Search Results (5,088)

High-precision robotics is frequently compromised by joint compliance, a factor often over-simplified by traditional rigid-body modeling. This research investigates the structural dynamics of a two-link manipulator, addressing critical discrepancies between experimental data and conventional models. Much like biological musculoskeletal systems, joint flexibility fundamentally influences the dynamic response of articulated structures. While traditional rigid-joint models accurately capture mode shapes, they yield excessive natural frequency prediction errors with peaks reaching 72%. To bridge this gap, a refined Flexible-Joint Finite Element Model (FJFEM) is developed to mimic adaptive joint compliance. This model is integrated with a bio-inspired computational framework (a Double-Stage Genetic Algorithm Framework (DSGAF)) to identify configuration-dependent joint stiffness across the operational workspace, where experimental frequencies $f_1$ and $f_2$ shift nonlinearly from 25.5 Hz to 44 Hz and 92.2 Hz to 51 Hz, respectively. Experimental validation demonstrates that this evolutionary strategy reduces frequency tracking errors to less than 3.5% across all positions, achieving an average identification routine runtime of 1.8 s. By capturing nonlinear compliance behavior, this framework provides a robust foundation for the design, online calibration, and vibration control of advanced flexible robotic systems. Full article

Accurate and robust localization is essential for intelligent vessels operating in complex marine and port environments. However, single-sensor localization is often affected by limited observation range, environmental occlusion, local interference, and sensor degradation. Although multi-sensor fusion can improve localization reliability, unknown cross-correlated measurement noise arising from shared disturbances, time synchronization errors, communication delays, and inconsistent fusion rates may degrade traditional information-filter-based fusion methods. To address this problem, this paper proposes an Adaptive Correlation Information Filter Network (ACIFNet) for multi-sensor fusion localization of intelligent vessels. ACIFNet preserves the recursive structure of the extended information filter and uses a Transformer-based network to learn adaptive information-domain fusion weights, thereby compensating for unknown inter-sensor correlations without explicitly estimating the full correlation covariance matrix. Experiments on constant-velocity, coordinated-turn (CV), and three-degree-of-freedom vessel motion models, together with a real-world restricted-waterway dataset, demonstrate that ACIFNet achieves higher localization accuracy and stability than Edge Incorporative Fusion (EIF)-inexact fusion, measurement fusion, and KalmanNet. In the CV and three-degree-of-freedom experiments, ACIFNet reduces the mean RMSE by 48.7%, 23.2%, and 26.1%, respectively, compared with KalmanNet. On the real-world dataset, ACIFNet achieves a mean position error of 9.90 m, an RMSE of 11.24 m, and a cross-track error of 8.72 m. These results show that ACIFNet effectively combines the interpretability of information filtering with the adaptive representation capability of neural networks for robust multi-sensor fusion localization under unknown cross-correlated measurement noises. Full article

This paper proposes a safe and effective human–robot physical interaction control framework for exoskeleton robots that enhances system compliance and safety while enabling the robot to adapt to human motion. The framework is designed around two primary objectives: first, a model-free adaptive control method is employed for reference trajectory estimation to achieve real-time estimation of human motion intention; second, the Forgetting Factor Recursive Least Squares (FFRLS) method is utilized for online estimation and the learning of human impedance parameters, considering their time-varying nature. In addition, a model-free adaptive trajectory tracking control strategy is proposed to optimize control performance during human–robot physical interaction. Simulation results demonstrate that the proposed control framework outperforms conventional methods significantly in terms of safety and compliance. Full article

The bearingless induction motor (BIM) relies on accurate displacement feedback to maintain stable magnetic suspension, but sensor faults, degradation, and noise can distort feedback and induce transients during branch switching. This paper proposes a confidence-fusion-based fault-tolerant displacement measurement method for the BIM suspension feedback chain. A four-channel asymmetric redundant sensor configuration is developed, and channel state evaluation functions are constructed from sampling-difference terms and geometric-consistency residuals. A decreasing Sigmoid mapping with first-order smoothing generates continuous confidence coefficients to represent channel health. Combined with discrete fault flags of the primary channels, four reconstruction branches, AB, BC, AC, and CD, are adaptively weighted to obtain the reconstructed displacement, which is connected to the original suspension controller through a smooth feedback access mechanism. A MATLAB/Simulink closed-loop suspension model is used to evaluate the method under fault-free operation, an abrupt fault of primary channel A, simultaneous and sequential faults of primary channels A and B, abrupt and gradual degradation, constant bias, intermittent signal dropouts, and noise disturbance of primary channel B. Results show that the method identifies abnormal primary channels, redistributes reconstruction weights according to sensor conditions, and maintains a fallback path through the CD branch under dual-primary-channel failure. Under channel-B degradation, the confidence coefficient tracks the deterioration and supports the subsequent AB-to-AC branch transfer, whereas under noise disturbance, the fault flag remains inactive and unnecessary branch switching is avoided. The method improves feedback continuity without changing the main suspension controller. Full article

