Search Results (97)

In flexible manufacturing systems, the composite mobile manipulator (CMM) is subject to nonlinear inertial disturbances arising from the dynamic coupling between the mobile platform and the robotic arm. These disturbances significantly impair positioning precision during grasping tasks. This paper addresses the dynamic decoupling of multi-body nonlinear inertial disturbances within CMM systems. Departing from the conventional “stop-then-plan” serial execution paradigm, we propose a full-cycle spatiotemporally coupled trajectory optimization method. The operation cycle is bifurcated into two synergistic stages: “dynamic calibration” and “static execution.” The dynamic calibration trajectory is pre-planned and executed synchronously during platform movement to actively compensate for inertial-induced pose deviations. Concurrently, the static execution trajectory is optimized and then triggered immediately upon platform standstill, ensuring a seamless and precise transition to the “Grasping Pose”. It is worth noting that the temporal characteristic central to this framework lies in the concurrent execution of static trajectory optimization and platform transit: by the time the platform reaches its destination, the pre-planned trajectory is already available for immediate triggering, achieving zero task-switching wait time at the planning layer. The term “zero-latency” here does not imply a fixed-cycle real-time response at the control layer, but rather the complete elimination of decision latency afforded by the parallel planning architecture. This framework eliminates computational latency, markedly enhancing operational efficiency. Key innovations include two novel constraints. First, the Adaptive Task-space Bounded Search Constraint (ATBSC) framework restricts optimization to a geometry-inspired search region, thereby enhancing search efficiency and ensuring controllable deviations. Second, the Multi-Rigid-Body Coupling Constraint (MRBCC) system explicitly models inertial transmission across motion phases to suppress pose fluctuations. The proposed framework is developed and validated within an obstacle-free workspace. In simulation-based validation on a UR10 6 degree-of-freedom manipulator model, experimental results indicate that ATBSC increases valid solution density to 84.7% and reduces average deviation by 72.8%. Furthermore, under the tested conditions, MRBCC mitigates end-effector position errors by 79.7–81.0% with a 97.5% constraint satisfaction rate. The improved Cuckoo Search algorithm (ICSA), serving as the solver component of the proposed framework, achieves an 11.9% lower fitness value and a 13.1% faster convergence rate compared to the standard Cuckoo Search algorithm in the tested scenarios, suggesting its effectiveness as a reliable solver for the constrained multi-objective trajectory optimisation problem. Full article

Nonlinear friction in the mechanical transmission system of machine tools induces transient stagnation of the feed axis as its velocity crosses zero, thereby giving rise to contouring errors in multi-axis machining and significantly degrading machining accuracy. To address this issue, a feedforward compensation strategy is proposed based on a simplified static friction model (SSFM) with dual-segment and dual-parameter characteristics. The nonlinear friction is represented by a combination of a linear segment and an exponential segment, while the model incorporates two essential parameters that characterize the maximum friction force and the negative damping effect. Experimental results from two-axis circular trajectory tests show that the proposed SSFM reduces contour errors by approximately 73.4% and 79.2% at 600 mm/min and 2100 mm/min, respectively. To improve compensation under high-speed conditions, an acceleration-dependent dynamic correction is further introduced to establish the SDFM. The results show that the maximum contour error is further reduced to 1.44 μm and 1.49 μm at 3600 mm/min and 5000 mm/min, respectively. Compared with many existing reduced-order or hybrid friction models that rely on more parameters or more complex identification procedures, the proposed method provides a more compact and compensation-oriented modeling strategy for the velocity-reversal region of CNC feed systems. Full article

