Jian Li

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 both H∞ 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.

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 (DNs). The framework consists of two main layers: the cloud-based multiobjective optimization layer and the control and defense layer. The upper cloud computing layer focuses on multiobjective 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 (DG), 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-to-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 (HIL) experimental platform.

This article proposes a robust defense framework for hierarchical active distribution networks under multiple malicious attacks including false data injections (FDIs) and line disconnections. The framework integrates an adaptive augmented observer and a Defender-Attacker-Optimizer (D-A-O) algorithm against different layers of attacks. The adaptive augmented observer reconstructs the FDI signals and sends them to the controller as compensation values to defend against controller FDI attacks. In addition, the observer transmits the observed system states which are used in the D-A-O algorithm. Due to the observed true states of the system, the observer can defend against interlayer FDI attacks from the physical layer to the cloud decision layer. The D-A-O algorithm, based on Column-and-Constraint Generation (CC&G), is designed to achieve lower cost of the system under line disconnection scenarios. The proposed framework demonstrates stronger attack defense and lower cost through simulation and hardware-in-the-loop (HIL) testing of an IEEE 33-bus 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-based H_∞ filter. Lastly, the proposed framework effectiveness is verified by StarSim hardware-in-the-loop simulation.