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ABSTRACT System dynamics uncertainties and cyberattacks pose significant challenges to load frequency control in power systems. This paper presents a data‐driven load frequency control strategy for interconnected multi‐area power systems subject to denial‐of‐service attacks that disrupt both feedforward and feedback communication channels. A dynamic linearization method is employed to construct an equivalent data model of the power system. To enhance control performance, the proposed controller integrates proportional, differential, and quadratic difference terms. Additionally, a dynamic dual event‐triggered mechanism is designed to improve resource efficiency and reduce computational overhead. The proposed approach also compensates for DoS attacks affecting both feedback and feedforward channels. Simulation results demonstrate that the method operates without requiring prior system model information, relying solely on control input and output data. Extensive simulations validate the effectiveness and robustness of the proposed control strategy.
The Six-Dimensional Movable Antenna (6DMA) system has emerged as a promising technology to enhance wireless capacity by fully exploiting spatial degrees of freedom. However, applying 6DMA to high-mobility Internet of Vehicles (IoV) scenarios faces significant challenges, primarily due to the difficulty of acquiring instantaneous Channel State Information (CSI) and the risk of service interruptions caused by mechanical reconfiguration delays. To address these issues, this paper proposes a low-complexity, CSI-free single-step reconfiguration framework. First, we design a deterministic discrete position generation scheme based on a latitude-longitude grid with inherent topological structures. Leveraging graph theory, we explicitly model and theoretically derive the lower bounds of movement and time costs for antenna reconfiguration. Subsequently, utilizing the directional sparsity of 6DMA channels, we develop an adaptive optimization strategy that fuses offline environmental priors with online historical feedback. Furthermore, a periodic reconfiguration mechanism based on predicted cumulative vehicle distributions is introduced. By strictly restricting antenna adjustments to the first-order spatial neighborhood, the proposed single-step method effectively eliminates service interruptions. Simulation results demonstrate that the proposed scheme significantly outperforms traditional fixed and global-search-based benchmarks in terms of uplink sum rate, while incurring negligible mechanical overhead and latency, thereby validating its feasibility and robustness in highly dynamic vehicular networks.
This paper investigates a multi-Unmanned Aerial Vehicle (UAV) joint base station-assisted Internet of Vehicles (IoV) task offloading system in dense urban environments. To minimize system delay and energy consumption under strict coupling constraints, the complex non-convex optimization problem is decoupled into a hierarchical execution framework. First, a sequential distributed optimization algorithm based on Second-Order Cone Programming (SOCP) is proposed to optimize the 3D flight trajectory of each UAV, ensuring adaptive network coverage. Second, a novel hybrid resource scheduling paradigm synergizing Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) is developed. Within this framework, the DRL agent dictates the initial resource allocation, while the LLM acts as a semantic macro-scheduler to rectify long-tail allocation imbalances for failed and surplus tasks. Crucially, a reward decoupling mechanism is introduced to isolate DRL training from external LLM interventions, thereby ensuring policy convergence. Finally, the task offloading ratios are precisely determined via Linear Programming (LP) within an alternating optimization loop. Simulation results demonstrate that the proposed method significantly outperforms traditional multi-agent reinforcement learning baselines in terms of task success rate and system efficiency.
In general, the microstructure of direct laser deposited (DLD) titanium alloy had a coarse or columnar prior (3 grains, which cannot satisfy the demand of high strength and ductility. In this study, a hybrid DLD and in-situ rolling technique was utilized for the fabrication of Ti-10V-2Fe-3Al alloy to manipulate the microstructure and optimize the tensile property. It has been found that DLD specimen had a relatively lower yield strength due to the coarse intragranular alpha lath. A higher in-situ rolling load at 8 kN increased the yield strength but deteriorated the ductility due to the formation of continuous grain boundary alpha GB. An appropriate in-situ rolling load at 2 kN contributed to the refinement of both prior (3 grains and intragranular alpha, which resulted in improved yield strength compared to the other two counterparts. In addition, the shortest thermal cycling time in the inter-granular alpha GB precipitation temperature interval provided a discontinuous grain boundary alpha GB and the highest ductility in the specimen in-situ rolled at 2 kN.
This paper proposes a dual-channel dynamic event-triggered (DET) constrained control strategy with packet loss compensation to regulate power in battery energy storage systems (BESSs) under packet loss conditions and input saturation constraints. The compact form dynamic linearization (CFDL) method is first employed to simplify the modeling of BESSs. Based on the linearized model, output data packet loss is compensated, while an input data packet loss compensation mechanism is developed using the latest control increment. A dynamic antiwindup compensator is designed to mitigate the effects of constraints on the magnitude and rate of control input. Furthermore, semi-independent DET mechanisms are established for control input and measurement output channels to reduce communication and computation resource consumption. Theoretical convergence analysis is given by employing the contraction mapping principle and indicates that the tracking error is uniformly bounded. Finally, numerical simulations verify the efficacy of the proposed approach.