With the development of 6G communication technology, Space–Air–Ground Integrated Networks (SAGINs) have become critical infrastructure for global intelligent collaborative computing. However, federated learning deployment in SAGINs faces three severe challenges: the high dynamics of satellite orbital motion, node resource heterogeneity, and privacy vulnerabilities in data transmission. This paper proposes an adaptive compressed sensing differential privacy federated learning framework based on orbital spatiotemporal characteristics. First, we design orbital periodicity-driven time-varying sparse sensing matrices that dynamically adjust compression strategies according to satellite orbital positions, achieving intelligent communication efficiency optimization. Second, we propose an orbital predictability-based privacy budget temporal allocation mechanism and perform differential privacy noise injection in the compressed domain, establishing a compression–privacy joint optimization algorithm. Furthermore, we construct an energy–communication–privacy ternary collaborative mechanism that achieves multi-objective dynamic balance through model predictive control. Finally, we design reinforcement learning-based dynamic routing scheduling and hierarchical aggregation strategies to effectively handle the time-varying characteristics of network topology. Simulation experiments demonstrate that compared to existing methods, the proposed approach achieves 3–12% improvement in model accuracy and 30–50% enhancement in communication efficiency while maintaining differential privacy protection with dynamic privacy budget $\epsilon \in [0.1,10.0]$ and compression ratio $\rho \in [0.2,0.8]$. Unlike static compressed sensing approaches that ignore orbital periodicity, the proposed orbital-driven time-varying sensing matrices reduce reconstruction error by up to 19.4% compared to fixed-matrix baselines, validating the synergistic effectiveness of integrating orbital spatiotemporal characteristics with federated learning in 6G SAGIN deployments. The framework assumes reliable orbital propagation via SGP4/SDP4 models and does not account for Doppler frequency shifts or inter-satellite link handover delays; future extensions include scalability to mega-constellations and integration of quantum-resistant privacy mechanisms. Full article

As the primary power transmission conduits, aircraft hydraulic pipelines are critical for actuating flight control surfaces and landing gear systems. Accurate in situ strain evaluation of these pipelines is essential, as installation-induced pre-loads directly compromise fatigue life and sealing performance, threatening overall system reliability. However, such evaluation is frequently hindered by the perspective distortions and limited depth of field inherent in conventional imaging systems. To overcome these metrological limitations, this study presents a novel virtual telecentric camera array system designed for high-precision, non-contact strain measurement. Unlike traditional pinhole models, the proposed system leverages a catadioptric setup with planar mirrors to create a virtual four-eye telecentric array from a single physical lens, ensuring constant magnification within the depth of field. A comprehensive simulation framework was established to rigorously compare the reprojection errors and scale accuracies between telecentric and pinhole projection models, quantitatively demonstrating the superior stability of the telecentric approach. Furthermore, a dedicated calibration strategy for non-overlapping telecentric fields of view was developed and validated. Experimental results from pipeline installation tests indicate a high concordance with strain gauge data, confirming that the proposed telecentric system effectively mitigates parallax errors and provides a robust solution for static and quasi-static micro-scale deformation monitoring in complex assembly environments. Full article

Understanding how seat-induced whole-body vibration (WBV) is transmitted to and actively compensated by the human body is essential for accurately assessing discomfort, fatigue, and postural control in vehicle occupants. This study proposes a visual–inertial fusion framework utilizing IMU-RGB-D data to isolate seated human body vibration in dynamic vehicular environments. In real-cabin monitoring systems, measured motion is a superposition of platform vibration, passive transmission through the body, active postural compensation, and camera jitter. Existing WBV and driver monitoring studies typically rely on single modality sensing, such as inertial or visual approaches, without decomposing these components or modelling camera vibration. The framework synchronized three IMUs with RGB-D landmarks. Seat, human body, and camera accelerations are separated, and body vibration velocity is derived from body–seat differential acceleration via band-pass filtering and spectral integration. The 3D landmarks enable rotational-translational Postural Compensation Index metrics, axis-wise energy distributions, and anthropometric consistency checks. The study is held in an in-service urban tram case. Torso vibration is dominated by 40% anteroposterior components, while head postural is predominantly > 50% lateral sway. Near static anthropometric evaluation was also studied, resulting in shoulder width errors that remain within ±10–20 mm. The results show that the framework can distinguish passive ride phases from strongly compensated phases, separate camera jitter from true body motion, and reveal anisotropic postural strategies, providing a structured basis for vibration and posture analysis in in-vehicle monitoring. Full article

