A collaborative cloud-based control and defense framework is presented to address the challenges of low-carbon economy and compound attacks such as false data injection (FDI) and denial-of-service (DoS) in distribution networks. The framework consists of two main layers: the cloud-based multi-objective optimization layer and the control & defense layer. The upper cloud computing layer focuses on multi-objective optimization, aiming to minimize generation cost, line losses, and node voltage deviations in low-carbon conditions. Meanwhile, the lower layers combine control with attack defense strategies. State-feedback control is utilized to regulate the dynamics of the distributed generation, and defense strategies are employed to protect against potential compound attacks (FDI and DoS attacks). The defense strategy employs a dual control law sliding mode observer for attack reconfiguration, complemented by periodic event triggering. Also, the input-state stability of the control strategy is demonstrated. To validate the effectiveness of the proposed control strategy, simulations are conducted both on a computer and on the StarSim hardware-in-the-loop experimental platform.

In this paper, the hybrid control bumps attenuation problem is investigated for networked switched systems subject to multi-source disturbances and event-triggering. First of all, a new hybrid bumpless transfer performance characterization is established to attenuate abrupt large hybrid control bumps jointly caused by triggering and switching. Secondly, by introducing the bump-dependent function with switched adjustable parameters, a novel bump-related adaptive event-triggered strategy is creatively established to improve hybrid bumpless transfer performance. Thirdly, the sampled-state-based switching strategy is designed to simultaneously eliminate the Zeno phenomenon of switching and remove the dependency on real-time information of system state, which is a necessary prerequisite for the classical state-dependent switching law to work. Then, sufficient conditions are derived to guarantee the asymptotic stability of the augmented system while achieving bothH∞ performance and hybrid bumpless transfer performance. Moreover, we propose the hybrid bumpless transfer control scheme through the co-design of the switching rule, bump-related adaptive event-triggered strategy, and disturbance observer-based event-triggered controllers. Finally, the turbofan engine model is employed to verify the effectiveness and applicability of the presented method.

This study focuses on the node recovery optimization of cyber–physical power systems (CPPSs), and employs a propagation probability weighted method considering the electrical characteristics of the power network to establish the suscepted-exposed-infected-recovered-dead (SEIRD) epidemiological model. The model considers the complex interaction between infection propagation and power supply demand. Unlike traditional methods, the fault recovery strategy for CPPS in this model integrates multiple factors, including network status, degree distribution and centrality of infected nodes, node admittance, and power supply demand. By calculating a composite score for each fault node, this scoring method effectively prioritizes nodes to optimize the stability of the system. To validate the effectiveness of the proposed method, simulations are conducted on the IEEE 118-bus system. The results indicate that using a scoring-based method for node recovery optimization can significantly enhance the reliability and power supply capability of the system.

A cloud-based collaborative framework is proposed for the low-carbon economic dispatch problem in distribution networks (DNs). The framework integrates multi-objective optimization and control layers in a hierarchical control approach by coordinating active and reactive power from distributed generation (DG). The upper cloud computing layer comes to multi-objective optimization to minimize the generation cost, line loss and bus voltage deviation under low carbon conditions. The lower control layer regulates the dynamics of the DG using state feedback control combined with a game theory-basedH∞filter. Lastly, the proposed framework effectiveness is verified by StarSim hardware-in-the-loop simulation.

In this article, the problems of attack detection and secure tracking control for the power cyber-physical system are investigated. Considering the critical role of cyber networks in influencing decision-making for power grid optimization, a multiobjective optimization problem is introduced to determine the output power of generators. This optimization problem is solved based on the improved particle swarm optimization algorithm. The power system is modelled with dynamic characteristics taken into account. Furthermore, a resilient state-feedback tracking control strategy, that exploits a sliding mode observer, is introduced to ensure the reference value generated by the cyber network is tracked even under attacks. In addition, by using the reconstructed attack signals, an attack detection scheme is proposed. Some sufficient conditions are then obtained for the solvability of the tracking control problem. Finally, a simulation example and the experimental validation built into the StarSim hardware-in-the-loop simulation platform are introduced to illustrate the effectiveness of the proposed method.

This article presents a collaborative cloud-based control and defense framework designed to address scheduling challenges and interlayer false data injection (FDI) attacks in a low carbon economy. The proposed framework integrates the principles of low carbon economy strategy and new energy (wind turbine, photovoltaic) modeling to coordinate active and reactive power of distributed generation (DG) using a layered control approach. The framework consists of two main layers: a lower layer and an upper layer. The lower layer combines control and attack defense strategies. State feedback control is utilized to regulate the dynamics of the DG and defense strategies are employed to defend against potential controller FDI attacks. The upper layer, on the other hand, consists of interlayer defense strategies and cloud computing. The FDI defense from the lower control layer to the upper cloud computing layer obtains the actual operating state of the DG. And these data are used for cloud computing to get the next reference power. Cloud computing focuses on multiobjective optimization with the aim of minimizing generation cost, line loss, and bus voltage deviation under low carbon conditions. In order to verify the effectiveness of the proposed control strategy, simulations are conducted on a computer and StarSim hardware-in-the-loop experimental platform. The results show that the framework can effectively manage energy consumption in a low-carbon economy.

The article investigates security control problems of Takagi-Sugeno (T-S) fuzzy cyber-physical systems (CPSs) against cyber-attacks on sensor measurement and actuator input signals. In T-S fuzzy CPSs, premise variables (PVs) of fuzzy controllers may rely on the measurable states. They can be influenced by the sensor attacks, to bring about the degradation of the control performance. Then, a fuzzy cooperative interaction observer is proposed to construct new, reliable, and available PVs as ones of the controllers. On the other hand, the information usage of sensor and actuator attack estimation errors (AEEs) is insufficient in the existing fuzzy observers. Consequently, it is difficult to further improve the estimation accuracy. Different from them, two auxiliary systems containing the AEE information are constructed. They are able to cooperate with the dynamics of observer's estimation errors, so that the AEE information is fully utilized to enhance the reconstruction accuracy of the attack signals. Furthermore, a class of new fuzzy observer-based security control with the reliable, available PVs is given such that the influence of sensor and actuator attacks and unreliable PVs is removed. Finally, the merit of the presented scheme is illustrated by the MATLAB-based simulation and hardware-in-the-loop experiment.

This article studies state estimation and attack reconstruction problems for sensor and actuator biasing attack detection and secure control in cyber–physical systems (CPSs) through the observer techniques. The considered unknown bounded biasing attacks are produced from any dynamical system satisfying the error system between it and its equilibrium to be input-to-state stable, so that some reported biasing attacks are contained as a special case. In order to deal with potential compromised sensor output signals, an auxiliary filter is first introduced. Then, an augmented system, including the CPS and filter dynamics is constructed. Furthermore, with the aid of the system structure and input–output data, fictitious systems comprising the information of attack reconstruction errors are given. And then, by the cooperative interaction between the dynamics of observer’s estimation errors and virtual systems, an observer with a cooperative interaction structure is proposed, where the fictitious systems can be viewed as co-workers conducing to the accurate state estimation and attack reconstruction. By the Lyapunov approach, it is shown that state estimations and reconstructed attack signals, respectively, converge to the small set around system states and real attack signals, and the set bound can be reduced by adjusting the observer’s parameter. The applications of the observer to biasing attack detection and secure control are further given. Finally, an illustrative example validates the proposed methods.