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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.

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.

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.

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.

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.

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.

The assembly process of car body in white (BIW) in body shop is a very important link in the whole vehicle manufacturing process. Hundreds of stamping parts must be located and joined on the correct location of the platform, and the related accuracy influence the performance and quality of the cars. To guarantee the BIW manufacturing accuracy, the dimension control is a critical task in each automobile production line. In Comparison to traditional offline dimension control, this article proposed an event-triggered feedforward predictive control method to realize real-time inline dimension control during BIW assembly process. Experiments based on the actual production data from BMW Brilliance Automotive Ltd. demonstrate this control strategy can significantly improve the final products key point pass rate and decrease the fixture adjustment frequency.

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.

Multi-agent distributed optimal coordination (DOC) involves the motion conflicts of large numbers of physical systems, which leads to significant safety challenges in terms of collision avoidance. This paper studies the problem of secure DOC for multiple uncertain Euler-Lagrangian (EL) systems. The objective is to steer each EL agent to achieve the optimization task while avoiding collisions with other agents. The main challenge focuses on the co-design of optimal coordination strategy and collision avoidance mechanism. The safety barrier certificates in the closed form of path integrals are constructed which generate provably collisions-free behaviors. With convex optimization and adaptive nonlinear control, a fully distributed protocol is proposed. Based on Lyapunov method and boundedness analysis for the barrier functions, it is proved that the protocol simultaneously guarantees the global convergence and collision avoidance of the EL systems in the presence of parametric uncertainties.

This article focuses on the leader-following consensus for linear multiagent systems under a directed communication topology. A novel dynamic event-triggered mechanism is put forward to schedule the interagent communication. It removes the assumption that each agent has to broadcast continuously, and therefore resource consumption is reduced significantly. Different from the existing event-triggered mechanisms, the proposed mechanism involves internal dynamic variables that play an important role in excluding Zeno behavior. Then, an event-based distributed control protocol is developed by using locally triggering data. It is proven that the leader-following consensus is achieved under our control strategy. Moreover, the criterion for designing a desired triggered mechanism and the control protocol is derived in terms of linear matrix inequalities. Numerical simulations demonstrate that the proposed strategy is capable of reducing communication more effectively than the existing results.

This paper investigates the man-in-the-middle (MITM) attack against cyber-physical systems (CPSs) under the random access protocol (RAP) scheduling, where an attacker intercepts and modifies the transmitted data and then forwards them on to degrade the system performance. The RAP schedules the sensing devices to avoid data collisions, where only one node is allowed to access the shared communication channel at each time instant. Hence, it makes the existing stealthy attacks invalid. To overcome the protocol-induced effects, a novel attack model utilizing only part of the measurements at each time instant is proposed, based on which the strictly stealthy and ∊ -stealthy attacks are designed. For strictly stealthy attack, the Kullback-Leibler divergence (KLD)-based stealthy constraint is converted into a linear matrix inequality and then a semi-definite program problem is constructed to obtain the optimal attack parameters. In such case, the attack performance is optimal but limited. Furthermore, an ∊ -stealthy attack is proposed to achieve higher attack performance, where the analytical attack parameters are obtained by solving an off-line convex optimization problem. Finally, simulations are provided to verify the results.

This article studies the distributed composite optimization problem over relay-assisted networks (DCOP-RNs). By combining the local primal–dual updates with a data fusion strategy, an effective distributed algorithm is developed to solve the DCOP-RN. Compared with the existing linearized alternating direction method of multipliers (ADMMs), this article provides a novel interpretation of the proposed algorithm. More specifically, it is shown that the proposed algorithm can be interpreted as a simplified proximal augmented Lagrangian method . Within this framework, a simplified convergence analysis of the algorithm is conducted, based on which the convergence to an optimal solution to the DCOP-RN is proved. Further, taking into account the limited bandwidth in real networks, this article proposes a quantized distributed algorithm to solve the DCOP-RN. Especially, a relationship between the quantization resolution and the convergence accuracy of the algorithm is established. Finally, simulation examples verify the theoretical results.

This article is concerned with the tracking control problem for a sort of networked system with unknown nonlinear functions, unmatched disturbances, and event-triggered input. A novel prescribed performance control strategy together with an actuator update protocol is developed to form a solution. Different from the existing results, only a binary signal (either 0 or 1) needs to be sent to the actuator. In this way, the number and the bit of data transmission are both curtailed, leading to the economy of the communication cost. On the other hand, output tracking with guaranteed transient and steady-state performance is achieved regardless of unknown nonlinearities, disturbances, and measurement errors. Finally, simulation results are given to illustrate the established theoretical findings.

This paper investigates the noise covariance estimation problem for the networked linear system under communication protocol. To reduce network burden and avoid data collisions, the data transmission from the sensor nodes to the filter is scheduled by the random access protocol (RAP), where only a sensor node is permitted to send data at each transmission instant. Within the framework, the correlations of the innovations data are utilized to construct a least-squares problem to estimate the noise covariances, where a novel estimation form of the auto-covariance from measurement data is given. Moreover, the two cases of the process noise correlated and uncorrelated with the measurement noise are both discussed. It can be proved that the estimated covariances are unbiased and asymptotically converge to the true values as the sample size increasing. Finally, the simulation results are given to demonstrate the effectiveness of the presented 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.

