Observer-Based Event-Triggered Control for Networked Linear Systems Subject to Denial-of-Service Attacks

Bipartite synchronization of competition-cooperation neural networks and its application via truncated sampled-data control

This paper proposes a scheme to reduce coupling strength and achieve bipartite synchronization in competition-cooperation neural networks based on truncated sampled-data control. In this approach, the truncated sampled-data control means that only partial sampling information is used and the rest are discarded. Based on this, this paper proposes the concept of maximum data truncation rate, which quantitatively characterizes the bandwidth savings. A pinning truncated sampled-data controller associated with competition-cooperation interactions is designed, and a tractable error system is constructed using coordinate transformation techniques. Then, aiming at reducing coupling strength, a dual-interval-dependent Lyapunov function is designed according to the characteristics of the control scheme and the network structure. Combining Lyapunov theory with inequality techniques, two sufficient criteria are developed to ensure the bipartite synchronization of competition-cooperation neural networks. Based on these criteria, two algorithms are developed to determine the minimum allowable coupling strength and the maximum allowable data truncation rate, respectively. Two improvements over the previous Lyapunov functions are discussed: reducing the allowable coupling strength and increasing the allowable data truncation rate. Finally, the advantages and effectiveness of the proposed control scheme are demonstrated by an application and numerical examples.

Communication Security and Stability in NNCSs: Realistic DoS Attacks Model and ISTA-Supervised Adaptive Event-Triggered Controller Design

This article addresses the challenge of achieving asymptotic stability in nonlinear networked control systems (NNCSs) amid denial-of-service (DoS) attacks, particularly under constrained communication resources. We begin by establishing a practical DoS attack model using the NSL-KDD dataset, which provides a realistic depiction of DoS attack dynamics based on real-world data. We then introduce the iterative shrinkage-thresholding algorithm (ISTA) to supervise the adaptive event-triggered controller (AETC), ensuring that system parameters are adjusted effectively while conserving communication resources. We develop an enhanced data compression mechanism to further mitigate the impact of DoS attacks on communication servers. Additionally, we construct an asymmetric Lyapunov-Krasovskii function (LKF) to rigorously verify the asymptotic stability of NNCSs. Finally, we empirically validate the effectiveness of our proposed AETC using an autonomous vehicle (AV) model.

Resilient event-triggered observer-based control of non-linear systems under denial-of-service attacks with actuator saturation

This paper studies the control problem for a continuous-time networked system with non-linearity in the state equation as well as in the input, as saturation. The system is considered under denial-of-service (DoS), attacks which cause the blockage of input and/or output components in the overall closed-loop model. An event-triggering scheme that is resilient in nature, along with an observer-based control, has been considered under DoS attacks. The resultant scheme ensures efficient network resources and excludes Zeno behavior naturally due to the presence of a minimum positive interevent delay. Then, an event-based switched non-linear model is presented to address both the event-triggering scheme and the presence of DoS blocking attacks. A piece-wise Lyapunov-Krasovskii functional method on the described non-linear model, resulting in the switched system, is considered for achieving an exponentially stable response by driving the required feasibility conditions. In the presence of a non-linear system with saturation in the actuator, the presented design establishes quantitative relationships among the exponential decay rate, active/sleeping intervals of attacks, parameters of the event-triggering condition, and sampling period of the system. After that, linear matrix inequalities are presented for designing an event-triggered controller with an observer, while the design also includes the region of convergence for dealing with the input non-linearity. Finally, comparative results for an offshore structure model with non-linearity in states as well as in actuator, are demonstrated to verify the results of the control scheme that is developed. It has been verified that our design is less conservative than the previous designs, and can handle the non-linearities in the dynamics of plant and actuator saturation more efficiently, while DoS attacks are also present. By applying our proposed method, the overshoot and undershoot are less than ±2.5 percent, while system states converge to the origin within 55 s.

