Search Results (2,187)

This paper proposes a super twisting observer-based fast terminal sliding mode control scheme with a novel dual-term reaching law for output voltage regulation of a DC–DC buck converter under disturbances. The proposed reaching law replaces the discontinuous sign function with a continuous hyperbolic tangent function, resolving the fundamental chattering–convergence trade-off inherent in classical reaching laws. The first term employs an exponential gain structure to accelerate convergence when system states are far from the sliding surface, while the second fractional-power term ensures rapid terminal-phase positioning in the vicinity of the surface. A fast terminal sliding surface is incorporated to further reduce settling time, and a super-twisting observer estimates matched disturbances arising from load and input voltage variations using only the output voltage measurement, eliminating the need for additional current sensors. Lyapunov-based stability proofs are provided for both the observer and the controller. Simulation results demonstrate that the proposed reaching law achieves a convergence time of 0.0403 s with the lowest tracking error among all compared methods: RMSE and MAE are reduced by 45.3% and 58.4%, respectively, relative to the exponential reaching law, while the chattering rate value decreases by 13.0%. Real-time experimental validation on a dSPACE1104 platform confirms that the proposed controller achieves fast reference voltage tracking, maintains tight output voltage regulation under load variations, and effectively rejects input voltage disturbances, demonstrating superior convergence speed, chattering attenuation, and disturbance rejection capability. Full article

Financial risk along the China–Pakistan Economic Corridor may combine fast firm-level scaling changes with slower joint-tail exposure. This study evaluates a leakage-controlled two-speed architecture for seven Pakistani equities during July 2021–June 2026. Causally timed, bounded-influence wavelet p-leader states feed quantile learners, while 13 prespecified directed hyperedges define a structural map. Evidence comprises 999 iterative amplitude-adjusted Fourier-transform (IAAFT) surrogates per node, 999 matched random edge sets, a 249-origin locked Value-at-Risk–Expected Shortfall test, learner-by-feature ablations, moving-block inference, and cost-adjusted portfolios. Robustification removes the legacy KEL spectrum anomaly; only LUCK rejects the IAAFT null after false-discovery-rate control (q = 0.009). No hyperedge has a confirmed increment beyond singleton conditionals and the pairwise-only benchmark. The map’s mean lift is 0.951 versus a placebo median of 0.989 (p = 0.834). The reservoir is competitive in point loss but indistinguishable from the conditional autoregressive Value-at-Risk benchmark (p = 0.420); its p-leader gain does not transfer across learners or survive KEL-block exclusion. The 0.606-percentage-point drawdown difference relative to historical conditional Value-at-Risk (CVaR) is imprecisely estimated (95% interval: [−7.652, 5.648]; p = 0.626). The framework supports robust multiscale measurement and transparent structural mapping, not general forecasting superiority, unique hyperedge information, or reliable portfolio protection. Full article

The increasing popularity of electric vehicles is inevitable due to their lower dependence on conventional fuel and reduced air pollution. Among various drivetrain architectures, four-wheel independently driven electric vehicles (4WID-EVs) have gained significant attention owing to their superior load-carrying and dynamic performance. However, the distributed four-motor architecture makes them vulnerable to unpredictable motor failures, necessitating an effective fault-tolerant control strategy. This work proposes a common sliding mode controller (CSMC)-integrated quantum complete graph neural network (QCGNN) for adaptive tuning under one-, two-, and three-motor failure conditions at reference speeds of 20 and 40 m/s, ensuring stable operation through continuous state feedback. Simulation results demonstrate fault recovery within 3 s, a rise time of 1.2–1.3 s, a settling time below 5.5 s, a peak overshoot below 8%, and a steady-state error below 0.2%. Compared with the QCGNN-Optimal LQR, the proposed QCGNN-CSMC reduces the Mean Absolute Error (MAE) from 34 to 18, Root Mean Square Error (RMSE) from 41 to 23, and the steady-state error from 1.07 to 0.56, while maintaining R² values above 0.90. Rapid controller prototyping further validates the robustness, reliability, and real-time applicability of the proposed fault-tolerant control framework for 4WID-EVs. Full article

