Resilient distributed control in the presence of misbehaving agents in networked control systems

Encryption-decryption-based distributed fault-tolerant consensus tracking control for multi-agent systems

This study investigates fault-tolerant consensus tracking for discrete-time multi-agent systems (MASs) subject to external eavesdropping threats and additive actuator faults. First, actuator faults are modeled by difference equations, and decentralized observers are constructed to estimate actuator faults as well as system states. To offset fault-induced effects, ensure secure communication, and alleviate communication congestion, neighboring encrypted state information based on the encryption-decryption strategy (EDS) and estimated fault are integrated into a distributed active fault-tolerant consensus tracking control (FCTC) protocol. Through the properties of compatible norms, criteria for the controller, observer, and dynamic encryption key in EDS are derived to achieve leader-following consensus (LFC) of MASs with bias and drift actuator faults. Simulation results confirm the validity of the encryption-decryption-based distributed FCTC strategy.

A hierarchical framework for distributed resilient control of islanded AC microgrids under false data injection attacks

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation.

Resilient Output Formation Containment of Heterogeneous Multigroup Systems Against Unbounded Attacks

This article studies the attack-resilient output formation containment of general high-order heterogeneous multigroup systems under unknown unbounded attacks. The multigroup systems consist of cooperative heterogeneous leaders and followers, as well as adversarial attackers. Potential attacks on the multigroup systems consist of unknown unbounded signals generated from the attackers and injected distributedly into the actuator, the local state feedback, and communication channels of each agent to destabilize the synchronization dynamics. In contrast to the existing literature dealing with bounded disturbances, noises, and faults, which are caused unintentionally, this article studies the unknown unbounded attacks that are intentionally designed to jeopardize the system. The control objective is to make each follower's output trajectory reach the uniformly ultimately bounded (UUB) convergence to the time-varying formation reference, that is, the centroid of the multiple leaders' output trajectories while keeping a predefined time-varying offset with respect to it. Fully distributed attack-resilient control protocols are proposed, without requiring any global information. Lyapunov techniques are used to analyze the stability and UUB synchronization result of the overall closed-loop system. Comparative simulation examples are given to validate the proposed results.

A Localized Event-Driven Resilient Mechanism for Cooperative Microgrid Against Data Integrity Attacks

This article investigates the impact of data integrity attacks (DIAs) on cooperative economic dispatch of distributed generators (DGs) in an ac microgrid. To establish resiliency against such attacks and ensure optimal operation, a localized event-driven attack-resilient scheme is proposed. Most of the existing works examine neighboring information to infer the presence of DIAs, where the detection is limited to events such as multiple link failures. Two kinds of DIAs are considered in this article-namely, fault and random attacks, which are segregated based on the final values of consensus updates. First, to improve the robustness of the detection theory, a localized resilient control update is designed by modeling each DG with a reference incremental cost. Second, event-driven control signal is generated for the local incremental cost and held upon the detection of attacks, to prevent malicious data from propagating to the neighboring nodes. The proposed strategy acts immediately upon the detection of DIA to ensure maximization in the economic profit. Furthermore, the proposed detection approach is theoretically verified and validated using simulation conditions.

Resilient control of multi-microgrids against false data injection attack

A multi-microgrid system, including several microgrids and distributed energy resources, is always threatened by numbers of faults and attacks as a consequence of which malfunctioning can occur on a large scale. Thus, minimizing the effects of such disruptions is of paramount importance. This paper addresses the problem of mitigating a multi-microgrid system that faces false data injection and replay attacks by considering the multi-microgrid as a multi-agent system in which each microgrid as an agent represents a node in a weighted directed graph. The problem of consensus among normal agents is studied when microgrids and their communications are attacked. The malicious agents become isolated with the help of Weighted Mean Subsequence Reduced (W-MSR) algorithms in which all normal agents neglect the extreme values received from their neighbors. The proposed controller is able to maintain the system's desired performance when false data is injected into the system, or valid data is received with time-delays. Finally, numerical examples and simulation results are provided.