Neural-Network-Based Adaptive Fault-Tolerant Cooperative Control of Heterogeneous Multiagent Systems With Multiple Faults and DoS Attacks

In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

Sampled-Data-Based Secure Synchronization Control for Chaotic Lur'e Systems Subject to Denial-of-Service Attacks

This article investigates the sampled-data-based secure synchronization control problem for chaotic Lur'e systems subject to power-constrained denial-of-service (DoS) attacks, which can block data packets' transmission in communication channels. To eliminate the adverse effects, a resilient sampled data control scheme consisting of a secure controller and communication protocol is designed by considering the attack signals and periodic sampling mechanism simultaneously. Then, a novel index, i.e., the maximum anti-attack ratio, is proposed to measure the secure level. On this basis, a multi-interval-dependent functional is established for the resulting closed-loop system model. The main feature of the developed functional lies in that it can fully use the information of resilient sampling intervals and DoS attacks. In combination with the convex combination method, discrete-time Lyapunov theory, and some inequality estimate techniques, two sufficient conditions are, respectively, derived to achieve sampled-data-based secure synchronization of drive-response systems against DoS attacks. Compared with the existing Lyapunov functionals, the advantages of the proposed multi-interval-dependent functional are analyzed in detail. Finally, a synchronization example and an application to secure communication are provided to display the effectiveness and validity of the obtained results.

Adaptive tracking control of two-wheeled mobile robots under Denial-of-Service attacks

This paper focuses on the tracking control problem for the two-wheeled mobile robot (TWMR) with unknown parameters. The robot collects its own states from the networked positioning system, which is subject to Denial-of-Service (DoS) attacks. To handle the uncertainties in the robot model and mitigate the attack effects on the system performance, parameter estimators with the projection operator technique are introduced. Then, an adaptive tracking controller is designed by adopting the backstepping technique. Correspondingly, a stability condition is derived, which guarantees that all the closed-loop signals are semi-globally uniformly bounded and tracking errors can converge to an adjustable compact set. The stability condition also reveals the relationships among the attack durations, design parameters and tracking errors, which can be utilized to guide the choice of design parameters. Experimental results are provided to validate the effectiveness of the proposed control scheme.

Secure control scheme for CPSs under DoS attacks via multiple transmission channels and unknown input observer

The paper investigates the secure control problems for cyber-physical systems (CPSs) when the transmission channels suffer from Denial-of-Service (DoS) attacks based on switching observer and unknown input reconstruction (UIR). Firstly, an augmented system whose system state consists of the original system state and the measurement noises is set up, and the preconditions for the original system and augmented system are discussed in detail. Secondly, a full-order observer is constructed to generate the estimations of the augmented system state. Besides, based on the state estimation, an algebraic UIR method is developed and the UIR decouples the control input signal successfully. Thirdly, under the situation that some transmission channels suffer from DoS attacks, an observer-based secure controller is designed based on state estimation feedback and UIR feedback in view of a switching system. The stability of the switching system is analyzed as well. Finally, to verify the effectiveness of the proposed protocols, two simulation examples and the comparison with existing methods are given.

Adaptive event-triggered control for almost sure stability for vehicle platooning under interference and stochastic attacks

This paper considers the adaptive event-triggered strategy and controller design of vehicle platooning for stochastic attacks and interferences in communication channels. Bernoulli distribution and Markovian distribution Denial of Service models are introduced in this paper. In designing the controller, Aiming at the stochastic jamming attacks, the stability criterion is presented to guide the controller for almost sure string stability, and meanwhile aiming at the special safety requirements and the reduction of system interferences, the asymmetry event-triggered strategy framework is presented to adapt the transmission environment and the different safety requirements, which is designed to balance the principal concern in different situations. Finally, an example is introduced to demonstrate the controller performances of the vehicle platooning with the Bernoulli distribution and Markovian distribution DoS models, which implies that the presented methods are effective.

Robust Stabilization of Linear Time-Delay Systems under Denial-of-Service Attacks

This research examines new methods for stabilizing linear time-delay systems that are subject to denial-of-service (DoS) attacks. The study takes into account the different effects that a DoS attack can have on the system, specifically delay-independent and -dependent behaviour. The traditional proportional-integral-derivative (PID) acts on the error signal, which is the difference between the reference input and the measured output. The approach in this paper uses what we call the PID state feedback strategy, where the controller acts on the state signal. Our proposed strategy uses the Lyapunov-Krasovskii functional (LKF) to develop new linear matrix inequalities (LMIs). The study considers two scenarios where the time delay is either a continuous bounded function or a differentiable and time-varying function that falls within certain bounds. In both cases, new LMIs are derived to find the PID-like state feedback gains that will ensure robust stabilization. The findings are illustrated with numerical examples.