Within the domain of Earth observation, tracking terrestrial transitions via high-resolution optical data plays a fundamental role. Nevertheless, current methods face critical challenges, including the difficulty in collaborative modeling of local details and global features and the singularity of bi-temporal difference representation, along with insufficient cross-scale feature communication, thereby constraining both the precision and resilience of models when applied to complicated environments. To solve these problems, we propose LADENet (Locally Adaptive Mamba and Multi-scale Feature Enhancement Network), an innovative framework that synergizes CNN, Transformer, and Mamba paradigms. By leveraging customized local contextual refinement alongside sophisticated hierarchical fusion, this integration delivers highly precise and resilient detection performance. LADENet adopts a weight-sharing multi-level Transformer encoder combined with a sequence reduction mechanism to generate multi-scale global features, achieving precise alignment of bi-temporal features and global context modeling while reducing computational complexity. To realize accurate localization and local enhancement of changed regions, we design a dual spatiotemporal adaptive local feature marking module based on State-Space Scanning (SSS). This module screens high-saliency changed regions through an adaptive scanning strategy, realizes pixel-aligned spatiotemporal feature fusion via cross-temporal state-space scanning, and introduces a sliding window boundary calibration mechanism to alleviate boundary information loss caused by window segmentation. To strengthen the feature representation of changed regions, a dual-branch difference enhancement module is constructed, which collaboratively captures global change trends and fine-grained local features through an attention-enhanced difference branch and a multi-scale convolution concatenation branch, effectively suppressing background interference. To address the semantic gap between cross-scale features, a global cross-scale spatial feature fusion decoder is proposed, which balances local detail preservation and global context perception through the synergy of spatial attention and two-dimensional selective scanning, completing refined multi-scale feature fusion and spatial resolution recovery. To rigorously validate the proposed LADENet, comprehensive experiments were conducted across four widely adopted bi-temporal benchmarks: LEVIR-CD, WHU-CD, CLCD-CD, and GVLM-CD. The presented architecture establishes substantial superiority over existing cutting-edge methodologies across primary evaluation criteria. Specifically, it yields an F1-measure of 91.06% alongside an IoU of 85.28% in the LEVIR-CD tests, while registering 90.51% (F1) and 82.45% (IoU) for WHU-CD. Similarly, robust outcomes are delivered on CLCD-CD (82.15% F1, 72.83% IoU) as well as GVLM-CD (89.12% F1, 77.78% IoU). These results demonstrate that LADENet possesses excellent detection accuracy, boundary delineation capability and generalization performance in diverse and intricate bi-temporal observation environments. Full article

We present a one-dimensional shear-force-driven droplet formation model with a flux-based error estimator. The model is derived using asymptotic expansion and a front-tracking method to simulate the droplet interface. The model is then discretized using the Galerkin finite element method in the mixed form. However, the solution gradients exhibit large jumps across element boundaries and can grow rapidly due to the highly convective pinch-off process. This leads to an erroneous droplet interface and incorrect curvature. Therefore, the mesh must be sufficiently refined to capture the interface accurately. The mixed form of the governing equation naturally provides smooth interface gradients that can be used to compute the error estimate. The computed error estimate is then used to drive the adaptive mesh refinement algorithm. The efficacy of the error estimator is illustrated by comparing the droplet profiles obtained with adaptive refinement to those obtained with regular refinement. The adaptive mesh refinement approach reduces the computational cost significantly without compromising accuracy. For an 85% glycerol droplet in co-flowing air, AMR reproduces pinch-off location, surface area, volume, and pinch-off time with only ≈1% accuracy loss compared to the highly refined reference while reducing wall-clock time from 638 s to 153 s (4.17× speedup) and reducing the maximum element count from 800 to 146 (81.75% reduction). Full article

