Search Results (3,408)

One of the major challenges in autonomous path following for unmanned surface vehicles (USVs) is the impact of stochastic environmental forces—primarily wind, waves and currents—which introduce nonlinearities that affect control models. Conventional strategies often rely on minimizing cross-track error, resulting in a reactive system that corrects heading only after a disturbance has displaced the vessel, potentially leading to oscillatory behavior and reduced precision. Deep Reinforcement Learning (DRL) is successfully used for a wide range of nonlinear control tasks. It has already been shown that robust solutions that can handle disturbances such as sensor noise or changes in system dynamics can be obtained. This study investigates whether an agent, provided it can explicitly observe disturbances, can go beyond simply correcting deviations and autonomously learn the correlation between environmental conditions and necessary counter-forces. We show that integrating the wind vector directly into the agent’s observation space allows a Proximal Policy Optimization (PPO) policy to decouple the environmental cause from the kinematic effect, facilitating drift compensation before significant errors accumulate. By systematically comparing agents trained with randomized wind scenarios, we found that agents that can observe the wind can achieve goal reaching rates of up to 99.0% and reduce the spread of path deviation and velocity in our tested scenarios. Furthermore, our results quantify a distinct Pareto frontier between navigational velocity and tracking precision, demonstrating that explicit disturbance perception improves consistency, although robust implicit training already provides substantial resilience. These findings indicate that augmenting state observations with environmental data enhances the stability of learning-based controllers. Full article

The Four-Leg Three-Phase Voltage Source Inverter (4LVSI) is a versatile solution for integrating renewable energy sources (RESs) into distribution networks, as it compensates unbalanced voltages and currents while providing a path for zero-sequence components. Accurate current control is essential to ensure power quality and reliable operation under these conditions. Conventional controllers such as proportional–integral, resonant, or feedback-linearization methods achieve acceptable tracking under static dc-link conditions, but their performance degrades when dc-link voltage dynamics arise due to renewable-source fluctuations. This paper proposes a data-driven model reference neural control (MRNC) strategy for a four-leg inverter connected to RESs, explicitly accounting for dc-link voltage variations. The proposed controller reformulates the classical Model Reference Adaptive Control (MRAC) as a lightweight single-layer neural network whose adaptive weights are updated online using the Recursive Least Squares (RLS) algorithm. In this framework, the dc-link variations are not modeled explicitly but are implicitly learned through the data-driven adaptation process, as their influence is captured in the neural network regressors formed from real-time input–output measurements. This allows the controller to continuously identify the inverter dynamics and compensate the effect of dc-link fluctuations without requiring additional observers or prior modeling. The proposed approach is validated through detailed time-domain simulations and real-time Hardware-in-the-Loop (HIL) experiments implemented at a 10 kHz switching frequency. The results indicated that the RLS-based MRNC controller achieved the lowest steady-state current error, reducing it by approximately 1.85% and 1% compared to the Proportional-Resonant (PR) and One-Step-Ahead (OSAC) controllers, respectively. Moreover, under dc-link voltage variations, the proposed controller significantly reduced the current overshoot, achieving decreases of 5.9 A and 6.36 A relative to the PR controller. Full article

Systematic radiometric anomalies, manifesting as non-physical range-direction oscillations, significantly compromise the quality of Miniature Radio Frequency (Mini-RF) S-band SAR imagery and its scientific application in the lunar south polar region. In this study, we analyzed 1262 scenes from the Mini-RF archive in south polar regions. By employing a statistical screening method based on fitting the relationship of backscattering signal and off-nadir angle, 377 scenes (29.9%) were identified as radiometrically anomalous scenes with systematic errors. To correct these errors, a physics-based radiometric correction framework has been proposed by reconstructing the effective antenna gain pattern (AGP) of Mini-RF. Referenced relationship between the backscattering signal and the local incidence angle was established using normal scenes. For each anomalous scene, a simulation-driven gradient descent optimization approach is developed to estimate the offset of the AGP. Subsequently, the derived offset is applied to realign the AGP of the anomalous scene, effectively compensating for the systematic range-direction oscillations and restoring the true backscatter intensity. Using the proposed method, systematic errors in anomalous scenes have been eliminated effectively, reducing the Root Mean Square Error (RMSE) relative to the reference radiometric curve from 2.11 to 1.21 and decreasing the image entropy from 2.83 to 2.29. By eliminating systematic banding artifacts, the proposed method has significantly improved the radiometric fidelity of Mini-RF data. Furthermore, a temporal periodicity was found in the gain offsets, suggesting dynamic instrument distortion driven by variations in the orbital thermal environment. Full article