Resilient fixed-time stabilization of switched neural networks subjected to impulsive deception attacks

This article focuses on the resilient fixed-time stabilization of switched neural networks (SNNs) under impulsive deception attacks. A novel theorem for the fixed-time stability of impulsive systems is established by virtue of the comparison principle. Existing fixed-time stability theorems for impulsive systems assume that the impulsive strength is not greater than 1, while the proposed theorem removes this assumption. SNNs subjected to impulsive deception attacks are modeled as impulsive systems. Some sufficient criteria are derived to ensure the stabilization of SNNs in fixed time. The estimation of the upper bound for the settling time is also given. The influence of impulsive attacks on the convergence time is discussed. A numerical example and an application to Chua's circuit system are given to demonstrate the effectiveness of the theoretical results.

Hybrid Dynamic Event-Triggered Load Frequency Control for Power Systems With Unreliable Transmission Networks

In this article, we consider the load frequency control problem for a class of power systems based on the dynamic event-triggered control (ETC) approach. The transmission networks are unreliable in the sense that malicious denial-of-service (DoS) attacks may arise in the power system. First, a model-based feedback controller is designed, which utilizes estimated states, and thus can compensate the error between plant states and the feedback data. Then, a dynamic event-triggered mechanism (DETM) is proposed by introducing an internal dynamic variable and a timer variable with jump dynamics. The proposed (DETM) can exclude Zeno behavior by regularizing a prescribed strictly positive triggering interval. Incorporated in the ETC scheme, a novel hybrid model is established to describe the flow and jump dynamics of the power system in the presence of DoS attacks. Based on the hybrid dynamic ETC scheme, the power system stability can be preserved if the attacks frequency and duration sustain within an explicit range. In addition, the explicit range is further maximized based on the measurement trigger-resetting property. Finally, a numerical example is presented to show the effectiveness of our results.

Composite Finite-Time Resilient Control for Cyber-Physical Systems Subject to Actuator Attacks

Cyber-physical systems (CPSs) seamlessly integrate communication, computing, and control, thus exhibiting tight coupling of their cyber space with the physical world and human intervention. Forming the basis of future smart services, they play an important role in the era of Industry 4.0. However, CPSs also suffer from increasing cyber attacks due to their connections to the Internet. This article investigates resilient control for a class of CPSs subject to actuator attacks, which intentionally manipulate control commands from controllers to actuators. In our study, the supertwisting sliding-mode algorithm is adopted to construct a finite-time converging extended state observer (ESO) for estimating the state and uncertainty of the system in the presence of actuator attacks. Then, for the attacked system, a finite-time converging resilient controller is designed based on the proposed ESO. It integrates global fast terminal sliding-mode and prescribed performance control. Finally, an industrial CPS, permanent magnet synchronous motor control system, is investigated to demonstrate the effectiveness of the composite resilient control strategy presented in this article.

Synchronization of Complex Dynamical Networks Subject to DoS Attacks: An Improved Coding-Decoding Protocol

This article investigates the synchronization of communication-constrained complex dynamic networks subject to malicious attacks. An observer-based controller is designed by virtue of the bounded encode sequence derived from an improved coding-decoding communication protocol. Moreover, taking the security of data transmission into consideration, the denial-of-service attacks with the frequency and duration characterized by the average dwell-time constraint are introduced into data communication, and their influence on the coder string is analyzed explicitly. Thereafter, by imposing reasonable restrictions on the transmission protocol and the occurrence of attacks, the boundedness of coding intervals can be obtained. Since the precision of data is generally limited, it may lead to the situation that the signal to be encoded overflows the coding interval such that it results in the unavailability of the developed coding scheme. To cope with this problem, a dynamic variable is introduced to the design of the protocol. Subsequently, based on the Lyapunov stability theory, sufficient conditions for ensuring the input-to-state stability of the synchronization error systems under the communication-constrained condition and malicious attacks are presented. The validity of the developed method is finally verified by a simulation example of chaotic networks.