Water Resources Plans (WRPs) are foundational policy instruments globally, yet implementation rates remain persistently low. Without consistent action classification, policymakers cannot define what to measure, track outcomes systematically, or generate evidence for adaptive learning. This study develops and validates a comprehensive typology of water resources actions, positioning it as a foundational framework for systematic performance measurement and international transferability. The typology was constructed through a rigorous multi-phase methodology: initial consolidation and unification of actions from Ceará’s hydrographic plans (serving as a methodological foundation due to the state’s comprehensive participatory water resources planning process, 2021–2024), expert consensus via focal group discussions, and empirical validation across the entire Brazilian national context. Validation encompassed 53 Water Resources Plans (20 Brazilian state plans, 14 Brazilian river basin plans, and 19 international plans), achieving 99.6% applicability. The typology operationalizes action classification through 13 first-order categories and 160 subtypes, organized around the concept of ‘deliverable’—a governance-neutral principle that permits instantiation across diverse institutional arrangements. The identified action categories reflect universal principles of water management maturity recognized in international planning contexts (European Water Framework Directive, Turkish and Moroccan water governance systems), demonstrating that the typology captures generalizable patterns of adaptive planning behavior rather than Brazil-specific peculiarities. Furthermore, the typology’s governance-agnostic design—based on deliverable-centered logic rather than institutional-specific procedures—enables its adaptation to diverse water governance models, from highly decentralized (Brazil’s basin committees) to centralized systems (as in other countries). By offering a structured and comprehensive categorization, this typology functions as a valuable menu of action types for future Water Resources Plans development, ensuring a holistic consideration of potential interventions. Its dual role—as a precursor to robust indicator development and as a guide for future planning—underscores its transformative potential for both assessing past actions and informing prospective water management. Full article

The increasing integration of wind power reduces the equivalent inertia of power systems, leading to lower frequency nadirs and higher rate of change of frequency following disturbances. In Type-4 wind turbine systems, conventional maximum power point tracking (MPPT) may counteract the additional inertial power command during frequency support and cause secondary frequency dips during rotor-speed recovery. To address these issues, this paper proposes a virtual-inertia rate-of-change-of-frequency (VI-RoCoF) frequency-modulated Andronov-Hopf oscillator (AHO)-based adaptive inertia enhancement method together with an adaptive MPPT coordination strategy. The proposed method constructs a frequency-support demand from frequency deviation and VI-filtered RoCoF and embeds it into the instantaneous angular-frequency evolution of the AHO. Different from a conventional linear virtual-inertia controller that directly converts frequency-deviation and RoCoF signals into an algebraic power command, the proposed method realizes the additional support through a bounded limit-cycle frequency-forming process, thereby preserving phase continuity and nonlinear amplitude self-regulation during frequency modulation. Meanwhile, the adaptive MPPT strategy adjusts the power reference in stages to suppress the counteractive effect of conventional MPPT on inertial support and to ensure a smooth transition back to maximum power point tracking. Theoretical analysis shows that the proposed modulation maintains the limit-cycle stability of the AHO under bounded control constraints while improving the equivalent inertia and damping characteristics of the system. Simulation results, including both averaged-model and switching-level SPS simulations, demonstrate that, compared with conventional AHO-based, fixed-inertia AHO-based, and linear VI-RoCoF benchmark schemes without AHO dynamics, the proposed AHO-MPPT coordinated control strategy increases the frequency nadir, reduces the peak RoCoF, improves recovery-stage frequency dynamics, mitigates secondary frequency dips, maintains bounded AHO internal variables, and preserves DC-link voltage stability. Full article

In outdoor road environments, vehicle acoustic source direction-of-arrival (DOA) estimation is challenged by a low signal-to-noise ratio (SNR), dynamic-noise interference, and stringent real-time requirements. Under such conditions, conventional methods often struggle to achieve an effective balance among estimation accuracy, computational efficiency, and robustness against noise. To address this issue, this paper proposes a DOA estimation method that integrates a dynamic-pruning strategy with an adaptive subspace tracking mechanism. The proposed approach reduces computational complexity while enhancing algorithmic stability in complex and time-varying noise environments. Extensive experiments conducted on simulated data, the LOCATA dataset, and real-world outdoor road measurements demonstrate that the proposed method achieves comparable or superior DOA accuracy relative to conventional approaches, while significantly reducing computational cost. Furthermore, it exhibits stronger stability and robustness in real-world static and dynamic vehicle localization scenarios. Our method achieves a more favorable trade-off among multiple performance metrics. The results show that this method has good engineering application potential in complex outdoor environments, and can provide a practical solution for real-world vehicle monitoring. Full article

