Cooperative control for cyber-physical multi-agent networked control systems with unknown false data-injection and replay cyber-attacks

Secure consensus for PDEs-based multiagent systems under DoS attacks via boundary control approach

This paper focuses on secure consensus for leader-following multiagent systems (MASs) modeled by partial differential equations (PDEs) under denial of service (DoS) attacks. To mitigate the negative effects of DoS attacks, which can paralyze communication and cause agents to fail to receive valid control inputs, a buffer region is established in the communication channels among agents to temporarily store messages from neighbors. Additionally, since the states of the leader and followers are not always measurable, observers are used to estimate these states. To address these challenges, this paper proposes two boundary controllers to ensure leader-following consensus in both measurable and unmeasurable states. One controller is based on original boundary information, while the other utilizes observation information from both the leader and followers. To the best of our knowledge, this is the first attempt to use buffers to solve a class of PDEs-based MASs under DoS attacks. Furthermore, the boundary control approach has the potential to significantly reduce the number of actuators required, thereby lowering control costs. Finally, we present two numerical examples to validate the feasibility of the proposed methods.

Switching-like event-triggered control of uncertain NCSs under delay distribution and DoS attacks

The paper addresses the switching-like event-triggered control for uncertain networked control systems with time-varying delay under DoS attacks. First of all, to reduce the communication burden, a switching-like event-triggered mechanism is designed to automatically select the trigger condition according to whether the system is under DoS attacks, which have the advantage of reducing the number of data packets transmitted. Secondly, unlike the traditional assumption of time-varying delay, here it satisfies the condition that the probability is known, and combines the networked control systems to propose a novel time-delay system model, which can obtain a larger upper bound on the delay. Then, by using both the Lyapunov functional method and linear matrix inequality technique, we obtain sufficient conditions of uncertain networked control systems to achieve exponentially stable in the mean square sense. Furthermore, under the common limitations of the maximum continuous packet losses caused by the DoS attacks and delay, the stability criterion is derived, which can be used to estimate the communication parameters and security controller gain. Finally, through two simulation examples, the larger upper bound of time delay, less trigger times, faster convergence rate are obtained, which verify the validity of our theoretical analysis.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.

Observer-based model predictive control for uncertain NCS subject to hybrid attacks via interval type-2 T-S fuzzy model

In this study, an observer-based model predictive control (MPC) algorithm is addressed for an uncertain discrete-time nonlinear networked control system (NCS) subject to hybrid malicious attacks by using interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy theory. Hybrid malicious attacks, including two typical attacks, i.e., denial-of-service (DoS) attacks and false data injection (FDI) attacks, are considered in the communication networks. Under DoS attacks, the control signals will be interfered, which cause the degradation of signal-to-interference-plus-noise ratio, then lead to packets loss. Under FDI attacks, the false signals are injected and output signals are modified so that the system performance is deteriorated. For the NCS subject to hybrid attacks, a secure observer that can resist FDI attacks is devised and a fuzzy MPC algorithm that can solve the controller gains is proposed. Besides, by updating the bound of augmented estimation error, the recursive feasibility can be guaranteed. Finally, illustrative examples are given to show the effectiveness of proposed scheme.

Cloud-based predictive formation control of networked multi-agent system and its application to air bearing spacecraft simulators

This paper studies a class of networked multi-agent systems with communication delays. A centralized cloud predictive control protocol is proposed to realize formation control of multiple agents and especially the predictive method is introduced to actively compensate for the delays in the network. The analysis of closed-loop networked multi-agent systems provides the necessary and sufficient condition of stability and consensus. Finally, the proposed cloud-based predictive formation control scheme is verified by its application to 3-degree-of-freedom air-bearing spacecraft simulators platform. The results show that the scheme can effectively compensate for the delays in the forward channel and the feedback channel and can be applied to the networked multi-agent systems well.

Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks

In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear minimum variance criterion is presented based on compressed measurement data. It has the same estimation accuracy as and lower computational cost than that based on uncompressed measurement data. When the attack rates and noise variances of the stochastic deception attack signals are unknown, a correlation function method is employed to identify them. Then, a distributed self-tuning filter is obtained by substituting the identified results into the distributed optimal filtering algorithm. The convergence of the presented algorithms is analyzed. A simulation example verifies the effectiveness of the proposed algorithms.