Dynamically Triggering Resilient Control for Networked Nonlinear Systems under Malicious Aperiodic DoS Attacks

Networked nonlinear systems (NNSs) have great potential security threats because of malicious attacks. These attacks will destabilize the networked systems and disrupt the communication to the networked systems, which will affect the stability and performance of the networked control systems. Therefore, this paper aims to deal with the resilient control problem for NNSs with dynamically triggering mechanisms (DTMs) and malicious aperiodic denial-of-service (DoS) attacks. To mitigate the impact from DoS attacks and economize communication resources, a resilient dynamically triggering controller (RDTC) is designed with DTMs evolving an adaptive adjustment auxiliary variable. Thus, the resulting closed-loop system is exponentially stable by employing the piecewise Lyapunov function technique. In addition, according to the minimum inter-event time, the Zeno behavior can be excluded. Finally, the merits of the proposed controllers and theory are corroborated using the well-known nonlinear Chua circuit.

Dynamic Event-Triggered Output Feedback Control for Networked Systems Subject to Multiple Cyber Attacks

This article is concerned with the problem of the H_∞ output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed H_∞ performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.

Sampled-Data Asynchronous Control for Switched Nonlinear Systems With Relaxed Switching Rules

This article investigates the problem of quantized sampled-data control for continuous-time switched Takagi-Sugeno (T-S) fuzzy systems with the asynchronous phenomenon. First, considering that the system and controller modes could not be perfectly synchronized all the time, we study possible cases of mode mismatching by exploiting the average dwell time (ADT) switching strategy. Since the fact that a minimum dwell time of each subsystem is not required in the ADT switching rule, multiple system switching is allowed within one sampling interval, which overcomes the limitation of at most once switching during any sampling interval in existing works. Second, the asynchronous premise variables between the fuzzy system and fuzzy controller are taken into consideration in the quantized sampled-data control scheme. Then, by virtue of the Lyapunov function approach, we obtain sufficient conditions to guarantee that the asynchronously switched T-S fuzzy system is exponentially stable with quantized sampled-data input. Furthermore, the weighted L₂ -gain is discussed for the system under external disturbance, and an H_∞ state feedback controller is correspondingly designed with prescribed disturbance attenuation. Finally, the validity and advantage of the proposed methods are illustrated by two examples.

Security control for nonlinear systems under quantization and Round-Robin protocol subject to deception attacks

This paper is concerned with the security control problem for the nonlinear systems with the effects of quantization, communication protocol and deception attacks based on the Takagi-Sugeno (T-S) fuzzy model. The measurement output and control input signals are quantized by dynamic quantizers simultaneously, and the quantized signals are transmitted through the communication channels scheduled by Round-Robin (RR) protocol. Moreover, two Bernoulli processes are utilized to characterize the deception attacks occurring on the different channels respectively. The mode-dependent observer-based controller and dynamic quantizers are designed such that the security in probability of the closed-loop system with the prescribed quadratic cost index can be guaranteed. Then, sufficient design conditions are obtained for the controller gains and quantizers' parameters based on the linear matrix inequalities (LMIs) approach. In the end, a numerical example is carried out to demonstrate the validity of the proposed method.

Event-Triggered Resilient L∞ Control for Markov Jump Systems Subject to Denial-of-Service Jamming Attacks

In this article, the event-triggered resilient L_∞ control problem is concerned for the Markov jump systems in the presence of denial-of-service (DoS) jamming attacks. First, a fixed lower bound-based event-triggering scheme (ETS) is presented in order to avoid the Zeno problem caused by exogenous disturbance. Second, when DoS jamming attacks are involved, the transmitted data are blocked and the old control input is kept by using the zero-order holder (ZOH). On the basis of this process, the effect of DoS attacks on ETS is further discussed. Next, by utilizing the state-feedback controller and multiple Lyapunov functions method, some criteria incorporating the restriction of DoS jamming attacks are proposed to guarantee the L_∞ control performance of the event-triggered Markov closed-loop jump system. In particular, the bounded transition rates rather than the exact ones are taken into account. That is appropriate for the practical environment in which transition rates of the Markov process are difficult to measure accurately. Correspondingly, some criteria are proposed to obtain state-feedback gains and event-triggering parameters simultaneously. Finally, we provide two examples to show the effectiveness of the proposed method.