The high-performance control of active suspension electro-hydraulic actuators (ASEHA) is limited by a timing mismatch: the primary internal physical state (load pressure) responds to disturbances almost instantaneously, whereas the tracking error used for feedback lags behind. To address this issue, a physics-aware co-design framework introduces three innovations: (i) a pressure-adaptive bandwidth ESO that directly schedules the observer bandwidth via load pressure, enabling faster disturbance estimation; (ii) a disturbance-adaptive constraint controller whose safety boundary is adjusted in real time using the observer‘s disturbance estimates, balancing tracking precision and safety; and (iii) a structured disturbance-separation architecture that reduces observer burden via model-based feedforward. By leveraging load pressure as a feedforward signal, this framework overcomes the latency inherent in error-feedback methods. Comparative simulations show that the proposed method outperforms conventional error-feedback methods by achieving a significant reduction in estimation error, as well as 2.3-times faster convergence, while ensuring both high tracking accuracy and strict constraint satisfaction. Full article

The implementation of safe and reliable Autonomous Driving Stacks in complex urban environments remains a formidable engineering challenge. While classical modular pipelines provide necessary component-level interpretability, they are inherently rigid, often struggling to adapt to novel environments and failing to provide robust scene interpretation in highly interactive scenarios. In this paper, we present a modular End-to-End ROS-based autonomous driving architecture that upgrades a classical modular baseline by injecting learning-based models into its individual processing layers, integrating GaussianCaR and CLIP for dense semantic BEV perception, expanding the Hierarchical Petri Net state space for safe multi-agent reasoning, refining the planning layer with continuous curve optimization, and replacing the previous reactive controller with an Adaptive Nonlinear Model Predictive Control strategy for superior trajectory tracking. Validated in the CARLA simulator across challenging traffic scenarios and adverse environmental conditions, the proposed architecture raises the Driving Score from 53.81% to 66.46% over the previous baseline, driven by a substantial increase in the Infraction Penalty from 0.59 to 0.79, reflecting a fundamental shift towards safer and more conservative driving behavior at the cost of a moderate reduction in route completion. Against pure End-to-End approaches, our architecture achieves the highest Driving Score at 73.9% and the strongest Infraction Penalty at 0.913, demonstrating that modular interpretability and competitive End-to-End performance are not mutually exclusive. Code will be made publicly available online. Full article

Despite the growing adoption of Augmented Reality (AR) in industrial maintenance, no structured methodology exists to systematically identify which operations are best suited for effective AR deployment. This study addresses this gap by proposing a five-step, Multi-Criteria Decision-Making (MCDM)-based selection framework for determining AR-compatible maintenance operations in high-speed railway systems. The framework—applied under the AFNOR FD X 60-000 standard—integrates maintenance-level compatibility analysis, multi-criteria filtering across five dimensions (operational frequency, execution complexity, safety impact, traceability, and scalability), and expert validation involving 100 railway maintenance professionals. Applied to 12 candidate operations at a high-speed railway maintenance facility in Morocco, the framework identified OP10 (insulating oil level verification of the Main Transformer) as the optimal pilot use case, confirming expert consensus (Kruskal–Wallis: H = 18.479, p < 0.001). The selected operation was subsequently integrated into a hybrid AR–Deep Reinforcement Learning architecture employing a Deep Q-Learning (DQL) agent for adaptive decision support, deployed on a Magic Leap 2 head-mounted device via a Unity-based rendering pipeline with hybrid marker-based and markerless computer vision tracking through Vuforia Engine. Experimental validation conducted over three months under simulated and semi-operational conditions yielded a 34–47% reduction in intervention time, a 55–70% decrease in human error rates, and a 28–42% decline in failure-related costs. While results are currently limited to a single-site context, the proposed methodology is directly transferable to any asset-intensive, regulated maintenance environment beyond the railway sector. Full article