Underwater vehicles performing sampling tasks often encounter significant buoyancy variations due to payload adjustments and environmental changes, which severely challenge the stability and accuracy of controllers. To address this issue, this paper proposes a hybrid control framework that integrates Proximal Policy Optimization (PPO) with adaptive PID tuning. The framework employs PPO to dynamically adjust PID parameters online while incorporating output saturation, stepwise quantization, and dead zone filtering to ensure control safety and actuator longevity. A dual-error state representation—combining instantaneous error and its derivative—along with actuator command buffering is introduced to compensate for system lag and inertia. Comparative simulations and experimental tests demonstrate that the proposed method achieves faster convergence, lower steady-state error, and smoother control signals compared to both conventional PID and pure PPO-based control. The framework is validated through pool tests and field trials, confirming its robustness under realistic hydrodynamic disturbances. This work provides a practical and safe solution for adaptive depth control of sampling-capable AUVs operating in dynamic underwater environments. Full article

Phase unwrapping is the decisive factor for achieving dimensional accuracy in phase-shifting profilometry, yet unavoidable phase jumps occur at discontinuities. Existing dual-frequency heterodyne techniques suffer from a narrow measurement range and overly coarse projected fringes due to grating superposition requirements, leading to large errors when scanning objects with hole-like features. To address these issues, this paper proposes an edge-oriented phase-unwrapping error-compensation method based on fringe projection phase profilometry. First, the wrapped phase of the measured object is acquired via phase-shifting profiling. The wrapped phase map is then smoothed at multiple scales using Gaussian filters, and parallel Canny edge detection combined with phase gradient thresholding is applied to comprehensively capture both coarse and fine discontinuities. Morphological closing fills in breakpoints, followed by skeleton thinning and connectivity reconstruction to generate an edge band of defined width. Within this band, edge-preserving smoothing is performed using guided filtering or bilateral filtering, and the result is fused with the original phase through Gaussian weighting based on the distance to the skeleton. Finally, an ordered multi-frequency heterodyne unwrapping restores the absolute phase, maximally preserving true discontinuities while effectively correcting noise and detection errors. Experiments show that this method overcomes edge-induced phase jumps—with jump-error correction rates exceeding 96.7%—exhibits strong noise resilience under various conditions, and achieves measurement precision better than 0.06 mm. Full article

The accuracy of online parameter identification for permanent magnet synchronous motors (PMSMs) is constrained by discrete model errors, rank deficiency in the steady-state identification matrix, and voltage deviations resulting from inverter nonlinearities. This paper proposes a multi-parameter identification method acting as a high-precision virtual sensor, based on Zero-Order Hold (ZOH) discretization and an inverter nonlinear voltage compensation scheme utilizing a dual-sampling strategy. First, a discrete model of the PMSM, accounting for rotor position variations within the control period, is established using the ZOH discretization method. Compared with the forward Euler discretization method, this approach effectively minimizes discretization model errors, especially under high-speed operating conditions where rotor position variations are significant. Second, the rank deficiency problem of the steady-state identification matrix is overcome by combining d-axis small-signal injection with a dual-sampling strategy. Furthermore, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is introduced to achieve online multi-parameter identification. Finally, the influence mechanisms of the dead-time effect, power switch voltage drop, and turn-on delay on the output voltage are analyzed. Consequently, an inverter nonlinear voltage compensation strategy tailored for the dual-sampling mode is proposed. Experimental results demonstrate that the proposed method significantly enhances parameter identification accuracy across the entire speed range. Specifically, under high-speed conditions, the identification errors for resistance, inductance, and flux linkage are maintained within 5.47%, 4.05%, and 2.46%, respectively. Full article