Observer-Based Security Control for Interconnected Semi-Markovian Jump Systems With Unknown Transition Probabilities

This article investigates the issue of observer-based security control for the interconnected semi-Markovian jump systems with completely unknown and uncertain bounded transition probabilities (TPs). Considering the limited bandwidth of communication network in each subsystem, an adaptive event-triggered mechanism (AETM) is developed to relieve more network burden than the conventional event-triggered mechanism (ETM), where the designed adaptive law can dynamically adjust the triggering threshold. In addition, two Bernoulli distributed variables are utilized to describe the influence of denial-of-service (DoS) attacks and false-data injection (FDI) attacks in the proposed observer-based security control strategy. Moreover, some sufficient criterions are derived for the stochastic stability with an H_∞ attenuation level of augmented systems. Meanwhile, the observer and controller gain matrices can be attained simultaneously with the help of linear matrix inequalities (LMIs). Finally, we provide a practical example to demonstrate the effectiveness of theoretical results.

Fully distributed event-triggered secure consensus of general linear multi-agent systems under sequential scaling attacks

In this article, the secure consensus problem is studied for a general linear multi-agent system, where a fully distributed event-triggered scheme is introduced to increase the feasibility and improve the scalability of the control protocol. Firstly, an edge-based sequential scaling attack is formulated with constraints on the attack duration and the frequency, which includes multiplicative deception attacks and DoS attacks as a particular situation when the scaling factor is equal to 0. Then a fully distributed event-triggered control protocol is designed, in which the global information is no longer needed. Sufficient conditions related to triggering parameters, the scaling factor, the attack duration and attack frequency are derived ensuring the asymptotic consensus of MAS. Furthermore, the impact of the triggering parameters and the scaling factor on the attack duration and the attack frequency are also discussed. The Zeno behavior is proved not to happen. Finally, two simulation examples based on unmanned intelligent vehicles are conducted to verify the results.

Robust Dynamic Actuator Failure Compensation Control of Nonlinear Systems via Cooperative Interaction Design

This article studies fault-tolerant resilient control (FTRC) problems for uncertain Takagi-Sugeno fuzzy systems when subjected to additive actuator faults and/or malicious injections on control input signals. The effects of faults and malicious injections are modeled by unknown bounded signals. The signals are produced by any finite- L₂ -gain dynamical system and a Lipschitz and derivable function with respect to states, so that the considered fault model contains some reported ones as special cases. By employing the available state and input data, a function, which is equivalent to a fictitious dynamical system comprising the information about compensation errors for unknown actuator faults, is presented. Then, based on the virtual system, a novel actuator failure compensator (AFC) with the structure of dynamic feedbacks is proposed, so that the compensation capability is improved via cooperative interaction designs between the virtual dynamical systems and closed-loop systems. Furthermore, through the equivalence class and Lyapunov theories, it is proved that the presented robust dynamic AFC-based fuzzy controller ensures the asymptotic convergence of system states to zero. Different from the existing FTRCs, good transient performance is guaranteed, even in the presence of unforeseen actuator faults. Two illustrative examples verify the effectiveness of the presented method.

Event-Triggered Control of Nonlinear Discrete-Time System With Unknown Dynamics Based on HDP(λ)

The heuristic dynamic programming (HDP) ( λ )-based optimal control strategy, which takes a long-term prediction parameter λ into account using an iterative manner, accelerates the learning rate obviously. The computation complexity caused by the state-associated extra variable in λ -return value computing of the traditional value-gradient learning method can be reduced. However, as the iteration number increases, calculation costs have grown dramatically that bring huge challenge for the optimal control process with limited bandwidth and computational units. In this article, we propose an event-triggered HDP (ETHDP) ( λ ) optimal control strategy for nonlinear discrete-time (NDT) systems with unknown dynamics. The iterative relation for λ -return of the final target value is derived first. The event-triggered condition ensuring system stability is designed to reduce the computation and communication requirements. Next, we build a model-actor-critic neural network (NN) structure, in which the model NN evaluates the system state for getting λ -return of the current time target value, which is used to obtain the critic NN real-time update errors. The event-triggered optimal control signal and one-step-return value are approximated by actor and critic NN, respectively. Then, the event trigger-based uniformly ultimately bounded (UUB) stability of the system state and NN weight errors are demonstrated by applying the Lyapunov technology. Finally, we illustrate the effectiveness of our proposed ETHDP ( λ ) strategy by two cases.

