Resilient Control Problem Research Articles

Observer‐Based Attack Detection and Data‐Driven Resilient Control for Heterogeneous Nonlinear Nonaffine Mass Under FDI Attacks

ABSTRACTThe attack detection and data‐driven resilient control problem for heterogeneous nonlinear nonaffine multi‐agent systems (MASs) in the presence of intermittent false data injection (FDI) attacks is investigated in this article. First, an attack detection and isolation mechanism based on the distributed observer are designed to determine the attack signals and isolate the attacked communication links in time. Then, in order to solve the problem of the unknown system model, the dynamic linearization method is used to convert the nonlinear nonaffine MASs into an equivalent linear data model. Further, based on the linear data model, the data‐driven resilient controller with the attack isolation mechanism is designed to achieve the leader‐following tracking control objective. Finally, the simulation result illustrates the performance of the proposed scheme with comparisons.

Characterization of multi-channel denial-of-service

The embedded closed-loop in integrated systems can be compromised when attackers execute a successful malicious attack. Hence, over the past decade, interest in enhancing the safety of Cyber-Physical Systems (CPSs) has risen. This article examines the resilient control problem for CPSs with numerous transmission channels under Denial-of-Service (DoS). To begin with, a partial observer technique is developed in response to the Multi-Channel DoS (MCDoS) condition. Secondly, the changing frequency of MCDoS is characterized while maintaining the Input-to-State Stability (ISS) of the closed-loop system. Next, an event-based control strategy is developed to address Full-Scale DoS (FSDoS). Finally, the optimal control parameters are designed to make the system resilient against maximum MCDoS changing frequency.

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.

Sampled-based adaptive event-triggered resilient control for multiagent systems with hybrid cyber-attacks

The multiagent systems have shared broad application in many practical systems including unmanned aircraft clusters, intelligent robots, and intelligent transportation. However, many unexpected cyber-attacks may disturb or disrupt the normal communication of the agents, thus reducing the interacting efficiency of multiagent systems. Ever since the cyber-attacks have been proposed, the resilient control problem for multiagent systems has been intensively explored in light of the communication network growth. However, most of the consequences only focused on denial-of-service (DoS) attacks or deception attacks independently. Distinguished from the existing resilient control mechanisms, the current investigation represents the first attempt at designing an adaptive resilient controller for multiagent systems according to the sampled-based adaptive event-triggered manner, where denial-of-service (DoS) attacks and deception attacks are both considered. First, the hybrid cyber-attacks model and its impact on the closed-loop system are addressed. And then, an adaptive event-triggered strategy is proposed to reduce network resource consumption and ease the communication burden, where the designed adaptive law can automatically adjust the triggering threshold. Finally, the consensus state of multiagent systems is capable of achieving via a series of reasonable control rules formulated through Lyapunov functional approach despite suffering hybrid cyber-attacks. And a simulation example is given to substantiate the feasibility of the proposed method.

Fuzzy adaptive resilient control against unknown false data injection attacks for high-order nonlinear systems with actuator failures

An adaptive resilient control technique for high-order nonlinear systems with actuator failures is presented in this work. The unknown false data injection (FDI) attacks make all controlled system states unavailable. To counteract this negative impact of FDI attacks, a new coordinate transformation is constructed during the procedure of recursive design. Moreover, the effect of the actuator faults is completely eliminated by skillfully designing the controller and the adaptive laws. On the basis of the backstepping technology and the Nussbaum gain technique, an adaptive resilient control strategy is presented by using compromised state variables, which ensures all the signals of the closed-loop system is bounded when the sensor is attacked. Compared with the existing results, the proposed control approach not only solves the resilient control problem of high-order nonlinear systems whose powers are positive odd numbers, but also eliminates the assumption that the attack weights are positive. In the end, a simulation result is presented to verify the feasibility of the designed scheme.