Accurate wind power forecasting is essential for the stable and economic operation of power systems with high renewable penetration. Although machine learning models have been widely adopted for this task, the assumption that greater model complexity invariably yields superior forecasting accuracy has received insufficient scrutiny. This paper presents a systematic experimental investigation that covers two complementary stages, i.e., wind speed correction and wind power forecasting. For wind speed correction, we compare 10 machine learning methods, including spanning linear, instance-based, and tree-based ensemble learners, under four newly proposed progressively enriched feature configurations. For wind power forecasting, we benchmark 20 methods spanning traditional machine learning, time-series deep learning, and Transformer-based architectures on two geographically distinct wind farms. Our results reveal a clear task-dependent pattern. In wind speed correction, tree-based ensemble methods, particularly gradient boosting variants, consistently dominate, and feature engineering contributes more to accuracy gains than model selection. In wind power forecasting, deep learning architectures substantially and consistently outperform traditional methods, with attention-based models generalizing the most robustly across regimes and recurrent networks proving to be the most sensitive to regime shifts. These findings provide actionable task-specific guidance for model selection in operational wind power forecasting systems. Full article

Residual vibration of industrial manipulators can limit positioning efficiency and dynamic accuracy during high-speed motion. This study develops an integrated vibration-suppression framework for a rigid-link manipulator with flexible-joint dynamics. A controller-oriented rigid–flexible model with lumped disturbances is established, and a disturbance observer (DOB) is employed as the inner-loop compensation layer under a small-gain robustness constraint. On the compensated nominal model, partial eigenstructure assignment (PESA) selectively increases the damping of the retained flexible modes while preserving the rigid-body eigenstructure associated with trajectory tracking. A pose-dependent gain-scheduling mechanism further updates the PESA feedback gain to accommodate configuration-dependent modal-frequency variation. Numerical comparisons with conventional PID, standalone DOB, and standalone PESA demonstrate improved residual-vibration attenuation and settling behavior. Hardware tests on an Aubo i5 manipulator, with 16-channel responses directly acquired under the respective control configurations, further show an approximately 80% reduction in the representative low-frequency vibration amplitude relative to the PID baseline under the considered operating condition. Full article

This study proposes a hybrid ant colony optimization–backstepping control (ACO-BC) strategy for a multi-machine wind energy conversion system based on five-phase permanent magnet synchronous generators (PMSGs). The proposed strategy is developed to enhance the dynamic response, power regulation, robustness, and power quality of the system under variable wind-speed conditions. The ACO-ABC mechanism is employed to optimize the controller parameters, thereby reducing power fluctuations and improving the dynamic characteristics of the controlled system. The designed strategy is comprehensively evaluated against a developed sliding-mode control (DSMC) strategy under two distinct wind-speed operating scenarios using MATLAB 2021/Simulink. The simulation results demonstrate significant performance improvements with the proposed ACO-BC strategy. Under the first test, the active-power ripple and response time are reduced by 42.85% and 34.19%, respectively, compared with DSMC, while the active-power steady-state error (SSE) is reduced by 33.33%. Moreover, the current total harmonic distortion (THD) is reduced from 7.63% with DSMC to 2.32% with ACO-ABC, corresponding to a substantial reduction of 69.59%. Under the second test, the proposed strategy reduces the active-power ripple, response time, and SSE by 50%, 22.76%, and 66.67%, respectively, compared with DSMC. In addition, ACO-BC maintains the same overshoot level as DSMC while achieving substantial improvements in ripple, SSE, and dynamic response. These results demonstrate that the proposed strategy provides enhanced power regulation, faster dynamic response, reduced fluctuations, and improved current quality under both stepwise and randomly varying wind-speed conditions. Although the ACO-BC strategy involves higher implementation complexity and requires the tuning of multiple controller gains, its overall performance demonstrates its potential as an effective control solution for multi-machine PMSG-based wind energy conversion systems. Full article

Near-seabed contact operations with underwater hexapod robots can require one leg to execute a contact task, which reduces the stability of the remaining asymmetric five-legged support. To address this problem, this study proposes a five-legged asymmetric coordinated stability control method that integrates gait rhythm planning with attitude feedback. The method decouples the left middle leg from support, propulsion, and CPG phase evolution to form a ‘5+1’ asymmetric support base. This configuration creates an asymmetric support base intended for future task execution while the reserved leg is maintained in a prescribed safe/task posture in the present simulations. Meanwhile, a five-legged Hopf-CPG rhythm maintains continuous gait under asymmetric support. Low-bandwidth attitude feedback modulation is introduced between the CPG-generated foot trajectory and the inverse-kinematics input to balance attitude correction with foot-contact continuity. Full-degree-of-freedom Webots simulations, rather than physical near-seabed experiments, show that, at a longitudinal flow speed of 0.8 m/s, the method reduces combined attitude RMS by 41.52% relative to the no-feedback strategy. Relative to high-gain PD control, it reduces the RMS rate of change of the control output by 71.32%. These results demonstrate improved command smoothness and metric-level compatibility with the five-legged CPG rhythm under a prescribed task-reserved leg posture, with a moderate trade-off in transient attitude suppression. The method therefore provides a simulation-verified control strategy for balancing attitude regulation and gait rhythm continuity under asymmetric five-legged support. Full article

