Search Results (2,427)

To address the issues of slow dynamic response and poor disturbance rejection in three-phase interleaved parallel buck converters under disturbance conditions such as voltage or load transients, an improved sliding mode auto-disturbance rejection control (SM-ADRC) strategy is proposed. Firstly, the traditional ADRC algorithm suffers from reduced disturbance observation accuracy in the extended state observer (ESO) due to discontinuous switching of the nonlinear function at segment boundaries. To address this, a novel nonlinear function is designed using an interpolation fitting method. Concurrently, an improved ESO is constructed based on deviation-control principles, utilising the deviation between each state variable and its observed value. Secondly, an enhanced state error feedback law combines an improved exponential approach law with an integral sliding mode surface, thereby enhancing the control system’s robustness. Finally, simulation comparisons of output voltage fluctuations and power response speeds under various operating conditions validate the superiority and feasibility of the proposed SM-ADRC strategy over both the conventional ADRC strategy and PI control strategy. Full article

With the continuous development of the petroleum industry, bottomhole pressure prediction technology, which exerts a significant impact on oil production and recovery, has become a key research direction in the current oil and gas field. To enhance the accuracy and robustness of bottomhole pressure prediction under transient and variable operating conditions, a method based on data augmentation strategies and hyperparameter optimization was proposed in this paper. Addressing challenges such as limited data volume and significant disturbances in actual oilfield production, a data augmentation strategy incorporating noise perturbation and sliding windows was introduced to expand training samples and improve model generalization. In terms of model architecture, a deep network integrating CNN, BiGRU, and Multi-Head Attention mechanisms was proposed in this paper, which is referred to as the CNN-BiGRU-Multi-Head Attention model. By introducing Bayesian optimization for automatic hyperparameter search, the performance of the temporal model was further enhanced, achieving efficient extraction and dynamic focusing of wellbore pressure temporal features. Prediction results demonstrated that the proposed method outperforms existing mainstream forecasting models in metrics such as Mean Absolute Error (MAE) and Coefficient of Determination (R²), with R² reaching 0.9831, which confirms its strong generalization capability and engineering applicability. Practical guidance for intelligent oilfield production management and bottomhole pressure forecasting, along with a novel prediction method, is provided by this study, which holds significant importance for extending well life and stabilizing hydrocarbon production. Full article

This paper investigates the controllability of vessel berthing systems assisted by multiple tugboats under actuator faults or failures. In such interconnected systems, a failure of an individual tugboat can potentially compromise the berthing operation, or even lead to the collapse of the entire system. To address this challenge, the dynamic model of the multi-tug-assisted vessel system is first derived, followed by a controllability analysis under various fault scenarios to identify tolerable fault configurations. Then, a robust controller is proposed, integrating an adaptive disturbance observer with finite-time sliding mode control. This design ensures effective rejection of maritime environmental disturbances, practical finite-time stability, and bounded trajectory tracking errors. To accommodate different fault conditions, a switching control allocation strategy is developed to redistribute control efforts among the remaining healthy tugboats, thereby maintaining system reliability and efficiency. Simulation results under various faulty conditions demonstrate the effectiveness and robustness of the proposed control approach. Full article

This paper proposes a hybrid non-personalized temporal recommendation framework integrating Sliding-Window Weighted Popularity (SWWP) with Elite Evolutionary Discrete Particle Swarm Optimization (EEDPSO) to address the challenges of extreme data sparsity and temporal dynamics in global popularity-based recommendation. We first formally prove the NP hardness of the temporal-constrained recommendation problem, justifying the adoption of a metaheuristic approach. The proposed SWWP model employs a dual-scale sliding-window mechanism to balance short-term trend adaptation with long-term periodicity capture. A novel deep integration mechanism couples SWWP with EEDPSO through a “purchase heat” indicator, which guides temporal-aware particle initialization, position updates, and fitness evaluation. Extensive experiments on the Amazon Reviews dataset with extreme sparsity (density < 0.0005%) demonstrate that SWWP achieves an NDCG@20 of 0.245, outperforming nine temporal baselines by at least 13%. Furthermore, under a unified fitness function incorporating temporal prediction accuracy, the SWWP-EEDPSO framework achieves 5.95% higher fitness compared to vanilla EEDPSO, while significantly outperforming Differential Evolution and Genetic Algorithms. The temporally informed search strategy enables SWWP-EEDPSO to discover recommendations that better align with future user behavior, while maintaining sub-millisecond online query latency (0.52 ms) through offline precomputation and caching, demonstrating practical feasibility for deployment scenarios where periodic offline updates are acceptable. Full article

Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) is widely used for geohazard and infrastructure health assessment because it enables high-precision deformation monitoring. However, long-term time series observations often contain phase discontinuities caused by localized deformation with large spatial gradients, which can severely compromise phase unwrapping reliability. To address this limitation, we propose an Adaptive Shortest-Path Network (ASPN) method for GB-InSAR phase unwrapping. A temporal sliding window strategy is used to partition the acquisition stream into processing units. Within each unit, arc quality is quantified by least squares inversion using the mean square error (MSE) and temporal coherence. The unreliable arcs are removed, and the network is then reconnected using Dijkstra’s shortest-path algorithm to improve unwrapping stability and accuracy. The method is evaluated on a corner reflector-controlled deformation dataset and a stope slope dataset. In the controlled experiment, ASPN reduces the root mean square error (RMSE) of cumulative deformation from 1.684 mm to 0.037 mm, representing a 97.8% reduction, while in the stope slope experiment, it reduces the mean phase residual by 30.3% relative to the Delaunay network and by 11.6% relative to APSP. Overall, ASPN provides an efficient dynamic update mechanism and a robust, high-accuracy solution for long-term GB-InSAR time series deformation monitoring. Full article

Background/Objective: Traditional hip implant evaluations often overlook patient-specific dynamic loadings. This study investigates the performance of novel collared hip implant designs under walking conditions, focusing on geometric profiles and two common stem materials: Ti-6Al-4V and CoCr alloy. Methods: Patient-specific dynamic forces were applied using commercial finite element analysis, adhering to ISO and ASTM standards. Four cross-sectional profiles—circular, elliptical, oval, and trapezoidal—were initially evaluated for induced stresses and displacements. Subsequently, wear characteristics at implant junctions were analyzed, comparing CoCr (MC 1) and Ti-6Al-4V (MC 2) stems. The study also assessed the impact of using Ultra-High Molecular Weight Polyethylene (UHMWPE) acetabular cups. Results: The elliptical (CS 2) cross-sectional profile demonstrated superior performance. Junction analysis revealed that the CoCr stem (MC 1) exhibited a stem-to-head sliding distance four times higher and contact pressure 5.5 times higher than the Ti-6Al-4V stem (MC 2). Specifically, MC 1 showed 82% higher contact pressure and 89% greater sliding distance at the stem–head junction compared to MC 2. Additionally, utilizing UHMWPE cups effectively eliminated squeaking sounds attributed to CoCr cups due to superior wear resistance. Conclusions: The combination of an elliptical (CS 2) cross-sectional profile with a Ti-6Al-4V stem and UHMWPE acetabular cup offers optimal performance. This configuration significantly reduces wear and contact pressure, suggesting enhanced functionality and durability for hip implants under dynamic loading conditions. Full article

Dam-break flows over erodible beds represent a complex fluid–solid interaction problem characterized by extreme turbulence and rapid morphological changes. This study investigates the dynamics of such flows over inclined granular beds by integrating an advanced Moving Particle Semi-implicit (MPS) method. To accurately resolve the transition between static and kinetic granular regimes, I introduce a state-dependent tangential friction model that explicitly distinguishes between sticking and sliding conditions based on local force balance. Furthermore, the momentum exchange between the fluid and solid phases is rigorously modeled using the porosity-dependent drag formulation. The numerical results demonstrate a distinct regime shift in energy dissipation: while low-inclination beds (0–$4\%$) promote distributed sediment transport, steep-inclination beds (8–$12\%$) trigger a localized “Shielding Effect”. In this regime, the surge’s horizontal kinetic energy is rapidly converted into vertical potential energy and frictional work, forming a deep sacrificial scour hole that acts as a topographical energy sink. This mechanism effectively mitigates the destructive potential of the surge in downstream areas. The proposed method provides a robust tool for predicting morphological feedback and designing topographical countermeasures for disaster mitigation in hydraulic and coastal environments. Full article