Resilient Tracking Control of Networked Control Systems Under Cyber Attacks

This article is concerned with the resilient tracking control of a networked control system under cyber attacks. The attacker is an active adversary whose aim is to severely degrade the tracking performance of the system by launching deception attacks on the sensor-to-controller communication channels and denial-of-service attacks on the controller-to-plant channels, respectively. First, a concept of resilient set-membership tracking control is presented, through which the system's true state is guaranteed to reside in a bounding ellipsoidal set of the reference state regardless of the existence of attacks and unknown-but-bounded (UBB) noises. Second, in the case that full information of the system's state is not implicitly trusted in the presence of attacks, a resilient set-membership estimation strategy is provided to secure the state estimates against the deception attacks. Furthermore, based on a recursive computation of a reference state ellipsoid and confidence state estimation ellipsoids, a convex optimization algorithm in terms of recursive linear matrix inequalities is proposed to obtain the gain parameters for both the desired resilient state estimator and the tracking controller. Finally, the effectiveness of the proposed method is illustrated through an Internet-based three-tank system.

A Secure Adaptive Control for Cooperative Driving of Autonomous Connected Vehicles in the Presence of Heterogeneous Communication Delays and Cyberattacks

The development of autonomous connected vehicles, moving as a platoon formation, is a hot topic in the intelligent transportation system (ITS) research field. It is on the road and deployment requires the design of distributed control strategies, leveraging secure vehicular ad-hoc networks (VANETs). Indeed, wireless communication networks can be affected by various security vulnerabilities and cyberattacks leading to dangerous implications for cooperative driving safety. Control design can play an important role in providing both resilience and robustness to vehicular networks. To this aim, in this article, we tackle and solve the problem of cyber-secure tracking for a platoon that moves as a cohesive formation along a single lane undergoing different kinds of cyber threats, that is, application layer and network layer attacks, as well as network induced phenomena. The proposed cooperative approach leverages an adaptive synchronization-based control algorithm that embeds a distributed mitigation mechanism of malicious information. The closed-loop stability is analytically demonstrated by using the Lyapunov-Krasovskii theory, while its effectiveness in coping with the most relevant type of cyber threats is disclosed by using PLEXE, a high fidelity simulator which provides a realistic simulation of cooperative driving systems.

Resilient asynchronous state estimation of Markov switching neural networks: A hierarchical structure approach

This paper deals with the issue of resilient asynchronous state estimation of discrete-time Markov switching neural networks. Randomly occurring signal quantization and packet dropout are involved in the imperfect measured output. The asynchronous switching phenomena appear among Markov switching neural networks, quantizer modes and filter modes, which are modeled by a hierarchical structure approach. By resorting to the hierarchical structure approach and Lyapunov functional technique, sufficient conditions are achieved, and asynchronous resilient filters are derived such that filtering error dynamic is stochastically stable. Finally, two examples are included to verify the validity of the proposed method.

Resilient and Robust Synchronization of Multiagent Systems Under Attacks on Sensors and Actuators

Resilient and robust distributed control protocols for multiagent systems under attacks on sensors and actuators are designed. A distributed H_∞ control protocol is designed to attenuate the disturbance or attack effects. However, the H_∞ controller is too conservative in the presence of attacks. Therefore, it is augmented with a distributed adaptive compensator to mitigate the adverse effects of attacks. The proposed controller can make the synchronization error arbitrarily small in the presence of faulty attacks, and satisfy global L₂ -gain performance in the presence of malicious attacks or disturbances. A significant advantage of the proposed method is that it requires no restriction on the number of agents or agents' neighbors under attacks on sensors and/or actuators, and it recovers even compromised agents under attacks on actuators. Simulation examples verify the effectiveness of the proposed method.

Resilient Autonomous Control of Distributed Multiagent Systems in Contested Environments

An autonomous and resilient controller is proposed for leader-follower multiagent systems under uncertainties and cyber-physical attacks. The leader is assumed nonautonomous with a nonzero control input, which allows changing the team behavior or mission in response to the environmental changes. A resilient learning-based control protocol is presented to find optimal solutions to the synchronization problem in the presence of attacks and system dynamic uncertainties. An observer-based distributed H_∞ controller is first designed to prevent propagating the effects of attacks on sensors and actuators throughout the network, as well as to attenuate the effect of these attacks on the compromised agent itself. Nonhomogeneous game algebraic Riccati equations are derived to solve the H_∞ optimal synchronization problem and off-policy reinforcement learning (RL) is utilized to learn their solution without requiring any knowledge of the agent's dynamics. A trust-confidence-based distributed control protocol is then proposed to mitigate attacks that hijack the entire node and attacks on communication links. A confidence value is defined for each agent based solely on its local evidence. The proposed resilient RL algorithm employs the confidence value of each agent to indicate the trustworthiness of its own information and broadcast it to its neighbors to put weights on the data they receive from it during and after learning. If the confidence value of an agent is low, it employs a trust mechanism to identify compromised agents and remove the data it receives from them from the learning process. The simulation results are provided to show the effectiveness of the proposed approach.