This article considers the event-driven fault detection (FD) problem for discrete-time interval type-2 (IT2) fuzzy networked control systems (NCSs) with nonlinear perturbations. To reduce signal transmission, an event-driven mechanism in the NCSs is introduced. A novel FD fuzzy filter is designed for generating a residual signal and detecting actuator faults by considering the event-driven strategy, nonlinear perturbations, communication delay, and mismatched membership functions. It is shown that the proposed event-driven FD technique is effective to optimize performance compared with the existing FD strategy. Finally, two examples are presented to verify the advantages of the novel design technique.

This article investigates the distributed secure state estimation problem of distributed systems where a set of agents estimates the state cooperatively in the presence of malicious agents. First, a sufficient condition for the solvability of the distributed secure state estimation problem is proposed. Second, based on the obtained condition, a distributed switched gradient descent (DSGD) algorithm is designed to solve the considered problem which is transformed into a distributed optimization problem. With the help of a candidate-removal mechanism, the proposed DSGD algorithm successfully generates reliable state estimates despite malicious agents. Finally, the effectiveness of the proposed algorithm is illustrated by a numerical example.

The problem of stealthy innovation-based attacks in cyber-physical systems is studied in this paper. Different from the existing results which only utilize the current data, a more general attack strategy is designed by combining the historical and the current innovations to deteriorate the estimation performance and keep stealthy to the detector simultaneously. Under the framework of the attacks, the remote state estimation error is analyzed, and the optimal attack policy is derived by solving a convex optimization problem to achieve the maximal estimation error. Moreover, it is proved that the optimal attack strategy is piecewise constant, such that the attack is designed with low calculation cost. Finally, simulation examples are provided to demonstrate the theoretical results.

This article studies the problem of $H_{\infty }$ controller design for discrete-time T–S fuzzy systems under an event-triggered (ET) communication mechanism. By proposing a new asynchronous premise reconstruction approach, new types of ET fuzzy controllers are designed to overcome the challenges caused by the mismatch of premise variables, in which the gains of the designed controllers are automatically updated at different triggering instants according to an online algorithm. By constructing discontinuous Lyapunov functions, it is proved that the proposed ET controllers guarantee the stability and $H_{\infty }$ performance of the closed-loop systems. Two examples are provided to verify the validity of the proposed design method.

In this paper, the input-to-state stability (ISS) control problem is studied for cyber-physical systems (CPSs) in the presence of asynchronous denial-of-service (DoS) attacks. To enhance the expone...

This paper investigates the combined economic environmental dispatch problem of Microgrids in a distributed manner. In order to optimize the competing agminated operation cost and environmental impact objectives simultaneously, a distributed method which combines the distributed consensus based algorithm with dynamic weights is proposed to assign the energy among generation units, energy storage units, and load units. By this method, each unit only needs to share its local information among neighbours, thus it offers better flexibility, robustness and privacy. Besides, different from existing distributed optimization algorithms, the proposed algorithm can solve the multi-objective problem over time-varying directed communication graph with a fixed step-size. Furthermore, it is proved that the proposed algorithm can converge to the optimal solution if the step-size does not exceed some upper bound. The simulation results show that the proposed method can simultaneously coordinate the two conflicting goals of the economic and environmental aspects of Microgrids, and obtain the entire Pareto front, effectively reducing operating costs and pollutant emissions. The advantage of convergence speed is also shown compared with some distributed algorithms.

This paper investigates the stealthy attack problem in cyber-physical systems (CPSs) under the scheduling effects of the Round-Robin protocol (RRP), where the transmission order of the sensor nodes is modeled as a periodic function and only one node is allowed to access the shared communication channel at each time instant. The adoption of the RRP can efficiently avoid data collisions, while it makes the existing innovation-based stealthy attack strategies invalid. Thus, the aim is to design an attack strategy under the protocol-induced behaviors to maximize the attack effect and remain strictly stealthy simultaneously. Different from the existing results, where only single-sensor application scenarios were considered, multi-sensor ones with limited bandwidth are investigated in this work. A new attack model is proposed to overcome the protocol-introduced effects, which utilizes only part of the historical and the current innovations at each time instant. Based on the attack model, the stealthy condition is obtained and converted into the form of linear matrix inequality (LMI). Then, the error covariance under the attack and the RRP is derived to quantify the attack effect. Subsequently, the optimal attack strategy is obtained by solving a semi-definite programming (SDP) problem. Finally, simulations are performed to demonstrate the results.