This paper proposes an adaptive wavelet neural network nonsingular fast terminal sliding mode control strategy based on a finite-time framework for a space robot system under external disturbances and model uncertainties. Firstly, the dynamic model of space robot is established based on the second Lagrange equation. Unlike sliding mode control, which converges asymptotically, terminal sliding mode control (TSMC) has been proposed to ensure finite-time convergence for a space robot system. Based on the aforementioned TSMC framework, the fast terminal sliding mode control (FTSMC) is proposed to enhance system convergence rate. However, TSMC exhibits a singularity issue attributed to the presence of negative fractional order. To avoid this issue, a nonsingular fast terminal sliding mode controller (NFTSMC) has been proposed. The controller is designed to integrate linear and nonlinear terms into a novel nonsingular fast terminal sliding mode surface. The method achieves fast finite-time convergence concurrently with improved robustness, while effectively avoiding singularities. To compensate for external disturbances and model uncertainties in the space robot system, this paper proposes the combination of wavelet neural network (WNN) for the real-time estimation of lumped uncertainties. Network parameters are dynamically adjusted via an adaptive law to mitigate chattering effectively and enhance trajectory tracking precision. Utilizing Lyapunov stability theory and numerical simulations, the space robot system’s stability is rigorously proven and the controller effectiveness is validated. Compared with the traditional NFTSMC, the proposed control strategy reduces the convergence time by 20.74%. In the case of trajectory tracking comparison, the root mean square error (RMSE) improves by 35.85%, the mean tracking error improves by 63.29%, the integral of absolute error (IAE) improves by 29.37%, and the integral of time-weighted absolute error (ITAE) improves by 93.06%. Additionally, a comparative simulation with RBFNN is included in this paper. Compared with RBFNN, the proposed control strategy reduces input torque energy consumption by 77.36% and improves control smoothness by 87.03%, quantitatively demonstrating the effectiveness of the proposed control strategy. Full article

The actuator reliability of Steer-by-Wire (SBW) systems is critical to the functional safety of autonomous vehicles. However, existing control methods struggle to simultaneously enhance both response speed and fault-tolerant performance when facing multiple uncertainties such as parameter perturbations, external disturbances, and actuator faults. To address these issues, this paper proposes a synergistic fault-tolerant control (FTC) strategy combining guaranteed cost control (GCC) and integral sliding mode control (ISMC). First, a dynamic model of the SBW system incorporating the multiple uncertainties is established. Second, a GCC law is derived based on linear matrix inequalities (LMIs) to impose strict constraints on the system’s tracking accuracy and robustness. Building upon this, an ISMC is integrated to significantly accelerate the system’s dynamic response without sacrificing steady-state accuracy, thereby forming a synergistic fault-tolerant architecture characterized by both high precision and rapid response. The results indicate that, under typical fault modes and steering conditions, the response speed of GCC+ISMC is significantly improved compared with GCC alone, and the GCC+ISMC reduces tracking errors by approximately 35% compared to adaptive integral sliding mode control (AISMC). These findings demonstrate that the proposed approach effectively mitigates multiple system uncertainties, offering comprehensive advantages in tracking accuracy, response speed, and robustness. Full article

Surface and interface science play an important role in the tribological properties of materials. Recently, research in this field has extended from the macroscopic scale to the molecular level to elucidate energy dissipation and structural evolution mechanisms at sliding interfaces. In this work, we propose a nanolubricant strategy based on carbon nanocages (CNCs). Three types of lubricating molecules—oleylamine (amine), oleic acid (carboxyl), and stearyl alcohol (hydroxyl)—were encapsulated into a polytetrafluoroethylene (PTFE) matrix to construct a composite tribological interface model. Molecular dynamics simulations were employed to investigate the interfacial enrichment, diffusion, and interaction mechanisms of these molecules with PTFE chains and the Fe counterface. Particular emphasis was placed on how different functional groups regulate energy transfer and dissipation pathways. This study deepens the molecular–level understanding of structure–lubrication relationships and provides theoretical guidance for designing high–performance polymer–based tribological materials. Full article

Despite the widespread deployment of quadrotor unmanned aerial vehicles (UAVs), ensuring their flight stability under asymmetric environmental disturbances, such as concurrent wind and rain, remains a significant challenge. To address the trajectory tracking problem under these severe conditions, this paper proposes a Composite Continuous Rapid Nonsingular Adaptive Terminal Sliding Mode (CCRNATSM) control strategy. First, a composite dynamic model is developed, integrating wind aerodynamics with rain impact characteristics to accurately simulate realistic flight environments. A High-Order Sliding Mode Observer (HOSMO) is then employed for the real-time, accurate estimation of these lumped disturbances. Subsequently, this observer is integrated with an adaptive control law to ensure rapid and precise system stabilization. Comparative simulations conducted under strong disturbance conditions demonstrate that the proposed method exhibits superior performance over existing strategies, reducing roll angle deviation by 75% and shortening the recovery time to 1.5 s. Ultimately, this control strategy significantly enhances the robustness and safety of quadrotor UAVs operating in harsh, asymmetric environments. Full article

