Accurate modeling of car-following behavior is crucial for improving traffic flow, safety, and energy efficiency in connected and autonomous vehicle systems. This study investigates vehicle dynamics using the Optimal Velocity Model (OVM) for both single vehicles and multi-vehicle platoons, with a focus on stability, accuracy, communication delay, and energy consumption. Simulation results indicate that the classical OVM exhibits persistent stop-and-go oscillations across all intervals, reflecting inherent instability under high-sensitivity parameters. Single-vehicle tracking initially achieves high accuracy, with a root-mean-square error (RMSE) of 0.74 m/s, but degrades over time, rising to 1.21 m/s, highlighting cumulative error under dynamic conditions. Extending the model to a vehicle string reveals disturbance amplification and sustained limit-cycle oscillations, demonstrating string instability and the critical influence of inter-vehicle interactions. Communication delay analysis shows that minimal latency maintains stable tracking. In contrast, delays exceeding 500 ms induce high-amplitude oscillations in relative velocity, providing quantitative bounds for vehicle-to-vehicle and vehicle-to-infrastructure systems. Energy consumption is analyzed over time, showing consistent cumulative trends and robust handling of transient high-power events. Calibration against empirical spacing data improves model fidelity, maintaining RMSE below 16 m across all scenarios. Overall, the framework enhances velocity tracking and reproduces realistic traffic patterns, offering a robust platform for evaluating car-following behavior. The results provide a foundation for future work on adaptive, learning-based controllers, delay-aware coordination, and real-time validation in complex traffic environments.

This paper addresses the distributed H_∞-consensus state estimation issue for a class of discrete time-varying systems operating within binary sensor networks. An integral measurement output model is developed for each node to formulate the time intervals associated with sampling. Every binary sensor is equipped with an energy harvester to improve power efficiency. Information transmission between sensor nodes and their neighboring nodes is carefully orchestrated through a dynamic event-triggering protocol. Valuable information for estimation purposes is obtained by analyzing the discrepancies between the real and predicted inputs of binary sensors. Information from neighboring nodes is only transmitted when the node’s energy level is positive and the event-triggering condition is met. Two random variables are introduced to represent the energy level and the information from neighboring nodes to be received or not, respectively. Based on the available information, a distributed estimator is constructed for every binary sensor, and the expected performance constraints are given for the dynamic characteristics of estimation errors within a finite horizon. Sufficient conditions are constructed to obtain the desired distributed estimation performance constraint, and associated estimator gains are achieved by resolving the recursive linear matrix inequalities at each node, indicating the excellent scalability of the proposed approach. Ultimately, the effectiveness of the distributed estimation algorithm proposed in this paper is validated through an extensive simulation analysis.

This paper focuses on the problem of boundary control for a distributed parameter system (DPS) under denial-of-service (DoS) attacks. Initially, a DPS model is employed. Considering the incomplete measurement of the DPS's state, a novel boundary observer is then proposed, which only relies on the right boundary state instead of full-domain information to achieve accurate state estimation, significantly reducing the measurement cost. Subsequently, an anti-DoS observer-based boundary controller is designed, which is applied only to the spatial boundary to lower actuator deployment costs while improving robustness to intermittent DoS attacks. In addition, a Lyapunov-Krasovskii functional is introduced, and the design methods for the controller and observer are derived by solving linear matrix inequalities. Finally, the feasibility of the control strategy is verified through an example.

With the ongoing evolution of modern power grids, power flow calculation, which is the cornerstone of power system analysis and operation, has become increasingly complex. While promising, existing data-driven methods struggle with key challenges: poor generalization in data-scarce scenarios, efficiency bottlenecks when integrating physical laws, and a failure to capture higher-order interactions within the grid. To address these challenges, this paper proposes a Spatial Multi-scale Reservoir Computing framework that seamlessly incorporates functional matrix and physical information to solve power flow calculation. The framework utilizes parallel readout layer parameters to construct the functional matrix and integrates physical information to create a multi-scale information processing mechanism and readout constraints. By improving the reservoir computing model, the framework also combines the reservoir paradigm with the inherent physical characteristics of power grids while maintaining computational efficiency. Experimental results demonstrate that the presented framework achieves exceptional performance across various IEEE bus systems, showcasing superior generalization in data-scarce scenarios, as well as improvement in computational speed, prediction accuracy, and robustness, while ensuring the feasibility of the output results.