Ling Tan

Extended state observers (ESOs) are usually tuned in continuous time and then transferred to sampled implementations, where the poles that the running code actually places, and the resulting noise sensitivity, can differ from the values the design intended. This paper examines a third-order ESO that combines exact zero-order-hold state prediction with a bandwidth-parameterized innovation gain vector. Its characteristic polynomial differs from the intended triple-pole polynomial by a defect proportional to the third gain, and the resulting spectral radius returns toward 1 as the requested bandwidth increases, although the observer remains Schur stable at every positive bandwidth. Matching the characteristic polynomial term by term gives a corrected gain vector that places all three poles at the intended location for the same update timing. When the disturbance varies from sample to sample, its increment enters the extended state through a specific input channel, and the resulting estimation lag depends on bandwidth. We then separate three noise measures that are often used interchangeably: the white-noise variance factor, the unit-impulse peak, and the induced gain for bounded input. A closed-loop simulation confirms that the spectral-radius rebound is a genuine disturbance rejection penalty: at high bandwidth the examined estimate alternates in sign from sample to sample, whereas the corrected design stays monotone. Finally, a JONSWAP (Joint North Sea Wave Project) case study uses the physically consistent spectral moment and log-uniform importance weights, and the corrected estimator is unbiased for the band-limited spectral moment that carries almost all of the physical spectral mass in the band used here. All numerical results are produced by a documented Python workflow that is released as an open repository.

Distributed model predictive control (MPC) coordinates constrained multi-agent systems while preserving computational locality, yet an implementation can cease to represent its stated convex program. We study a minimal linear plant so communication denial is a modeled source of uncertainty, and treat centralized equivalence as a hard gate: an edge-splitting consensus alternating direction method of multipliers (ADMM) formulation of the formation-tracking quadratic program is built so every distributed update can be audited against a centralized solver. Against a Lagrangian dual-decomposition baseline, ADMM attains the same accuracy without step-size tuning. Across topologies, horizons, penalties, budgets, and denial windows, the exact branch matches the centralized optimum to numerical precision, the equivalence gate and saturation signature survive scaling to eight agents, and the saturation signature also survives measurement noise. For communication denial, we prove a bounded-error result separating the summable-error regime, which recovers the optimum exactly, from the merely-bounded regime, whose limit points remain O(ε)-optimal. Coupling each edge weight to message age through an exponentially decaying confidence factor contracts the distributed problem to uncoupled local MPC, so formation error saturates as outages lengthen; a fixed-weight ablation isolates this decay as the causal mechanism. We contribute a reproducible baseline and two honest mechanisms—centralized-equivalence gating and confidence-decayed coupling—for distributed formation MPC.

To address the challenges of small scales, weak features, complex backgrounds, and misalignment in multi-scale fusion for small-object detection in remote sensing images, this study proposes a frequency-guided fusion reconstruction YOLO (FFR-YOLO), an improved YOLOv8 framework. The method performs joint optimization across three levels: the backbone, neck, and front end of the detector head. In the backbone, a frequency-guided anti-alias progressive downsampling module utilizes Haar wavelet decomposition to replace traditional strided convolutions and incorporates a low-frequency-guided high-frequency gating mechanism to mitigate detail loss and background noise interference during downsampling. In the neck, a bridge-guided bidirectional reconstruction fusion module (BRFM) enhances the collaborative reconstruction of multi-scale semantic and detailed information via multi-source weighted fusion and cross-path bridging interactions. At the front end of the detector head, a recalibrated dual-branch local–global fusion (RDLGF) module implements dynamic allocation and complementary fusion of dual-path features. Experiments were conducted on two datasets, DIOR and NWPU VHR-10. The results demonstrate that FFR-YOLO achieves a mAP@0.5 of 85.8% and a mAP@0.5:0.95 of 63.1% on DIOR and 93.6% and 62.1% on NWPU VHR-10. These outcomes present improvements over the baseline YOLOv8, validating the effectiveness and practical value of the proposed method for small-object detection in remote sensing scenarios.

Unmanned surface vehicle (USV) swarms operating in communication-denied maritime environments face degraded formation control when inter-agent state exchange is disrupted. This paper presents a three-layer control architecture integrating (1) LLM-assisted strategic mission planning with formal safety verification, (2) predictive tactical coordination combining physics-based motion extrapolation with online-learned neighbor behavior models, and (3) DMPC + ADMM execution for constrained formation control. The predictive coordination module in simulation-based evaluation across five representative scenarios reduces formation error by 76.0% (under simulation conditions) during 60 s communication outages compared to zero-hold prediction (Cohen d = 0.88, p < 0.001, DMPC + ADMM validated). The adaptive topology manager dynamically selects among star, mesh, and tree configurations via a utility function balancing communication quality, threat exposure, and overhead, reducing communication overhead by 71.7% (for the particular cases investigated) under the evaluated conditions while maintaining formation accuracy. Stability analysis using multiple Lyapunov functions and average dwell time theory guarantees global uniform exponential stability under topology switching, with explicit error bounds under communication denial derived via Gronwall-type arguments. The framework is validated through 1250 simulation trials across five scenarios with rigorous statistical analysis. The three-layer temporal decoupling architecture provides a practical template for safely integrating LLM-assisted planning with real-time multi-agent control in contested environments.

Unmanned Surface Vehicle (USV) clusters operating in maritime environments face dynamic communication conditions, including varying sea states, electromagnetic interference, and satellite denial, that render static communication topologies suboptimal. Existing approaches assess link quality through single indicators, typically the SNR, and lack mechanisms for automatic topology adaptation. This paper presents a multi-layer adaptive communication framework that achieves a mean communication quality score of 0.72 (vs. 0.51–0.66 for baselines), a message delivery rate of 94.1% under benign conditions, and a failure recovery time of 3.2 s (vs. 5.8–8.4 s for baselines) across five communication failure scenarios. The framework integrates three layers: a weighted multi-indicator communication quality metric fusing the SNR, packet loss rate, latency, and link stability into a unified score; a topology utility function that selects among centralized, distributed, and hierarchical topologies by optimizing a quality–threat–overhead objective; and a multi-modal backup communication manager with physics-based underwater acoustic, optical line-of-sight, and multi-hop relay fallback modes. Simulation results demonstrate consistent improvements over single-indicator and static-topology baselines, with particularly strong performance under satellite denial and jamming scenarios where multi-modal backup communication sustains delivery rates above 85% under simulated conditions. In summary, the framework demonstrates consistent improvements across all metrics (communication quality, delivery rate, recovery time) relative to four baselines, with the largest gains observed under the most challenging conditions (satellite denial and jamming). We emphasize that the framework adaptively selects among pre-defined canonical topologies (star, mesh, tree) based on real-time conditions rather than synthesizing optimal topologies de novo—a distinction between topology management and topology optimization.