Tea garden irrigation suffers from time delays, nonlinear interference, and phenological biomass fluctuations caused by plucking, leading to the failure of traditional Proportional–Integral–Derivative (PID) and fixed-threshold models in precise water supply. This study proposes a precision irrigation system for smart tea gardens integrating Phenology-Aware Collaborative Decision-Making and an Adaptive Gain Predictive Super-Twisting Sliding Mode Control (AG-PSTC) algorithm. A “temperature–time–water” phenological reference model was constructed, and Crop Water Stress Index (CWSI) was introduced to decouple shoot density changes into phenology-driven and water stress components, realizing dynamic target soil moisture (W_target) setting. The AG-PSTC algorithm combined an improved Smith predictor for phase compensation and a barrier function-based adaptive super-twisting term for chattering elimination and finite-time convergence. Simulations showed AG-PSTC reduced rise time by 78% and steady-state error by four orders of magnitude compared with PID, with robust performance under ±40% time-delay perturbation. Field tests confirmed the system suppressed false irrigation during plucking, with soil moisture standard deviation within 1.51%. This study provides a vertical integration framework from crop physiological models to precision control, promoting the transition of tea garden irrigation from experience-based to demand-based. Full article

Electric vehicle(EV) diffusion exhibits nonlinear, path-dependent dynamics shaped by interacting economic, technological, and social constraints. This paper develops a unified hybrid systems framework that captures these complexities by integrating microfounded household choice, capacity-constrained firm behavior, local network spillovers, and multi-level policy intervention within a Filippov differential-inclusion structure. Households face heterogeneous preferences, liquidity limits, and network-mediated moral and informational influences; firms invest irreversibly under learning-by-doing and profitability thresholds; and national and local governments implement distinct financial and infrastructure policies subject to budget constraints. The resulting aggregate adoption dynamics feature endogenous switching, sliding modes at economic bottlenecks, network-amplified tipping, and hysteresis arising from irreversible investment. We establish conditions for the existence of Filippov solutions, derive network-dependent tipping thresholds, characterize sliding regimes at capacity and liquidity constraints, and show how network structure magnifies hysteresis and shapes the effectiveness of local versus national policy. Optimal-control analysis further demonstrates that national subsidies follow bang–bang patterns and that network-targeted local interventions minimize the fiscal cost of achieving regional tipping. Beyond theoretical characterization, the framework is structurally calibrated to match the order-of-magnitude effects reported in leading empirical and simulation-based studies, including network diffusion models, agent-based simulations, bass-type specifications, and fuel-price shock analyses. The hybrid formulation reproduces short-run percentage-point subsidy effects, long-run forecast dispersion under alternative network assumptions, and policy-induced equilibrium shifts observed in the applied literature while providing a unified geometric interpretation of these heterogeneous results through explicit basin boundaries and regime switching. The framework provides a complex systems perspective on sustainable mobility transitions and clarifies why identical national policies can generate asynchronous regional outcomes. These results offer theoretical foundations for designing coordinated, cost-effective, and network-aware EV transition strategies. Full article

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

Master–slave bilateral teleoperation systems face several practical challenges, including model uncertainties, time-varying communication delays, and environment-induced force disturbances. To address these issues, this paper proposes an adaptive super-twisting sliding-mode control scheme to achieve high-precision position tracking and real-time force-feedback synchronization. First, joint-space dynamic models are established for both the master and the slave manipulators, and a passive impedance model is adopted to characterize the interaction dynamics at the operator–master and environment–slave interfaces. Second, to attenuate measurement noise in the environment interaction force, a first-order low-pass filter is used to preprocess the raw force measurements, and a radial basis function neural network (RBFNN) is employed to approximate the environment torque online. Furthermore, a super-twisting sliding-mode controller is developed and combined with an adaptive law to compensate online for system uncertainties, including dynamic parameter variations and environment-induced force disturbances. The stability of the resulting closed-loop system is rigorously analyzed using Lyapunov stability theory. Finally, the effectiveness of the proposed method is validated through numerical simulations, virtual experiments conducted in the MuJoCo physics engine, and real-world hardware experiments. The results show that the proposed strategy achieves accurate position synchronization and force tracking while maintaining stable haptic interaction in the presence of bounded time-varying delays, parameter uncertainties, and external disturbances. Full article