The evolution of network densification, from terrestrial Ultra-Dense Networks (UDNs) to aerial deployments of Unmanned Aerial Vehicles (UAVs), intensifies interference from line-of-sight links while imposing stringent energy constraints, making resource optimization critical. The Joint Power and Admission Control (JPAC) problem is fundamental to achieving efficient network services, but traditional algorithms face performance limitations in the high-dimensional sparse scenarios created by these large-scale networks. To address this, this paper proposes a novel algorithm, Adaptive Sieving with Gradual Removals (ASGR), inspired by the Adaptive Sieving (AS) method. While AS focuses solely on admitting links, ASGR enhances this process by incorporating a gradual removal mechanism. This key difference allows ASGR to proactively prune unpromising links, leading to higher-quality feasible solutions. Both theoretical analysis, which establishes a complexity ofO(Ulog3U) in sparse scenarios, and experimental results confirm that ASGR achieves a significant computational speedup over mainstream algorithms. The proposed algorithm attains this acceleration while achieving comparable results for total transmission power and the number of admitted links, and its comprehensive performance remains competitive in general scenarios.

To address the guidance problem of intercepting targets at the desired impact time, an optimal circular impact time guidance law is proposed in this paper. The proposed approach is designed by augmenting circular guidance with the well-justified predictor-corrector concept. Different from the existing circular impact time guidance (CITG) laws, the proposed method exactly predicts the impact time of circular guidance without any linearization, and corrects it by minimizing a quadratic integral index proportional to the total control effort. Hence, the proposed approach is more efficient in regulating the impact time. Theoretical analysis is provided to reveal the convergence properties of the predicted impact time and heading error. Comparative simulations are performed to validate the availability and superiority of the proposed method.

This paper proposes a precision autonomous landing system for unmanned aerial vehicles (UAVs) targeting high-speed moving platforms. By integrating gimbal-based precise positioning, smooth trajectory generation, and dynamically robust control, the system addresses key challenges in high-speed landing scenarios, such as significant visual localization deviations and difficulties in dynamic trajectory planning and control. The study introduces the Comprehensive Coordinate System (CCS-3AG) to eliminate dynamic optical-axis misalignment errors in the gimbal, thereby enhancing the gimbal’s ranging accuracy and control precision. We combine an enhanced single-stage minimum control (MINCO) trajectory framework (L-MINCO) with a bidirectional command update strategy to achieve fast and accurate trajectory planning that accounts for dynamic delays, and designs an Incremental Nonlinear Dynamic Inversion (INDI) controller for high-dynamic command tracking. Simulation and real-flight experiments demonstrate that, at target speeds between 0 and 7.7 m/s, the system attains an average landing precision of 0.108 meters, with a success rate of 97.78% across 90 actual landing tests, outperforming existing landing methods. This work provides a highly robust solution for UAV logistics delivery and emergency landing scenarios.

The existence of compound velocity and acceleration windups in clusters of nonholonomic mobile robots can seriously constrain the smoothness and stability of the overall motion. This article proposes a leader–follower-based distributed formation control framework for smooth and robust clustering of multiple nonholonomic mobile robots under compound windups of velocity and acceleration and unknown perturbations. The decoupled position and orientation kinematics and substrate wheel velocity dynamics are modularly devised via feedback linearization techniques to enable upper-level cooperative error regulation and lower-level wheel velocity trajectory tracking. The auxiliary dynamic system based on the velocity envelope generated by compound motion windups and the WMR kinematic is integrated into the collaborative error, adaptively mitigating the detrimental windup effects. The adaptive saturated extended state observer is utilized to flatly estimate unknown perturbations in the wheel velocity dynamics with enhanced robustness. Finally, the overall stability analyses are done based on Lyapunov’s theorem, and contrastive simulations and plentiful experiments are conducted to attest to the validity and availability.

Aggressive maneuvering is crucial for aerial vehicles to execute adversarial and penetration missions. However, this challenges the accurate tracking control of drones due to uncertainties induced by high-speed flight. Therefore, firstly, a highly dynamic tracking control framework is proposed to actualize the accurate tracking of aggressive trajectories with velocities up to 15 m/s (i.e., 54 km/h) and acceleration of 2 g. Secondly, in order to mitigate the impact of conjoint effects on uncertainty estimation during aggressive flights and to ensure that uncertainty is smoothly compensated, a novel adaptive nonlinear extended state observer (ANESO) with noise suppression and peak attenuation capabilities is designed. Finally, extensive comparative simulation and real-world practical experimental results certify the superiority of the proposed control strategy in tracking aggressive trajectories.

Mobile self-reconfigurable robots (MSRRs), as a novel multi-robot system with flexible configurations and task adaptability, hold promising applications in unstructured task environments. However, existing autonomous docking strategies are primarily applied in laboratory settings and face numerous challenges and limitations in actual applications, including differences in sensor characteristics, safety threats, and saturation constraints. To address these issues, this paper proposes a segmented secure docking control framework based on global localization and local perception to achieve stable and reliable reconfiguration of MSRRs in practical applications. Specific contributions include the implementation of a dual-layer constraint framework for safeness of units in the long-distance phase against velocity and acceleration nested windups, and the integration of active line-of-sight (LOS) correction and adaptive windup driving mobile units to achieve precise and rapid locking of docked positions within the LOS in the close-range phase. Finally, the validity of the proposed method is verified via physical experiments, offering an innovative approach to deploying MSRRs in complex scenarios.