Blockchain consensus still forces a hard choice among scalability, security, and energy use. Redbelly, a leaderless Byzantine Fault Tolerance protocol, largely settles the scalability-versus-energy side of that tension, yet it accepts any cryptographically valid transaction without judging whether it is economically fraudulent or whether the node behind it is misbehaving. That blind spot is what we target. We present ML-Redbelly, a formally specified extension that attaches four learning components to the Redbelly pipeline: a LightGBM gradient-boosted fraud classifier, an Isolation Forest behavioural anomaly detector, a tabular Q-Learning agent for adaptive committee selection, and a Paillier-based federated learning aggregator that keeps model updates private. We prove that this layer leaves Redbelly’s safety and liveness intact, give pseudocode and complexity bounds for every component, and measure the system on the IEEE-CIS Fraud Detection benchmark (400,000 transactions) paired with a faithful discrete-event Redbelly simulator parameterised from measured inputs and validated against the published Redbelly deployment. LightGBM reaches an F1 of 0.783 (precision 0.858, recall 0.719, AUROC 0.963), a 34 percent relative F1 gain over the conference-baseline Random Forest at five times the inference speed. The Isolation Forest detector attains recall 0.885 at a false-positive rate of 0.047, and the Q-Learning agent settles into a stable policy within about 200 rounds across normal, bursty, and Byzantine-attack conditions. End to end, the framework sustains 48,844 TPS on 32 validators (mean over 30 seeds), and because the leaderless superblock commits every proposer’s block in parallel, this throughput advantage over leader-based BFT grows with the validator count (5.0 times PBFT and 2.9 times HotStuff at 32 validators). The learning layer costs only about 4 percent in throughput, since the measured ML inference is small next to the geo-distributed consensus round. Per-transaction energy is comparable across BFT protocols, being dominated by signature verification, and is orders of magnitude below proof-of-work chains, which expend energy on mining. One federated update epoch takes 36 s across 10 nodes with 2048-bit Paillier keys and reconstructs gradients with negligible error. All performance figures are emergent outputs of the discrete-event simulation, which reproduces the published Redbelly benchmark to within a conservative factor of about 1.7. Taken together, these results outline a simulation-validated design for making consensus intelligent as well as fast and identify the steps needed toward real-cluster deployment. Full article

Standard modeling methodologies and classic PI control strategies are poorly suited for doubly salient permanent-magnet (DSPM) motors because of their nonlinear magnetic characteristics and commutation-dependent operating behavior. This study establishes a control-oriented nonlinear model for a 12/8-pole DSPM with an internal radial permanent-magnet arrangement. Current- and position-dependent finite-element flux-linkage Jacobians, rotor-position derivatives, and total electromagnetic torque are organized as three-dimensional lookup tables in the stationary αβ frame, thereby retaining the effects of saturation, armature reaction, interphase coupling, and simultaneous nonzero phase currents. Fuzzy-PI control, implemented as a low-complexity nonlinear gain-scheduling approach, provides a practical alternative to computationally intensive advanced algorithms. To optimize the operational performance, a closed-loop control system is introduced, which utilizes an outer fuzzy-PI loop and an inner current hysteresis loop. This fuzzy-PI speed controller is then compared with a conventionally tuned PI controller under the same conditions. The results demonstrate the usefulness of the FEA-derived model for control evaluation and the favorable transient performance of the fuzzy-PI controller. Full article