Consensus of Heterogeneous Linear Multiagent Systems Subject to Aperiodic Sampled-Data and DoS Attack

In this paper, the robust output consensus problem for a class of heterogeneous linear multiagent systems (MASs) in presence of aperiodic sampling and random deny-of-service (DoS) attack is investigated. A novel distributed output-feedback control strategy is proposed so that the controlled MAS achieves the objective of output consensus in spite of aperiodic sampling and DoS attack. By assuming that the sampling process is nonuniform and the consecutive attack duration is upper bounded, the closed-loop control system is first described as a discrete-time switched stochastic delay system. Some sufficient conditions are then obtained for the solvability of the secure consensus problem. Furthermore, a constructive design procedure for the proposed controller is then presented. Finally, a simulation example is introduced to illustrate the effectiveness of controller design.

Dissipativity-Based Resilient Filtering of Periodic Markovian Jump Neural Networks With Quantized Measurements

The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.

Consensus Tracking of Heterogeneous Discrete-Time Networked Multiagent Systems Based on the Networked Predictive Control Scheme

This paper studies the problem of consensus tracking for heterogeneous discrete-time networked multiagent systems (NMASs) with network-induced communication delay. By introducing the networked predictive control scheme, novel consensus tracking protocols are designed to ensure that states of the NMASs follow the external reference signal. One of the agents is defined as the leader agent, all the other agents are defined as the followers, and the external reference signal is always known by the leader agent, both cases where the external reference signal is available and unavailable to the follower agents are discussed in this paper. The necessary and sufficient conditions for the novel consensus tracking protocols are proposed based on algebraic and graph theory. Numerical examples are given to validate the theoretical results.

Distributed Secure Coordinated Control for Multiagent Systems Under Strategic Attacks

This paper studies a distributed secure consensus tracking control problem for multiagent systems subject to strategic cyber attacks modeled by a random Markov process. A hybrid stochastic secure control framework is established for designing a distributed secure control law such that mean-square exponential consensus tracking is achieved. A connectivity restoration mechanism is considered and the properties on attack frequency and attack length rate are investigated, respectively. Based on the solutions of an algebraic Riccati equation and an algebraic Riccati inequality, a procedure to select the control gains is provided and stability analysis is studied by using Lyapunov's method.. The effect of strategic attacks on discrete-time systems is also investigated. Finally, numerical examples are provided to illustrate the effectiveness of theoretical analysis.

Robust Filtering for a Class of Networked Nonlinear Systems With Switching Communication Channels

This paper is concerned with the problem of robust filter design for a class of discrete-time networked nonlinear systems. The Takagi-Sugeno fuzzy model is employed to represent the underlying nonlinear dynamics. A multi-channel communication scheme that involves a channel switching phenomenon described by a Markov chain is proposed for data transmission. Two typical communication imperfections, network-induced time-varying delays and packet dropouts are considered in each channel. The objective of this paper is to design an admissible filter such that the filter error system is stochastically stable and ensures a prescribed disturbance attenuation level bound. Based on the Lyapunov-Krasovskii functional method and matrix inequality techniques, sufficient conditions on the existence of the desired filter are obtained. A numerical example is provided to illustrate the effectiveness of the proposed design approach.

Norm Monitoring Under Partial Action Observability

In the context of using norms for controlling multiagent systems, a vitally important question that has not yet been addressed in the literature is the development of mechanisms for monitoring norm compliance under partial action observability. This paper proposes the reconstruction of unobserved actions to tackle this problem. In particular, we formalize the problem of reconstructing unobserved actions, and propose an information model and algorithms for monitoring norms under partial action observability using two different processes for reconstructing unobserved actions. Our evaluation shows that reconstructing unobserved actions increases significantly the number of norm violations and fulfilments detected.

