With the increasingly integrated nature of networked control systems (NCSs), security has become a challenging issue for their widespread deployment. Although resilient control methods against various attacks have been reported, the analysis and design of defense mechanisms for NCSs still require fresh efforts. To this end, this article is concerned with the security control of a class of NCSs vulnerable to smart false data injection (FDI) attacks. Specifically, the scenario of output tracking of NCSs is considered, where the communication between sensors and controllers, as well as between controllers and actuators, is compromised by sophisticated malicious adversaries. To enhance security, peer-to-peer (P2P) networks with blockchain technologies are utilized instead of traditional communication patterns to transmit measurement and control signals. Unlike previous work, this work carefully designs an optimal blockchain consensus policy by perceiving the performance of NCSs and develops a resilient dynamic output tracking controller based on this policy. The formulation of the consensus policy is derived from a game-theoretic framework that models the interaction between the blockchain and the malicious adversary, enabling deep integration of blockchain technology with NCSs. With the proposed approach, the adverse effects of malicious FDI attacks can be greatly mitigated by balancing energy consumption and tracking performance. Finally, the applicability of the proposed security control strategy is verified in a real-world power system.

This article addresses the cooperative tracking control problem of linear discrete-time heterogeneous networked multiagent systems suffering from random communication constraints. The communication constraints, including random network delays and packet losses, exist in the forward and feedback channels of each agent as well as the channels between agents. A dual-prediction-based cooperative control scheme with correction is proposed. Specifically, a dual-prediction generator is developed to compensate actively for the adverse effects of random communication constraints in the three communication channels. Besides, a correction item is introduced to ensure that the predicted value tends to the actual value. A novel cooperative tracking control law with proportional and integral action is designed for each agent. A sufficient condition is obtained for the derived closed-loop system to maintain stability. Comparative experimental results on a networked three-motor system demonstrate the effectiveness of the proposed method.

In large-scale networks, communication links between nodes are easily injected with false data by adversaries. This paper proposes a novel security defense strategy from the perspective of attack detection scheduling to ensure the security of the network. Based on the proposed strategy, each sensor can directly exclude suspicious sensors from its neighboring set. First, the problem of selecting suspicious sensors is formulated as a combinatorial optimization problem, which is non-deterministic polynomial-time hard (NP-hard). To solve this problem, the original function is transformed into a submodular function. Then, we propose an attack detection scheduling algorithm based on the sequential submodular optimization theory, which incorporates expert problem to better utilize historical information to guide the sensor selection task at the current moment. For different attack strategies, theoretical results show that the average optimization rate of the proposed algorithm has a lower bound, and the error expectation is bounded. In addition, under two kinds of insecurity conditions, the proposed algorithm can guarantee the security of the entire network from the perspective of the augmented estimation error. Finally, the effectiveness of the developed method is verified by the numerical simulation and practical experiment.

Security is a crucial issue for cyber-physical systems, and has become a hot topic up to date. From the perspective of malicious attackers, this article aims to devise an efficient scheme on false data-injection (FDI) attacks such that the performance on remote state estimation is degraded as much as possible. First, an event-based stealthy FDI attack mechanism is introduced to selectively inject false data while evading a residual-based anomaly detector. Compared with some existing methods, the main advantage of this mechanism is that it decides when to launch the FDI attacks dynamically according to real-time residuals. Second, the state estimation error covariance of the compromised system is used to evaluate the performance degradation under FDI attacks, and the larger the state estimation error covariance, the more the performance degradation. Moreover, under attack stealthiness constraints, an optimal strategy is presented to maximize the trace of the state estimation error covariance. Finally, simulation experiments are carried out to illustrate the superiority of the proposed method compared with some existing ones.

This article studies the reachable set of cyber-physical systems subject to stealthy attacks with the Kullback-Leibler divergence adopted to describe the stealthiness. The reachable set is defined as the set in which both the system state and the estimation error of the Kalman filter reside with a certain probability. The necessary and sufficient conditions of the reachable set being unbounded are given for the finite and infinite time cases, respectively. When the reachable set is bounded, an ellipsoidal outer approximation is obtained by solving a convex optimization problem. An application of this approximation to the safety evaluation is also given. A numerical simulation of an unmanned ground vehicle is presented to demonstrate the effectiveness of the proposed approach.

This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

A recursive networked predictive control (RNPC) approach is proposed for networked control systems (NCSs), which mainly consists of two parts: a control prediction generator (CPG) and a network delay compensator (NDC). Based on a NARMA model, the CPG is applied to generate control predictions using the historical input-output data of the plant. The NDC is designed in the actuator to actively compensate for the network communication constraints such as network-induced delay, data packet disorder, accumulation and dropout. The RNPC is easy to be implemented in practice compared with previous results in that the recursive method is used to derive the future output predictions and control predictions, and the round-trip time delay is also used in the compensation scheme. Two RNPC systems are designed for a DC motor Internet-based control system, which are based on the nonlinear model and the simply linearized model, respectively. Practical experiments have been carried out to demonstrate the effectiveness of the proposed approach.