With the increasing penetration of inverter-based photovoltaic (PV) generation, utility-scale grid-connected PV plants are frequently exposed to voltage regulation and voltage stability challenges driven by intermittent irradiance and limited reactive power flexibility under operating constraints. Conventional static Volt/VAR control schemes are typically designed for quasi-steady conditions and therefore struggle to respond to fast variations in PV output and network states. This paper presents a digital twin (DT)-enabled framework for dynamic Volt/VAR optimization in large PV plants. A four-layer DT architecture is developed to achieve real-time cyber-physical synchronization through multi-source data acquisition, secure transmission, fusion, and quality control. To balance model fidelity and computational efficiency, a hybrid physics–data-driven model is constructed, and a local voltage stability L-index is incorporated as an explicit security constraint. A multi-objective optimization problem is formulated to minimize node voltage deviations and reactive power losses while maximizing the static voltage stability margin. The problem is solved using an adaptive parameter particle swarm optimization (AP-PSO) algorithm with dynamic inertia and learning coefficients. Case studies on modified IEEE 33-bus and 53-bus systems demonstrate that the proposed method reduces the voltage profile index by up to 68.9%, improves the static voltage stability margin by 76.5%, and shortens optimization time by up to 30.3% compared with conventional control and representative meta-heuristic or learning-based baselines. The framework further shows good scalability and robustness under practical uncertainties, including irradiance forecast errors and measurement noise. Overall, the proposed approach provides a feasible pathway to enhance operational security and efficiency of grid-connected PV plants under high-penetration scenarios. Full article

The rapid integration of renewable energy sources (RES), particularly wind, together with fluctuating demand, has introduced significant uncertainty into power system operation, challenging traditional approaches for estimating Transmission Reliability Margin (TRM) and Available Transfer Capability (ATC). This paper proposes a fully adaptive TRM estimation framework that leverages rolling-window statistical analysis of net-load forecast errors to capture real-time uncertainty fluctuations. By continuously updating both the confidence factor and window length based on evolving forecast-error statistics, the method adapts to changing grid conditions. The framework is validated on the IEEE 30-bus system with 80 MW wind (42.3% penetration) and assessed for scalability on the IEEE 118-bus system (40.1% wind penetration). Comparative analysis against static TRM, fixed-confidence rolling-window, and Monte Carlo Simulation (MCS)-based methods shows that the proposed approach achieves 88.0% reliability coverage (vs. 81.8% for static TRM) while providing enhanced transfer capability for 31.5% of the operational day (7.5 h). Relative to MCS, it yields a 20.1% lower mean TRM and a 2.5% higher mean ATC, with an adaptation ratio of 18.8:1. Scalability assessment confirms preserved adaptation (12.4:1) with sub-linear computational scaling (1.82 ms to 3.61 ms for a 3.93× network size increase), enabling 1 min updates interval. Full article

This study addresses the challenge of annular gas migration control during the waiting-on-cement (WOC) period in managed pressure cementing for formations with narrow safe pressure windows. A dynamic pressure compensation optimization strategy is proposed by integrating a composite mechanistic model with experimental validation. Based on the hydration degree (T) model, a predictive model for static gel strength development was established. By coupling the gelation-induced suspension effect with cement slurry volumetric shrinkage, a static hydrostatic pressure decline model was developed. Experimental results indicate that the prediction errors of the proposed models are all within 7%, demonstrating improved accuracy compared with traditional empirical approaches and classical shear stress models. In addition, a testing methodology was developed to characterize pressure transmission efficiency during the WOC process, revealing its dynamic attenuation behavior. Experimental results show that when the static gel strength of anti-gas-migration cement slurry reaches 240 Pa, the pressure transmission efficiency ranges from 45% to 49%. Based on these findings, a wellhead backpressure calculation model incorporating the evolution of pressure transmission efficiency was established, providing a quantitative basis for annular pressure management during cement setting. Full article