Event-based distributed filtering against deception attacks for sensor networks with quantization effect

This paper considers a distributed secure filtering problem for a category of time-varying system subject to uncertainty and model-reality mismatch, two-stage deception attacks and bandwidth limitation. Both deception attacks between sensor and corresponding estimator and among estimators appear randomly. To alleviate communication burden, a quantization strategy is introduced before transmitting measurement and estimation signals. An event-triggered mechanism is employed for each estimator node thus only necessary data are transmitted to its neighbour sensors when a setting event occurs. The desired target of the problem to be handled is to devise a series of time-varying filters such that the H_∞ secure performance is guaranteed against random deception attacks over a finite time horizon. Sufficient conditions ensuring the existence of time-varying filters under effect of complex factors are derived, where filter gains are obtained by finding the solution of a sequence of recursive matrix inequalities online. Simulation results in both numerical example and industrial continuous-stirred tank reactor system are given to show the validity of the presented methodology.

Extended State Functional Observer-Based Event-Driven Disturbance Rejection Control for Discrete-Time Systems

In this article, an event-driven output feedback control approach is proposed for discrete-time systems with unknown mismatched disturbances. To estimate the unavailable states and disturbances, a reduced-order extended state functional observer is proposed, and by introducing an event-driven scheduler, the ZOH-based event-driven output feedback disturbance rejection controller is designed, and the stability and disturbance rejection analyses are performed. To further save the network resources, the predictive event-driven output feedback disturbance rejection control approach is proposed, and the stability and disturbance rejection analyses of the systems with predictive control are also conducted. It can be shown that the disturbances are compensated completely in output channels of the systems, and compared with the time-driven control schemes. And event-triggering frequency is greatly reduced with the proposed event-driven control methods. Finally, the effectiveness of the provided control approaches is demonstrated by numerical simulations.

Strategic Topological Formation for Wireless Control Systems

The excessive use of nodes and communication links in a wireless control system (WCS) causes unnecessary utilization of resources. In this article, a strategic topological formation is studied for a WCS, where a previously proposed topology consisting of a plant system, a controller system, and an intermediate network system is further developed. More specifically, this article presents a modeling framework and a design procedure for the topology that results in the utilization of a reduced number of nodes and communication links. It also discusses several conditions for the connectivity of the nodes under different topological scenarios. This article uses a four-tank process system as an application example to demonstrate the strategic topological formation of its WCS.

Event-Triggered H∞ Filtering for T-S Fuzzy-Model-Based Nonlinear Networked Systems With Multisensors Against DoS Attacks

This article focuses on the problem of resilient H_∞ filtering for Takagi-Sugeno fuzzy-model-based nonlinear networked systems with multisensors. A weighted fusion approach is adopted before information from multisensors is transmitted over the network. A novel event-triggered mechanism is proposed, which allows us not only to reduce the data-releasing rate but also to prevent abnormal data being potentially transmitted over the network due to sensor measurement or other practical factors. The problem of denial-of-service (DoS) attacks, which often occurs in a communication network, is also considered, where the DoS attack model is based on an assumption that the periodic attack includes active periods and sleeping periods. By employing the idea of the switching model for filtering error systems to deal with DoS attacks, sufficient conditions are derived to guarantee that the filtering error system is exponentially stable. Simulation results are given to demonstrate the effectiveness of the theoretical analysis and design method.

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.

A Unified Optimization for Resilient Dynamic Event-Triggering Consensus Under Denial of Service

This article proposes a resilient framework for optimized consensus using a dynamic event-triggering (DET) scheme, where the multiagent system (MAS) is subject to denial-of-service (DoS) attacks. When initiated by an adversary, DoS blocks the local and neighboring communication channels in the network. A distributed DET scheme is utilized to limit transmissions between the neighboring agents. A novel convex optimization approach is proposed that simultaneously co-designs all unknown control and DET parameters. The optimization is based on the weighted sum approach and increases the interevent interval for a predefined consensus convergence rate. In the presence of DoS, the proposed co-design framework is beneficial in two ways: 1) the desired level of resilience to DoS is included as a given (desired) input and 2) the upper bound for guaranteed resilience associated with the proposed co-design approach is less conservative (larger) compared to those obtained from other analytical solutions. A structured tradeoff between relevant features of the MAS, namely, the consensus convergence rate, frequency of event triggerings, and level of resilience to DoS attacks, is established. Simulations based on nonholonomic mobile robots quantify the effectiveness of the proposed implementation.