DSC‐based finite‐time adaptive resilient control for a class of nonstrict‐feedback switched nonlinear systems with deception attacks

AbstractIn this paper, the finite‐time adaptive resilient control problem for a class of switched nonlinear systems with deception attacks by using the dynamic surface control (DSC) strategy is addressed. A more general class of uncertain nonstrict‐feedback switched nonlinear systems with the sensor and actuator deception attacks are considered. Deception attacks can damage sensor, control and switching data, which results in the switching signal and the finite‐time control gains unknown, meanwhile the conventional dynamic surface errors inacessible. To overcome the difficulty, first, the approximation technique of neural networks (NNs) is used to deal with the unknown nonlinear terms and compensate the sensor and actuator deception attacks. Secondly, a new coordinate transformation and a novel switched adaptive finite‐time filter are designed to mitigate the effects caused by the sensor attacks and avoid the problem of “explosion of complexity”. Thirdly, Nussbaum technology is exploited to handle the unknown finite‐time control gains problem caused by deception attacks. Then, by the proposed control strategy, the practical finite‐time stability analysis is presented. Finally, a simulation example is provided to validate the feasibility of the proposed control method.

Mixed H∞ and passive resilient control for discrete-time systems with Round-Robin protocol and deception attacks

This paper focuses on the mixed [Formula: see text] and passive resilient control problem for uncertain discrete-time networked control systems subject to time delays. The measurement output is considered to be quantized by the dynamic quantizer before transmission through an unreliable communication channel, where the deception attacks may occur randomly. To further reduce the communication burden, the Round-Robin (RR) protocol is adopted to schedule sensors in a predetermined transmission order. Meanwhile, a new performance index is developed to solve restriction of the same dimension of control output and the exogenous disturbance. The S-procedure combined with a two-step uncertainties elimination method is adopted to deal with the nonlinear product terms involving multiple uncertainties. Furthermore, a decoupling method is introduced for static output feedback controller design. Sufficient conditions of the resilient controller and the dynamic quantizer are provided, which can improve the robustness of resulting closed-loop system and guarantee its stochastic stability and the specified mixed [Formula: see text] and passivity performance index. Finally, an example is given to illustrate the feasibility of the theoretical results.

Cyber–physical systems subject to false data injections: A model predictive control framework for resilience operations

In this paper, the resilient control problem for constrained cyber–physical systems subject to false data injections is addressed. The core of the proposed approach consists in defining an ad-hoc versatile framework whose main feature consists in the ability of being geared to different classes of attacks. This is formally achieved by resorting to the receding horizon philosophy that is fully exploited for detection, countermeasures and control purposes. In particular, set-theoretic model predictive arguments are combined with the perturbation analysis and sequential quadratic programming to reduce as much as possible the occurrence of refresh procedures on the communication network when resilient command actions are no longer available. Further, one of its main merits consists in the dismissal of constructive assumptions existing in recent competitors. In this respect, the framework is customized for covert attacks by specifying actuation/detection phases and proving feasibility and closed-loop stability properties.

Resilient Consensus for Networked Lagrangian Systems With Disturbances and DoS Attacks

In this article, we consider the resilient consensus control problem of multiple Euler–Lagrange systems in the presence of external disturbances and denial-of-service (DoS) attacks, which have bounded duration and frequency. Unlike the most existing methods for linear multiagent systems, the strong nonlinearity, and unknown disturbances of multiple Euler–Lagrange systems make the resilient control problem under DoS attacks be more challenging and still not well explored. To this end, an adaptive control scheme is first proposed with a robust sliding mode-like term to handle the unknown bounded external disturbances. In addition, an auxiliary system is designed by using the sampled neighboring information to achieve resilience to DoS attacks. Sufficient conditions are presented by using Lyapunov and small-gain approaches. Finally, simulation results are given to verify the effectiveness of the proposed resilient consensus control approach.

Neural-network-based output feedback adaptive resilient control for stochastic nonlinear systems subject to sensor and actuator attacks

This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples.