Consensus Under Bounded Noise in Discrete Network Systems: An Algorithm With Fast Convergence and High Accuracy

Most existing works investigate consensus with noise following a certain distribution, e.g., Gaussian distribution, with fixed expectation and variance, which may not be satisfied in practical applications. This paper investigates the discrete system consensus under bounded noise, which is important and practical problem. We first provide necessary and sufficient conditions for the convergence of consensus under bounded noise. To be more general, we derive an analytical bound to show the max-min difference between the nodes' states when the general consensus algorithm converges to a stable state. Then, a novel consensus algorithm, fast consensus under bounded noise (FCBN), is proposed to eliminate the accumulative error caused by the bounded noise. It is proved that FCBN has a faster convergence speed and a higher consensus accuracy than general consensus algorithms. Extensive simulations demonstrate the effectiveness of the proposed algorithm.

Formation Control With Size Scaling Via a Complex Laplacian-Based Approach

We consider the control of formations of a leader-follower network, where the objective is to steer a team of multiple mobile agents into a formation of variable size. We assume that the shape description of the formation is known to all the agents, which is captured by a complex-valued Laplacian associated with the sensing graph, but the size scaling of the formation is not known or only known to two agents, called the leaders in the network. A distributed linear control strategy is developed in this paper such that the agents converge to the desired formation shape, for which the size of the formation is determined by the two leaders. Moreover, in order to make all agents in a formation move with a common velocity, the distributed control law also incorporates a velocity consensus component, which is implemented with the help of a communication network that may, in general, be of different topology from the sensing graph. Both the setup of single-integrator kinematics and the one of double-integrator dynamics are addressed in the same framework except that the acceleration control in the double-integrator setup has an extra damping term.

A Survey on Security and Privacy in Emerging Sensor Networks: From Viewpoint of Close-Loop

Nowadays, as the next generation sensor networks, Cyber-Physical Systems (CPSs) refer to the complex networked systems that have both physical subsystems and cyber components, and the information flow between different subsystems and components is across a communication network, which forms a closed-loop. New generation sensor networks are found in a growing number of applications and have received increasing attention from many inter-disciplines. Opportunities and challenges in the design, analysis, verification and validation of sensor networks co-exists, among which security and privacy are two important ingredients. This paper presents a survey on some recent results in the security and privacy aspects of emerging sensor networks from the viewpoint of the closed-loop. This paper also discusses several future research directions under these two umbrellas.

Distributed Synchronization Control of Multiagent Systems With Unknown Nonlinearities

This paper revisits the distributed adaptive control problem for synchronization of multiagent systems where the dynamics of the agents are nonlinear, nonidentical, unknown, and subject to external disturbances. Two communication topologies, represented, respectively, by a fixed strongly-connected directed graph and by a switching connected undirected graph, are considered. Under both of these communication topologies, we use distributed neural networks to approximate the uncertain dynamics. Decentralized adaptive control protocols are then constructed to solve the cooperative tracker problem, the problem of synchronization of all follower agents to a leader agent. In particular, we show that, under the proposed decentralized control protocols, the synchronization errors are ultimately bounded, and their ultimate bounds can be reduced arbitrarily by choosing the control parameter appropriately. Simulation study verifies the effectiveness of our proposed protocols.

Consensus Control With Failure--Wait or Abandon?

This paper introduces and solves a decision-making problem under the context of consensus control with failure. We study an optimal consensus control problem in which n autonomous agents try to arrive at a target at the same time. One of the agents suddenly fails and the rest n - 1 agents can either wait or abandon the failed agent. If they wait, they must slow down and delay the consensus time. If they abandon the failed agent, they can reach consensus earlier at the cost of losing one agent at consensus. This cost is an added delay to the consensus time. The decision problem is to decide whether to wait or abandon and, if abandon, when? To solve this problem, we derive analytical expressions and establish structural properties for target distance functions. We use numerical examples and simulation examples to demonstrate the applications of the derived formulas and results.

Rigidity-Preserving Team Partitions in Multiagent Networks

Motivated by the strong influence network rigidity has on collaborative systems, in this paper, we consider the problem of partitioning a multiagent network into two sub-teams, a bipartition, such that the resulting sub-teams are topologically rigid. In this direction, we determine the existence conditions for rigidity-preserving bipartitions, and provide an iterative algorithm that identifies such partitions in polynomial time. In particular, the relationship between rigid graph partitions and the previously identified Z-link edge structure is given, yielding a feasible direction for graph search. Adapting a supergraph search mechanism, we then detail a methodology for discerning graphs cuts that represent valid rigid bipartitions. Next, we extend our methods to a decentralized context by exploiting leader election and an improved graph search to evaluate feasible cuts using only local agent-to-agent communication. Finally, full algorithm details and pseudocode are provided, together with simulation results that verify correctness and demonstrate complexity.