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system.

Resilient Control for Wireless Cyber-Physical Systems Subject to Jamming Attacks: A Cross-Layer Dynamic Game Approach

For wireless cyber-physical systems (CPSs) suffering jamming attacks, an optimal resilient control method is proposed through a novel cross-layer dynamic game structure in this article. To confirm to practical conditions of the cyber-layer, incomplete communication information is taken into consideration, and a Bayesian Stackelberg game approach is utilized to model interactions between a smart jammer and a cyber-user. Then, an H_∞ optimal resilient controller is studied for the closed-loop system with jam-induced packet losses and external disturbance in the sense of physical layer. With assumptions that the smart jammer has abilities of decoding system inputs and states, the changes of the jamming strategy are studied, and a coupled design between cyber and physical layers is presented with an algorithm to depict the dynamic variations of the CPSs. Moreover, the convergence of the proposed algorithm is also discussed. To validate the advantages of the proposed methods, a numerical simulation is performed in the end.

Event-triggered fuzzy filtering for nonlinear networked systems with dynamic quantization and stochastic cyber attacks

In this article, the H_∞ filtering issue is considered for discrete-time nonlinear networked systems subject to event-triggered communication scheme, dynamic quantization, and stochastic cyber attacks. The considered nonlinear networked system is described by the Takagi-Sugeno (T-S) fuzzy model. The event-triggered policy and the dynamic quantizer will be considered to realize the effective use of the restricted network bandwidth resources. Moreover, a stochastic variable that satisfies the Bernoulli random binary distribution is employed to characterize the effects of stochastic cyber attacks. This paper focus on the design of full- and reduced-order event-triggered H_∞ filters and the dynamic parameter of the quantizer such that the filtering error system is stochastically stable and satisfies a predefined H_∞ filtering performance. The sufficient design conditions for the event-triggered H_∞ filters and the dynamic parameter of the quantizer are proposed based on linear matrix inequalities (LMIs). Finally, an example based on practical application will be used to verify the effectiveness of the presented design methods.

Convex Neural Networks Based Reinforcement Learning for Load Frequency Control under Denial of Service Attacks

With the increase in the complexity and informatization of power grids, new challenges, such as access to a large number of distributed energy sources and cyber attacks on power grid control systems, are brought to load-frequency control. As load-frequency control methods, both aggregated distributed energy sources (ADES) and artificial intelligence techniques provide flexible solution strategies to mitigate the frequency deviation of power grids. This paper proposes a load-frequency control strategy of ADES-based reinforcement learning under the consideration of reducing the impact of denial of service (DoS) attacks. Reinforcement learning is used to evaluate the pros and cons of the proposed frequency control strategy. The entire evaluation process is realized by the approximation of convex neural networks. Convex neural networks are used to convert the nonlinear optimization problems of reinforcement learning for long-term performance into the corresponding convex optimization problems. Thus, the local optimum is avoided, the optimization process of the strategy utility function is accelerated, and the response ability of controllers is improved. The stability of power grids and the convergence of convex neural networks under the proposed frequency control strategy are studied by constructing Lyapunov functions to obtain the sufficient conditions for the steady states of ADES and the weight convergence of actor–critic networks. The article uses the IEEE14, IEEE57, and IEEE118 bus testing systems to verify the proposed strategy. Our experimental results confirm that the proposed frequency control strategy can effectively reduce the frequency deviation of power grids under DoS attacks.

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.

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.

Observer-based state feedback H∞ control for offshore steel jacket structures under denial-of-service attacks

This article focuses on observer-based state feedback H_∞ control for a jacket structure against DoS attacks and external wave loads. First, a networked model of the structure is formulated as a switched delay system, in which DoS attacks and network-induced delays are considered simultaneously. A matching switched observer is developed for estimating states of the networked jacket structure system. Then, some new sufficient conditions are provided for the observer-based networked H_∞ controller for the resultant switched system. Finally, it is shown from several case studies that the provided mechanism can maintain desired performance of the jacket structure against attacks and wave loads. In addition, the developed control schemes can save the control cost significantly.