Underwater optical wireless communication (UOWC) systems are significantly challenged by turbulence-induced signal degradation in dynamic channel conditions. This paper presents a novel framework that integrates Long Short-Term Memory (LSTM) networks with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to enable real-time turbulence prediction and multi-objective adaptive optimization of transmission parameters, including power, modulation scheme, and beam divergence. Experimental results demonstrate that the proposed LSTM–NSGA-II framework achieves a 45% reduction in bit error rate (BER) and a 36% improvement in energy efficiency compared to conventional static systems, while maintaining a signal-to-noise ratio (SNR) prediction accuracy of 94.7% and an adaptive response latency of 28.6 ms. Validation using field data from the Marine Institute in the Baltic Sea confirms the framework’s practical applicability and robustness, highlighting its potential to enhance autonomous and military underwater operations in turbulent environments. This work represents a significant step toward more reliable and efficient UOWC systems. Full article

To address the lack of reliable test data for evaluating star sensor performance in dynamic airborne environments, this paper presents a high-fidelity star map simulation method for all-time three-Field of View (FOV) star sensors. A comprehensive simulation framework integrating stellar radiation, atmospheric transmission, and detector noise models was developed to accurately model star trailing effects under dynamic conditions. First, a stellar position calculation model incorporating atmospheric refraction correction and platform motion parameters was established through coordinate transformations between the Geocentric Celestial Reference System (GCRS) and FOV coordinate system. Next, a complete energy transfer chain was constructed by combining star catalog data, atmospheric radiative properties, and detector noise characteristics. Finally, a quantitative evaluation system was introduced, employing metrics such as signal-to-noise ratio (SNR), total grayscale value (G_total), grayscale concentration index (GCI), and dynamic star displacement (DSD). Field experiments at 2388 m altitude (100.23°E, 26.86°N) demonstrated the average relative error of all evaluation metrics below 9% for static conditions and approximately 8% for dynamic scenarios between simulated and real star maps. The method effectively reproduces stellar radiation, atmospheric noise, and dynamic degradation, providing reliable simulation conditions for airborne star sensor testing and star trailing restoration algorithm development. Full article

The premium electric-vehicle market demands exceptionally quiet transmissions because the absence of engine masking makes gearbox noise more perceptible. Virtual NVH (noise, vibration, and harshness) evaluation requires coupling elastic deformation, gear–tooth contact, and vibration transmission through bearings and housing within a single environment. This study develops an integrated workflow in MSC ADAMS for predicting the NVH behavior of a 23/81-tooth helical gear pair. Finite element-based flank stiffness is imported, and a nonlinear contact model is applied to flexible teeth. Baseline simulation at 50 Nm and 200 rpm yields a static transmission error (TE) of 7.5 µm and a dynamic peak-to-peak TE of 0.7 µm, with the fundamental mesh tone at 77 Hz. Increasing tip relief by +0.10 mm lowers RMS TE by 31% and the first mesh order by 3.1 dB while raising the flank pressure from 1.65 GPa to 1.88 GPa. The workflow efficiently supports early-stage gear-noise optimization prior to the development of physical prototypes. Full article

With the comprehensive digitalization and electrification of aircraft, electromechanical actuation systems (EAS) have been increasingly applied. However, EAS are affected by various nonlinear factors, such as friction and mechanical backlash, which can compromise system stability and control accuracy, thereby reducing the operational lifespan of the EAS. This study focuses on these two nonlinear factors and proposes a hybrid control approach to mitigate their effects. In the speed loop of the EAS, a Super-Twisting sliding mode controller combined with a generalized proportional–integral observer (GPIO) is designed, while in the position loop, a hybrid controller integrating a radial basis function (RBF) neural network with sliding mode control is implemented. Leveraging the advantages of numerical analysis in SIMULINK and dynamic simulation in ADAMS, a co-simulation framework is established to evaluate the hybrid control algorithm under nonlinear effects. Furthermore, a control test bench for the control surface transmission system is constructed to analyze the dynamic and static performance of the system under different control strategies and input commands. The experimental results show that, compared with the PID control, the hybrid control method reduces the steady-state error and vibration amplitude of the step response displacement by 51% and 75%, respectively, and decreases the amplitude of speed fluctuations by 75%. For the sinusoidal response, the displacement lag is reduced by 76%, and the amplitude of speed fluctuations is reduced by 50%. Full article