Autonomous Haulage Systems (AHS) have significantly transformed surface mining operations by improving safety, productivity, and operational consistency. Currently, AHS predominantly rely on vehicle-centric perception architectures. Onboard LiDAR, radar, cameras, and Global Navigation Satellite Systems (GNSS) perform sensing, interpretation, and decision-making within individual systems. These processes enable collision avoidance and path tracking. However, they are limited in their ability to consider the broader, dynamic mining environment characterized by dust, terrain degradation, geotechnical instability, heterogeneous traffic, and rapidly evolving operational conditions. This paper presents a systematic review of dynamic vision systems of AHS in surface mining. It critically analyzes the transition from autonomy to interconnected, ecosystem-aware intelligence. The review synthesizes literature from mining automation, robotics, intelligent transportation systems, and multi-agent perception. It assesses sensing technologies, perception algorithms, sensor fusion strategies, and environmental robustness techniques. Attention is focused on the limitations of egocentric perception models in complex surface mining ecosystems. Building on identified gaps, the paper proposes a conceptual framework for Ecosystem-Centric Dynamic Vision (ECDV). Perception is enhanced through integration with fleet communication networks, dispatch systems, digital twins, geotechnical monitoring platforms, and environmental sensing infrastructure. The framework outlines a multi-layer architecture enabling cooperative perception, predictive hazard modeling, and risk-aware decision support at the mine-wide level. The review concludes by outlining a research agenda to transition from vehicle autonomy to ecosystem intelligence in surface mining. It highlights opportunities in cooperative perception, adaptive sensor fusion under degraded visibility, and digital-twin-integrated predictive safety systems. Full article

Vehicular platoons under realistic scenarios are prone to actuator saturation, model uncertainties, and external disturbances, which degrade transient tracking and spacing stability. Conventional prescribed-performance control (PPC) strictly requires initial errors to lie within a predefined envelope, while finite/fixed-time schemes cannot directly assign the settling-time bound. To resolve these limitations, this paper proposes a practical predefined-time sliding-mode adaptive platoon control strategy under input saturation constraints. Specifically, a smooth hyperbolic-tangent approximation combined with a mean-value-theorem-based gain formulation is utilized to handle saturation nonlinearity and simplify stability analysis. A novel initial-error transformation is developed to eliminate the stringent envelope constraint on the original initial tracking error. Furthermore, a predefined-time sliding variable and an adaptive compensation mechanism are synthesized to guarantee that tracking errors converge into a bounded neighborhood of the origin within a user-specified time. Numerical simulations and comparisons with predefined-time sliding-mode and PID controllers demonstrate that the proposed strategy eliminates initial error restrictions and suppresses chattering. Compared to the alternative schemes, the proposed method restricts the maximum tracking error within 0.05 m—representing reductions of approximately 77% and 91%, respectively—and shortens the settling time to within 2 s. These results validate its effectiveness for robust cooperative platoon control. Full article

This paper presents an adaptive neural-network-based algorithm for chattering mitigation in sliding mode control (SMC) of parallel multicellular DC/AC power converters. Although conventional SMC provides strong robustness against parameter uncertainties, external disturbances, and load variations, its discontinuous control action often generates chattering, resulting in excessive switching activity and reduced converter performance. To address this limitation, a computationally efficient adaptive neural network is integrated into the SMC framework to approximate the discontinuous switching term and generate a smooth control signal. The proposed algorithm updates neural network parameters online through an adaptive learning mechanism, enabling real-time compensation of modeling uncertainties while preserving the inherent robustness of SMC. The resulting adaptive neural network sliding mode control (ANN-SMC) algorithm is formulated to ensure accurate output voltage tracking, balanced operation of converter cells, and reduced switching oscillations. Extensive simulation studies are conducted under different operating scenarios, including load variations and system disturbances. The performance of the proposed method is evaluated against classical SMC using quantitative indicators related to tracking accuracy, dynamic response, robustness, and chattering suppression. The results demonstrate that the ANN-SMC algorithm significantly reduces high-frequency oscillations while improving transient behavior and maintaining robust operation. These findings indicate that the proposed adaptive learning-based control algorithm constitutes an effective and scalable solution for advanced power conversion systems operating under uncertain conditions. Full article