This article studies an optimal dynamic formation problem for heterogeneous affine nonlinear systems. The nonidenticality in agents and the requirement for dynamic spatial reconfiguration make it a challenging task to coordinate different types of agents to maintain an optimized formation shape. In an architecture of event-triggered decision and control, this article investigates how to fulfill dynamic formation by distributively optimizing a team cost function. The basic idea is to design a decision unit for each agent to generate an implicit trajectory as a servo signal, based on which a control unit is designed with a displacement-gradient-based law to achieve the desired local solution. Typical heterogeneous characteristics including different nonlinearities and nonidentical dimensions are dealt with in a unified framework. It is shown that with the proposed triggering mechanisms, the optimal dynamic formation problem can be solved by a distributed control law with only intermittent communication. In theory, the properties of convergence of trajectory tracking errors, optimality of the team solution, and Zeno-freeness of event-triggered mechanisms are proved. Two simulation examples are given to verify the proposed method.

This paper investigates the distributed adaptive optimization problem for nonlinear multiagent systems with external disturbances. The main goal is to optimize a global objective function by utilizing local and neighboring information while rejecting the external disturbance signals. Different from the existing results, the weight-balanced directed graph is considered and, by introducing the adaptive technique, the local objective functions are allowed only to be differentiable with locally Lipschitz gradients. Moreover, without requiring the system nonlinear functions to be globally Lipschitz, the global asymptotic convergence is obtained if the global objective function is strongly convex. Finally, simulation results are provided to verify the validity of the proposed algorithm.

This paper is concerned with the problem of fault estimation (FE) for cyber-physical systems (CPSs) modeled by interconnected systems. Under denial-of-service (DoS) attacks, the measurement transmission over the communication network is interrupted, which leads to the infeasibility of the existing FE methods. To overcome this difficulty, a novel switching-type FE scheme is proposed. More specifically, a sequence of adaptive observers and an algorithm are constructed to provide the estimates with improved estimation accuracy, and the observer gain matrices are switched to be zero to discard the outdated measurements under DoS attacks. Therefore, the estimation performance is effectively enhanced in the normal case, and a quantitative description of the estimation error divergence rate is given in the attack status. Compared with the existing results, the interconnections among subsystems are fully considered to improve the estimation accuracy, and in the disturbance-free case, it is proven that the mean sequence of the estimate errors converges to zero. Finally, some simulation results are provide to verify the theoretical findings.

This paper studies the design of the optimal stealthy switching location attacks in cyber-physical systems with multi-channels. By introducing the Kullback–Leibler divergence to characterize the stealthiness, an attack strategy with the energy constraint is proposed, which is only able to modify a part of the channels at each time step. Under the proposed attacks, the terminal estimation error is analyzed by utilizing the statistical characteristics of the innovations. And then, different from the DoS attack scheduling problems in the related works, the optimal attack strategy is derived by solving a constrained optimization problem and a 0–1 programming problem, which maximizes the remote estimation error and keeps deceptive simultaneously. Finally, the results are illustrated via the simulation examples.

This article investigates the problem of distributed hypothesis testing over networks. By introducing double and triple cumulations of historical observations, two novel distributed learning algorithms are developed to solve the considered problem. Within this framework, we study the convergence rates of the proposed algorithms. More specifically: 1) It is proven that the algorithm with double cumulation of historical observations has a faster convergence rate than those of the existing algorithms. 2) Furthermore, it is proven that the algorithm with triple cumulation of historical observations can be further accelerated. Especially, based on the theoretical results, this article shows that a tradeoff among convergence rate, transient performance, and memory burden exists in the proposed algorithms. Finally, simulation examples verify the theoretical results.

This paper investigates the resilient control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS). First, a set of partial observers is designed to estimate partial states corresponding to different channels. Second, by combining the proposed partial observers, the finite-time observer technique and a switching scheme, a modified finite-time partial observer-based controller is proposed to stabilize the system in the presence of DoS. Compared with the existing results for the CPSs with multiple transmission channels, the computational burden of designing observer-based controller is reduced, and the resilience against DoS is improved by adopting the proposed finite-time partial observers. Especially, besides stability, the proposed resilient observer-based controller also provides better disturbance rejection performance.

This paper is concerned with the private distributed optimization problem for multi-agent systems, where all agents cooperatively minimize the sum of individual convex objective functions with the additional requirement that the functions remain differential privacy. Based on the augmented Lagrangian algorithm, an effective distributed algorithm is presented to ensure the privacy by perturbing primal estimates with additive Laplace noises. The proposed algorithm is more likely to have a faster convergence rate compared to the primal-based algorithms, and has less computational burden contrast with the ADMM-based private algorithms. By means of an important factorization of weighted Laplacian matrix, it is proven that the error between optimal solution and ergodic average estimates is bounded, which is related to the privacy level. Within this framework, trade-offs between privacy level and convergence accuracy are analyzed. Finally the effectiveness of the proposed algorithm is illustrated by numerical examples.

In this paper, the problem of adaptive fault-tolerant tracking control for strict-feedback nonlinear systems is investigated. By using the Nussbaum function technique, the difficulty caused by input quantization and infinite number of time-varying actuator faults is handled and a novel fuzzy adaptive control scheme is designed without estimating a lower bound on the quantizer parameter and the time-varying effectiveness factor. Based on the Lyapunov stability theory, it is proven that all the signals in the resulting closed-loop system are bounded and the tracking error satisfies the desired performance all the time. Simulation results are presented to verify the effectiveness of the proposed controller.