A latent feature oriented dictionary learning method for closed-loop process monitoring

Industrial cyber-physical system (ICPS), by its powerful computing, communication, precise control and remote operation functions, has become the mainstream of modern industrial process. The observed variables of the closed-loop process in ICPS are subject to the degradation of equipment and other factors, resulting in exhibiting a stationary/nonstationary mixture feature and dynamic feature. Moreover, due to the frequent change of working conditions in the closed-loop process, the traditional open-loop process monitoring method always triggers false alarms, which will impose a negative impact on the safety and trustworthiness of ICPS. Therefore, for the closed-loop process in ICPS, a latent feature oriented dictionary learning (LFDL) method is proposed, which realizes the precise separation of latent features of raw data through three stages. First, closed-loop process variables are separated into stationary and nonstationary variables to mine the local information spatially. Then, from the temporal viewpoint, the static and dynamic features were extracted for stationary and nonstationary variables on the basis of the slow feature analysis method and cointegration analysis for local monitoring. Finally, the global monitoring results are obtained by utilizing the dictionary learning method to fuse respectively the local monitoring results of the static and dynamic features. Since the proposed method has taken the feature of the close-loop process from temporal and spatial viewpoints simultaneously, it can distinguish the normal change of operating conditions and actual faults accurately. Extensive experiments including the three-phase flow, the Tennessee Eastman process and an industrial roasting process are conducted to demonstrate the feasibility and effectiveness of the proposed method.

Dynamic event-based finite-horizon H∞ secure consensus control of a class of nonlinear multi-agent systems

In this paper, we investigate the H_∞ consensus control issue for nonlinear multi-agent systems (MASs) subject to multiple attacks over a finite time interval. A novel and comprehensive model to characterize the multiple attacks is presented that includes denial-of-service (DoS) attacks, scaling attacks and replay attacks. With the hope of easing the communication burdens, we implement a dynamic event-triggered scheme to schedule the process of data sharing among the individual subsystems, which helps judge if the collected data should be shared to neighboring agents for control input update. The aim of the proposed problem is to develop an output feedback strategy to meet the desired H_∞ consensus performance despite the existence of multiple attacks. Some conditions are presented for the solvability of the investigated problem, and the feedback gains are obtained via certain convex optimization algorithms. The proposed theoretical result is finally demonstrated by virtue of two illustrative simulation examples.

Event-Triggered Security Consensus for Multi-Agent Systems with Markov Switching Topologies under DoS Attacks

This paper studies secure consensus control for multi-agent systems subject to denial-of-service (DoS) attacks. The DoS attacks cause changes in topologies, which will destroy the channels of communication and result in network paralysis. Unlike the existing publications with Markov switching, this paper mainly studies the topological structure changes of the subsystem models after DoS attacks. To ensure the consensus of systems, this paper designs an event triggered to reduce the use of the controller and decrease the influence of channel breaks off caused by DoS attacks. On this basis, different Lyapunov functions are established in each period of attack. Then, stochastic and Lyapunov stable theory is used to form the consensus criteria. Moreover, Zeno behavior is excluded by theoretical analysis. Finally, the simulation experiment proves the effectiveness of the proposed protocol.

Security correction control of stochastic cyber-physical systems subject to false data injection attacks with heterogeneous effects

In this paper, the interconnected observer intervention-based security correction control idea is proposed for stochastic cyber-physical systems (CPSs) subjected to false data injection attacks (FDIAs). The FDIAs are injected into the controller-to-actuator channel by the adversary via wireless transmission. In particular, the FDIAs with heterogeneous effects are constructed, which consist of periodic attacks with unknown parameters and bias injection attacks with asymptotic convergence property. A novel interconnected adaptive observer structure is designed to online estimate the heterogeneous attack effects. The security correction control scheme with resilience is presented by integrating interconnected adaptive observer and robust technology. It is demonstrated that the impaired state signals can be corrected and desired security performance can be guaranteed for stochastic CPSs under FDIAs with heterogeneous effects. Finally, two simulation verifications, including a F-16 longitudinal dynamics system controlled by network, are established to verify the validity and feasibility for the presented strategy.