Resilient Control for Multiagent Systems With a Sampled-Data Model Against DoS Attacks

To reduce the computational burden and resist the denial-of-service (DoS) attacks, a resilient distributed sampled-data control scheme is proposed for multiagent systems. The agent states are sampled periodically by the sensors. DoS attacks disrupt the data communication from transmitters to controllers randomly or periodically with a limited duration time. Information on DoS attacks can be obtained by introducing novel logic processors embedded in corresponding controllers. Next, the problem of resilient control can be converted into one concerned with the upper and lower bound of the sampling interval of an aperiodic sampled-data control system. Some sufficient criteria for developing resilient distributed controllers are derived using the novel looped Lyapunov functional approach and the free-matrix-based inequality method. Finally, two illustrative examples, unmanned aerial vehicles and the two-mass-spring systems, are provided to demonstrate the efficiency of the proposed resilient distributed sampled-data control protocols against the DoS attacks.

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.

Resilient control for T-S fuzzy systems with multiple transmission channels under asynchronous denial-of-Service attacks

This paper investigates the resilient control problem of the T-S fuzzy system with multiple transmission channels under asynchronous denial-of-service (DoS) attacks. A part of the transmission channels are chosen as ones corresponding to the premise variables of the T-S fuzzy system. The considered asynchronous DoS attacks occur on the sensor-to-controller transmission channels, which may DoS attacks cause the system state to be unstable. The stability of the T-S fuzzy system under random DoS attack mode is considered, including the influence of the premise variables attacked. A class of parallel distributed compensation controllers are proposed, which adopts the strategy of discarding the attacked channels information. By using a piecewise Lyapunov function method and a class of linear matrix inequalities, the system stability sufficient conditions related to the duration and frequency of DoS attacks on the transmission channels are presented. Compared with the existing method, the more general relationship between premise variables and transmission channels are considered in this paper. Finally, a numerical example is used to illustrate the effectiveness of the proposed scheme.

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults.

This article considers neural network (NN)-based adaptive finite-time resilient control problem for a class of nonlinear time-delay systems with unknown fault data injection attacks and actuator faults. In the procedure of recursive design, a coordinate transformation and a modified fractional-order command-filtered (FOCF) backstepping technique are incorporated to handle the unknown false data injection attacks and overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for virtual control laws. The theoretical analysis proves that the developed resilient controller can guarantee the finite-time stability of the closed-loop system (CLS) and the stabilization errors converge to an adjustable neighborhood of zero. The foremost contributions of this work include: 1) by means of a modified FOCF technique, the adaptive resilient control problem of more general nonlinear time-delay systems with unknown cyberattacks and actuator faults is first considered; 2) different from most of the existing results, the commonly used assumptions on the sign of attack weight and prior knowledge of actuator faults are fully removed in this article. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.

Attack estimation–based resilient control for cyber-physical power systems

This article is concerned with the resilient control problem for cyber-physical power systems. The main difference from the existing works lies in that the proposed resilient control scheme focuses on robust control in consideration of the attack reconstruction. First, power systems under false data injection attacks are modeled as cyber-physical power systems through the characteristics of power systems and attacks. Second, we design a robust adaptive attack estimation observer with a prescribed [Formula: see text] performance index to reconstruct the attack signals. Sufficient conditions for the design of the observer are formulated into matrix inequalities by choosing an appropriate adaptive law for online learning. Third, on the basis of attack estimation, a resilient control strategy by dynamic output feedback is proposed, which avoids the difficulty in designing state feedback resilient control based on the observer. Meanwhile, the attack estimation observer and output feedback resilient control are designed separately, and their performance is considered respectively, which simplifies the design process. Finally, a power system with three generators and six buses is used as an example to verify the feasibility of attack estimation and resilient control scheme.