The high integration of renewables like distributed photovoltaic (PV) into medium- and low-voltage distribution networks causes bidirectional power flows, increased voltage fluctuations, and operational uncertainty. Traditional power flow models struggle to balance efficiency and accuracy for multi-period optimization. This paper proposes a dual-objective voltage optimization method based on a Holomorphic Embedding time-series power flow model. First, a recursive relationship for nodal voltage power series expansion is derived, revealing the linear superposition of first-order coefficients with power injection changes and the rapid decay of higher-order terms. A linearized analytical model neglecting higher-order terms is built, improving the computational efficiency of time-series power flow calculations while maintaining accuracy. Then, integrating energy storage systems and static var compensators, a dual-objective optimization model minimizing voltage deviation and daily operational cost is formulated. Tests on a practical 91-node rural distribution system show that the proposed power flow model maintains a voltage error below 0.25% compared to the Newton–Raphson method across various PV integration scenarios, and the optimization reduces computation time by about 61.3% versus the Second-Order Cone Programming method, validating its advantages in precision and efficiency for balancing voltage quality and economy. Full article

To provide information support for relay protection and emergency automation algorithms, electromagnetic measuring voltage and current transformers are most often used. As practice shows, the magnetic core of the current transformers can be saturated under transient processes. This negatively impacts the proper functioning of protection systems. This paper proposes a methodology for restoration of the current transformers’ secondary current based on machine learning algorithms. The task of current restoration is reduced to clustering and regression problems. The groups’ current data are clustered depending on the depth of core saturation and the shape of current distortion. Then, solving the regression problem, current restoration is performed. Considering the requirements for the performance of the protection system, the following machine learning algorithms were selected for current recovery: Decision Tree, Random Forest, XGBoost, and Support Vector Machine for regression problems. The results of computational experiments show that the optimal number of clusters is four. Among the current restoration algorithms, XGBoost proved to be the most suitable. On average, for 17,240 test saturation modes, its error was 4%. The time delay for restoration one saturation mode was 0.0067 ms. Full article

Non-line-of-sight (NLOS) propagation remains a major obstacle to high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. For error mitigation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to dynamically compensate for NLOS errors. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results show that the proposed framework achieves state-of-the-art–comparable performance with improved model efficiency in complex indoor UWB positioning scenarios. Full article

To address the precision and motion-smoothing challenges in path planning for spherical workpieces without Computer-Aided Design (CAD) models, this paper proposes a robust point-cloud-driven framework. Conventional Principal Component Analysis (PCA) alignment suffers from centroid shift errors due to asymmetric data loss from light-absorbing surface features. To solve this, a RANSAC-compensated hybrid PCA algorithm is developed to decouple position and orientation estimation, ensuring stable coordinate alignment despite incomplete data. Furthermore, to resolve the geometric collapse and kinematic jitter caused by traditional planar slicing in high-curvature polar regions, a spherical latitudinal equiangular conical slicing algorithm is introduced. By aligning the slicing planes with the sphere’s radial geometry, the method preserves topological accuracy while maintaining an optimal point density for smooth robotic execution. Experimental results on rubber ball groove processing demonstrate a repeat positioning accuracy of 0.09 mm and a feature coverage of 95.21%. This research provides a scientifically rigorous and computationally efficient solution for the automated processing of complex spherical surfaces. Full article

Sub-band errors are inevitable in synthetic bandwidth synthetic aperture radar (SAR) systems due to differences in signal paths and frequency responses of the components used for different sub-bands, which degrade imaging performance if not properly compensated. In this paper, a prominent-reflector-based sub-band error estimation method is proposed for synthetic bandwidth SAR. Based on the analysis of the sources and impacts of sub-band errors, the proposed method estimates and compensates the errors in three steps, corresponding to time-delay error, amplitude error, and phase error. By leveraging the stable reflective properties of prominent reflectors in the scene, the proposed method directly derives sub-band error estimates from focused sub-band images in the time domain. Compared to existing methods, the proposed method achieved robust, high-accuracy performance while requiring less execution time. The effectiveness and efficiency of the proposed method are validated using real data collected by a Ka-band synthetic bandwidth SAR system. Full article

