack is analyzed, where an upper bound of the optimal attack performance is obtained by solving a convex optimization problem. Then, an optimal ϵ -stealthy attack is designed to achieve the upper bound, which differs from the existing suboptimal ϵ -stealthy attack for the considered LQG systems. Finally, the simulations are provided to verify the developed results.

Distributed Fuzzy Adaptive Output-Feedback Control of Unknown Nonlinear Multiagent Systems in Strict-Feedback Form.

This article is concerned with the cooperative tracking control problem for heterogeneous multiagent systems in a leader-following form under a directed graph. The dynamics of each following agent is unknown, obeying a strict-feedback form. With the help of fuzzy-logic systems, input filters, and constraint-handling schemes, a fully distributed output-feedback control algorithm is proposed to achieve output synchronization with prescribed performance and guarantee boundedness of signals in the closed-loop systems. In addition, the algorithm exhibits a simplicity control attribute in the sense that: 1) the control design utilizes only relative output measurements, and no extra information needs to be transmitted via the network and 2) the issue of explosion of complexity is addressed, without employing command filters or dynamic surface control techniques. Finally, the simulation results clarify and verify the established theoretical findings.

Stability Analysis for Cyber-Physical Systems Under Denial-of-Service Attacks.

This article investigates the stability analysis problem for cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. Based on the real-time data characterizing the suffered DoS attack, a necessary and sufficient condition for the closed-loop stability in the presence of DoS attacks is provided. Besides, by transforming stability analysis the system under DoS into stability analysis of an auxiliary system, novel sufficient conditions for the closed-loop stability, which can be verified more easily than the necessary and sufficient condition, are provided. Based on the proposed conditions, an online stability monitoring strategy, which triggers an alarm if the closed-loop stability is not guaranteed, is proposed. Finally, two examples are given to illustrate the effectiveness of the proposed strategy.