Co-Design of Dynamic Event-Triggered Communication Scheme and Resilient Observer-Based Control Under Aperiodic DoS Attacks

This article is concerned with the problem of observer-based dynamic event-triggered control for a networked control system (NCS) under a class of power-constrained denial-of-service (DoS) attacks that aim at impeding the network communication from time to time. First, by carefully modeling such DoS attacks as aperiodic pulse-width-modulated (PWM) jamming signals, a switching observer, adapting to the DoS attacks, is delicately constructed to deal with the unavailability of full-state information. Second, to economize the limited bandwidth resources, a dynamic event-triggered communication scheme is designed under the aperiodic DoS jamming attacks, whose duration and frequency are assumed to be restricted. Third, a switching system model with artificial state delay is formulated, which characterizes the effects of the aperiodic DoS attacks and event-triggered communication scheme in a unified framework. Then, the asymptotic stability analysis and controller/observer synthesis conditions of the resulting switching system are obtained by using a piecewise Lyapunov-Krasovskii functional approach. Furthermore, a co-design method of the dynamic triggering parameters, controller, and observer gains is presented. Finally, an example is employed to verify the effectiveness of the obtained results.

Event-Based Secure Leader-Following Consensus Control for Multiagent Systems With Multiple Cyber Attacks

This article concentrates on event-based secure leader-following consensus control for multiagent systems (MASs) with multiple cyber attacks, which contain replay attacks and denial-of-service (DoS) attacks. A new multiple cyber-attacks model is first built by considering replay attacks and DoS attacks simultaneously. Different from the existing researches on MASs with a fixed topological graph, the changes of communication topologies caused by DoS attacks are considered for MASs. Besides, an event-triggered mechanism is adopted for mitigating a load of network bandwidth by scheduling the transmission of sampled data. Then, an event-based consensus control protocol is first developed for MASs subjected to multiple cyber attacks. In view of this, by using the Lyapunov stability theory, sufficient conditions are obtained to ensure the mean-square exponential consensus of MASs. Furthermore, the event-based controller gain is derived by solving a set of linear matrix inequalities. Finally, an example is simulated for confirming the effectiveness of the theoretical results.

Observer-Based Event-Triggered Predictive Control for Networked Control Systems under DoS Attacks

This paper studies the problem of DoS attack defense based on static observer-based event-triggered predictive control in networked control systems (NCSs). First, under the conditions of limited network bandwidth resources and the incomplete observability of the state of the system, we introduce the event-triggered function to provide a discrete event-triggered transmission scheme for the observer. Then, we analyze denial-of-service (DoS) attacks that occur on the network transmission channel. Using the above-mentioned event-triggered scheme, a novel class of predictive control algorithms is designed on the control node to proactively save network bandwidth and compensate for DoS attacks, which ensures the stability of NCSs. Meanwhile, a closed-loop system with an observer-based event-triggered predictive control scheme for analysis is created. Through linear matrix inequality (LMI) and the Lyapunov function method, the design of the controller, observer and event-triggered matrices is established, and the stability of the scheme is analyzed. The results show that the proposed solution can effectively compensate DoS attacks and save network bandwidth resources by combining event-triggered mechanisms. Finally, a smart grid simulation example is employed to verify the feasibility and effectiveness of the scheme's defense against DoS attacks.

Distributed Event-Triggered Estimation Over Sensor Networks: A Survey

An event-triggered mechanism is of great efficiency in reducing unnecessary sensor samplings/transmissions and, thus, resource consumption such as sensor power and network bandwidth, which makes distributed event-triggered estimation a promising resource-aware solution for sensor network-based monitoring systems. This paper provides a survey of recent advances in distributed event-triggered estimation for dynamical systems operating over resource-constrained sensor networks. Local estimates of an unavailable state signal are calculated in a distributed and collaborative fashion based on only invoked sensor data. First, several fundamental issues associated with the design of distributed estimators are discussed in detail, such as estimator structures, communication constraints, and design methods. Second, an emphasis is laid on recent developments of distributed event-triggered estimation that has received considerable attention in the past few years. Then, the principle of an event-triggered mechanism is outlined and recent results in this subject are sorted out in accordance with different event-triggering conditions. Third, applications of distributed event-triggered estimation in practical sensor network-based monitoring systems including distributed grid-connected generation systems and target tracking systems are provided. Finally, several challenging issues worthy of further research are envisioned.