Transmission error (TE) is an important parameter in gear dynamics that has a direct impact on the vibration and noise of gears. Under quasi-static conditions, gear elastic deformation and assembly errors amplify with increasing load, potentially contributing to noise and vibration. This paper presents a novel method for measuring the quasi-static transmission error (QSTE) of spur gears under quasi-static conditions. In particular, the study investigates the relationship between quasi-static transmission error, elastic deformation transmission error, and gear tangential acceleration. Gear elastic deformation transmission error was calculated from experimental data obtained with single-point, symmetrical dual-point, and orthogonal four-point configurations of tangential acceleration sensors. The orthogonal four-point sensor configuration greatly improves measurement accuracy when compared to theoretical values derived from material mechanics calculations. A dedicated on-machine acquisition system for spur gear tangential acceleration was constructed. Tangential acceleration tests were conducted across varying loads and rotational speeds. The acquired data underwent filtering and integration processing in order to obtain gear elastic deformation and quasi-static transmission error. The feasibility of the acceleration approach for measuring both gear elastic deformation and quasi-static transmission error is confirmed by a comparative analysis of the acceleration method results with transmission errors obtained via material mechanics calculations and magnetic grating detection. Full article

This paper aims to unveil the symmetry–asymmetry transition mechanisms in transient fault waveforms of offshore wind power AC/DC transmission systems, addressing the critical limitation of traditional simulation methods of the fact that they cannot characterize the dynamic evolution of system symmetry, such as static impedance adjustment failing to capture transient asymmetry caused by parameter imbalance or converter control. It proposes a fault waveform simulation approach integrating mechanism analysis, scenario extraction, and model optimization. Key contributions include clarifying the quantitative links between key system parameters like submarine cable capacitance and inductance and symmetry–asymmetry characteristics, defining the transient decay rate oscillation frequency and voltage peak as core indicators to quantify symmetry breaking intensity; classifying typical fault scenarios into a symmetry-breaking type with synchronous three-phase imbalance and a persistent asymmetry type with zero-sequence and negative-sequence distortion based on symmetry evolution dynamics and revising grid-connection test indices such as lowering the low-voltage ride-through threshold and specifying the voltage type for different test objectives; and constructing a simplified embedded RLC second-order model with symmetry–asymmetry constraints to reproduce the whole process of symmetric steady state–fault symmetry breaking–recovery symmetry reconstruction. Simulation results verify the method’s effectiveness, with symmetry indicator reproduction errors ≤ 5% and asymmetric feature fitting goodness R² ≥ 0.92, which confirms that the method can effectively reveal the symmetry–asymmetry mechanisms of offshore wind power fault transients and provides reliable technical support for improving offshore wind power fault simulation accuracy and grid-connection test reliability, laying a theoretical basis for the grid-connection testing of offshore wind turbines and promoting the stable operation of offshore wind power systems. Full article

This paper proposes a Fresnel-based Modified Moiré Varifocal Metalens (MMVL) addressing the inherent defocus at 0° rotation and significant focal quality degradation during varifocal operation in Traditional Moiré Varifocal Metalenses (TMVLs). The transmission function of the Fresnel-modified Moiré metalens combines a static term with a dynamic term, allowing the MMVLs to effectively overcome these limitations. Meanwhile, to minimize energy losses arising from polarization conversion and diffraction between the two metalenses, the nano-units on the metalenses are optimized by Particle Swarm Optimization (PSO) with FDTD simulations, maximizing the polarization conversion efficiency and transmittance. The simulation results demonstrate superior focal quality and stability in the MMVL throughout full rotational cycles, with super-diffraction-limited focusing maintained across all varifocal states. MMVLs have advantages in robustness; under axial distance variation (d = 0–20$d_0$, 0–3 μm), they maintain on-axis focus without deviation; with centering error (p = 0–10$p_0$, 0–3 μm), they sustain a clear focus at >36% efficiency. These results confirm that MMVLs have enhanced tolerance to manufacturing/assembly errors compared to TMVLs, delivering significantly stabilized optical performance. This advancement enables new possibilities for integrated micro-optics and optical tweezer applications. Full article