Quantized output feedback resilient control of uncertain systems under hybrid cyber attacks

SummaryThis article investigates the output feedback resilient control problem of an uncertain system with two quantized signals under hybrid cyber attacks. First, for uncertain systems, the output signal of the system and the output signal of the controller are quantized once by the quantizers before they are passed to the controller and the system respectively, thus affecting the signal with quantization error terms. At the same time, The adverse effects of hybrid cyber attacks, including denial‐of‐service attacks and deception attacks, are also taken into account during the signaling process. In this article, a guaranteed cost resilient controller design method is proposed for the above situation so that the closed‐loop system can be guaranteed to be stable or to meet the given performance index. In addition, by solving the given set of linear matrix inequalities, the design conditions for the relevant parameters required for the resilient controller can be obtained. Finally, the validity of the design method can be verified by simulation.

Resilient control of double-integrator stochastic multi-agent systems under denial-of-service attacks

The resilient control problem of double-integrator stochastic multi-agent systems under denial-of-service (DoS) attack is studied in this paper. We neutralize the effects of DoS attacks by introducing a hidden layer that has no physical significance. Compared with previous works, this method requires less computation, does not require a high degree of connectivity of communication topology, and does not need to know any information about attacks, such as attack frequency and attack duration. It is proved that the introduction of hidden layer will not affect the consensus of the original system and can improve its robustness. Besides, we also verify the effectiveness of event-triggered mechanism for systems with the hidden layer.

Resilient Model-Free Adaptive Iterative Learning Control for Nonlinear Systems Under Periodic DoS Attacks via a Fading Channel

This article studies the resilient control problem for a class of unknown nonlinear systems with fading measurements under malicious denial-of-service (DoS) attacks. The system output is assumed to be transmitted through a fading channel, where the fading phenomenon is described by a Rice fading model. The strategy of the attacker is to periodically interfere with the networked channels to reduce the success rate of data transmissions. First, a dynamic linearization method along the iteration domain is introduced to convert the nonlinear system into an equivalent data-related model. Then, a model-free adaptive iterative learning control (MFAILC) scheme is presented, which is independent of model information. The convergence of the MFAILC scheme is deduced theoretically and the influence of DoS attacks and stochastic fading phenomenon on system stability are also analyzed. Finally, the effectiveness of the design is verified by a numerical simulation and a trajectory tracking example of wheeled mobile robots (WMRs).

Resilient Control of Networked Switched Systems Against DoS Attack

This article investigates the resilient control problem of networked switched systems against denial-of-service attack. The resilient control strategies include the security game of the network layer and the optimal control for the physical layer. First, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{N}$</tex-math></inline-formula> -coalition noncooperative game is utilized to characterize the network security game between an intrusion detection system and the attacker, where their strategy sets are obtained from the Nash equilibrium. Then, by the dynamic programming principles, the optimization strategy is developed to obtain the optimal controller gains for the subsystems. Meanwhile, for ensuring the optimality of the switched system, a genetic algorithm is adopted to search for the optimal switching signal. Finally, the proposed methods are applied to a networked continuous stirred tank reactors system, which demonstrates its effectiveness.

Adaptive resilient control design for nonlinear time‐delay systems against unknown state‐dependent deception attacks

AbstractThis article investigates the adaptive resilient control problem for a class of nonlinear time‐delay systems (TDSs) with unknown state‐dependent deception attacks. For the purpose of overcoming the issue of “explosion of complexity” existing in the traditional backstepping control design and improving filter performance, an improved fractional‐order command filtered backstepping technique is adopted to obtain the filtered signals of the virtual control laws. Furthermore, the corresponding auxiliary system and attack compensator are constructed to compensate the effects of input delay and unknown sensor deception attacks. Stability results prove that all the signals in the closed‐loop systems are semi‐globally uniformly ultimately bounded (SGUUB) by using the proposed adaptive resilient controller. Finally, two simulation examples are provided to show the effectiveness of the developed control method.