Background: Augmented reality (AR) and mixed reality (MR) promise to enhance anatomical understanding, spatial orientation, and workflow in cardiac surgery. Their clinical adoption remains limited and the translational path is incompletely defined. Methods: A PubMed search was conducted by two independent reviewers from database inception through July 2025 and identified peer-reviewed, English-language articles describing peri- or intra-operative AR/MR applications in cardiac surgery. Relevant clinical, preclinical, technical, and review articles were selected for inclusion based on scope and content. Given the narrative approach and heterogeneity across studies, findings were synthesized qualitatively into application domains. Results: Fourteen studies were included. Five domains emerged: (1) preoperative planning and patient-specific modelling—MR enhanced spatial orientation and planning for minimally invasive and valve procedures; (2) intraoperative navigation and visualization—AR improved targeting and interpretation with preclinical overlay errors ≈ 5 mm; (3) physiological/functional guidance—thermographic AR detected ischemia in vivo with strong correlation to invasive thermometry; (4) robotic integration and workflow optimization—AR-guided port placement and stepwise robotic adoption supported the feasibility of totally endoscopic CABG; (5) AR-based early rehabilitation. Conclusions: Early clinical and preclinical evidence supports AR/MR feasibility and utility for visualization and orientation in cardiac surgery. Priorities include deformable, motion-compensated registration, ergonomic integration with robotic platforms, and multicentre trials powered for operative efficiency and patient outcomes. 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

Accurate photovoltaic (PV) power forecasting under extreme weather conditions remains challenging due to the non-stationary and multi-modal nature of meteorological influences. This study proposes a novel four-stage learning framework integrating signal decomposition, hyperparameter optimization, temporal dependency learning, and residual compensation to enhance forecasting resilience during El Niño–Southern Oscillation (ENSO) climate transitions. The framework employs CEEMDAN for fluctuation mode decoupling, TOC for global hyperparameter optimization, Transformer model for spatiotemporal dependency learning, and EEMD-GRU for error correction. Experimental validation utilized a comprehensive dataset from Australia’s Yulara power station comprising 104,269 samples at 5 min resolution throughout 2024, covering a complete ENSO transition period. Compared against baseline Transformer model and CNN-BiLSTM models, the proposed framework achieved nRMSE of 1.08%, 7.04%, and 2.81% under sunny, rainy, and sandstorm conditions, respectively, with corresponding R² values of 0.99981, 0.99782, and 0.99947. Cross-year validation (2023 to 2025) demonstrated maintained performance with nRMSE ranging from 4.68% to 15.88% across different temporal splits. The framework’s modular architecture enables targeted handling of distinct physical processes governing different weather regimes, providing a structured approach for climate-resilient PV forecasting that maintains 2.56% energy consistency error while adapting to rapid meteorological shifts. Full article

This paper proposes an iterative learning framework for a class of fractional-order nonlinear multi-agent systems operating under directed iteration-varying switching topologies. To suppress trial-to-trial fluctuations in initial states, a P-type initial condition learning mechanism is integrated into the update law, enabling each agent to actively compensate for its own startup offset in each iteration. The study designs a distributed iterative learning protocol using only local neighbor information, and this protocol can simultaneously achieve fault estimation and diagnosis. By constructing a fractional-order system model and adopting the contraction-mapping analysis method, sufficient conditions are derived in this paper, which guarantee that both the fault error and initial condition error converge asymptotically to zero as the number of iterations approaches infinity. The proposed scheme, based on iterative learning fault estimation, can effectively handle unknown nonlinearities without relying on an accurate system model. Numerical simulation results further verify the effectiveness of the designed fault observer in achieving fault estimation. Full article