Ti–6Al–4V alloy is widely used in aerospace and other high-performance engineering components, but its service reliability is often constrained by surface-initiated fatigue, fretting damage, wear, and corrosion. Ultrasonic surface rolling process (USRP) couples a static rolling force with high-frequency mechanical impacts to introduce severe plastic deformation while retaining relatively low surface roughness, thereby producing a gradient-strengthened surface layer. This review systematically summarizes advances in USRP strengthening of Ti–6Al–4V alloy within a “process–microstructure–surface integrity–service performance” framework. The effects of static load, ultrasonic amplitude and frequency, feed rate, spindle speed, processing passes, treatment temperature, and lubrication conditions are first compared. Particular attention is then paid to the mechanisms governing dislocation multiplication and rearrangement, grain subdivision, gradient nanostructure formation, the responses of the α and β phases, deformation-induced phase transformation, and the evolution of depth-dependent residual compressive stress. The intrinsic relationships between these mechanisms and surface roughness, hardness, strengthened layer depth, wear and corrosion resistance, fatigue performance, and fretting fatigue performance are subsequently clarified. Control strategies involving electropulsing, laser/temperature assistance, deep cryogenic treatment, and coating combinations are further reviewed, together with methods for contact dynamics analysis, residual stress prediction, and data-driven optimization. The combined evidence indicates that the performance gains from USRP are jointly controlled by surface defects, gradient microstructure, and residual compressive stress; excessive load, processing passes, or heat input may weaken or even reverse the fatigue benefit because of defect accumulation, gradient mismatch, and residual stress relaxation. Current limitations include inconsistent reporting of process parameters, difficulty in quantitatively separating the contributions of different strengthening mechanisms, insufficient investigation of residual stress stability, and limited validation on complex components. Full article

This paper compares Permanent-Magnet Synchronous Motor (PMSM) torque waveforms measured on a dynamometer test bench and generated by a real-time Hardware-in-the-Loop (HiL) simulation. A nonlinear PMSM model, parameterised using measurements from the physical motor and incorporating magnetic saturation through Look-Up Tables, was implemented on a dSpace HiL platform. Torque signals were evaluated at steady-state operating points of 20, 100, and 200 rpm using time-domain errors, Pearson and Spearman correlations, and FFT-based spectral analysis. An affine gain–offset transformation was applied to separate calibration-related discrepancies from differences caused by dynamic behaviour and unmodelled physical effects. The maximum observed initial discrepancy between dynamometer-measured and HiL-estimated torque was 0.638 Nm. Affine calibration reduced the maximum error by up to 85%, demonstrating that gain and offset mismatches were major contributors to the original error. However, correlation values remained low, and spectral analysis identified substantial differences in harmonic content. The dynamometer signals contained speed-dependent components, including a component near the 24th mechanical harmonic, whereas the HiL signals were dominated by the first mechanical harmonic. The proposed workflow supports mean-torque-oriented HiL validation and identifies priorities for improving model representation, channel scaling, and dynamic testing procedures. Full article

Background: Engaging in regular physical activity (PA) after completing structured exercise programmes may contribute to sustaining functional and cognitive gains in older adults over the long term. This study investigated whether different levels of PA during the one-year follow-up were associated with the preservation of the physical and cognitive gains achieved after a high-speed resistance training (HSRT) intervention lasting 16 weeks. Methods: Thirty-six community-dwelling older adults who had previously finished a supervised 16-week HSRT programme were monitored for one year. According to self-reported PA assessed at the one-year follow-up, participants were allocated to either a light activity group (LAG, n = 20) or a moderate-to-vigorous activity group (MVAG, n = 16). The following tests were used to assess physical function: six-minute walk test (6MWT), chair-stand test, seated medicine ball throw (SMBT), timed up-and-go test (TUG), and handgrip strength (HGS), whereas global cognitive function was assessed with the Mini-Mental State Examination (MMSE). Results: Exploratory between-group comparisons indicated that participants in the MVAG tended to show better chair-stand performance at the six-month (d_unb = 0.76 [0.09 to 1.45]) and one-year (d_unb = 1.02 [0.34 to 1.74]) assessments. SMBT performance declined similarly in both groups throughout follow-up (LAG: d_unb = −0.41 [−0.61 to −0.24]; MVAG: d_unb = −0.44 [−0.72 to −0.21]). Although the group × time interaction did not reach statistical significance (p = 0.079, η_p² = 0.088), exploratory analyses showed that declines in chair-stand and TUG performance occurred mainly in the LAG, pointing to possible patterns related to the maintenance of functional capacity. Cognitive performance decreased post-intervention in both groups, regardless of the PA level. Conclusions: Higher levels of self-reported PA assessed at the one-year follow-up were associated with more favourable trends in lower-limb functional performance. Full article

Reliable integration of wind energy into modern power grids requires control strategies capable of maintaining stable operation under stochastic wind conditions and grid-side disturbances. This paper presents an adaptive metaheuristic optimization and numerical modeling framework for robust multi-scenario tuning of proportional–integral controller parameters in a doubly fed induction generator (DFIG)-based wind-energy conversion system. The optimized control loops include the rotor-side converter, grid-side converter, rotor-speed loop, and DC-link voltage loop. Unlike conventional tuning approaches that rely on nominal operating points or limited deterministic cases, the proposed formulation evaluates each candidate controller over multiple operating scenarios, including start-up dynamics, step wind-speed variation, random wind fluctuation, and grid-voltage dip conditions. An Adaptive Whale Optimization Algorithm (AWOA) is developed by incorporating diversity-aware adaptation and stagnation-handling mechanisms into the standard WOA structure to improve the exploration–exploitation balance during the search process. The tuning objective combines aggregate transient-performance minimization with robustness-oriented scenario evaluation, thereby promoting controller gains that remain effective across uncertain operating conditions. Comparative numerical simulations against Grey Wolf Optimizer, Generalized Grey Wolf Optimizer, Moth-Flame Optimizer, and standard WOA show that the proposed AWOA achieves lower aggregate Integral Time Squared Error values across the considered cases. Convergence assessment, ablation analysis, and hold-out robustness testing further confirm the contribution of the adaptive mechanisms. Time-domain responses also demonstrate improved DC-link voltage regulation and reactive-power recovery under severe grid disturbances. These results indicate that the proposed framework can enhance the reliability and resilience of grid-connected DFIG wind-energy systems, supporting more robust and sustainable renewable-energy integration. Full article

In order to maintain stable formations of the underactuated unmanned surface vehicles (USVs) under ocean disturbances and reduce control chattering, a fuzzy adaptive super-twisting sliding mode control method combined with dynamic cooperative error correction is introduced in this paper. At the kinematic level, a dynamic cooperative error correction scheme is presented to include the relative positions of the neighboring vehicles. This results in a transition from independent tracking to interactive cooperation, thus improving the rigidity of the formation during maneuvers. Secondly, a composite inner-loop structure is designed by employing a nonlinear disturbance observer to counteract the effects of external loads. A fuzzy logic system is included to modify the super-twisting sliding mode gains in real-time. This approach effectively combines rapid error reduction with signal smoothness, addressing the trade-off between response speed and chatter suppression. Moreover, tracking differentiators and low-pass filters are utilized to obtain continuous control signals. The Lyapunov analysis indicates that the closed-loop system achieves semi-global uniform ultimate boundedness. Simulation results show that the proposed method can maintain high formation precision and obtain smooth control outputs with reduced chattering. Full article

Autonomous Vehicles (AVs) have been widely recognized as a promising solution to urban commuting congestion. However, quantitative evidence based on city-scale simulations of complete road networks in megacities remains limited. This study uses the complete urban road network of Beijing to investigate the influence of autonomous driving on commuting efficiency. Eleven autonomous vehicle penetration scenarios ranging from 0% to 100% at 10% intervals are established within the Simulation of Urban MObility (SUMO) microscopic traffic simulation platform. Human-driven vehicles are modeled using the Krauss car-following model, whereas autonomous vehicles are represented by the Cooperative Adaptive Cruise Control (CACC) model. The vehicle behavioral parameters are literature-based, adopted from published studies and open test data rather than calibrated against empirical Beijing traffic data, while the road network and commuting demand are constructed from Beijing-specific OpenStreetMap and mobile-signaling data. The simulation results reveal three major findings. First, autonomous driving exhibits a gradual efficiency transition over an approximate penetration range of 30% to 50% (identified qualitatively from the simulation trend rather than by a formal statistical change-point estimate). Below this threshold, behavioral heterogeneity between autonomous and human-driven vehicles intensifies traffic flow instability, whereas above it, the cooperative control capability of CACC becomes dominant and substantially improves overall network performance. Second, under full autonomous vehicle penetration, the city-wide average commuting speed increases from 7.20 m/s to 8.27 m/s, representing a 15% gain in the trip-weighted mean commuting speed (distinct from the 16% gain in the flow-weighted network speed reported in the Results), while the mean in-network simulated travel time per completed trip decreases from 561 s to 270 s. This travel-time value is an operational in-network measure and is not directly comparable to a full perceived door-to-door commute. Third, the efficiency benefits of autonomous driving display significant spatial heterogeneity. Speed improvements reach 16% to 20% on expressways and radial commuting corridors but remain between 4% and 8% on urban arterial roads. These findings indicate that the potential efficiency gains associated with autonomous driving, estimated here under fixed commuting demand and therefore as an upper bound, are constrained by the spatial characteristics of the road network. The results provide quantitative evidence supporting priority deployment of autonomous vehicles on expressways and major commuting corridors in megacities. Full article