Search Results (3,637)

Background/Objectives: The aim of this study was to evaluate the effect of intraoperative gaseous ozone applied to the implant osteotomy immediately before implant placement on early implant stability using a split-mouth design and resonance frequency analysis (RFA). Methods: This prospective, randomized controlled split-mouth study included 40 patients receiving 106 implants in bilaterally symmetrical edentulous sites. Patients and outcome assessors were blinded. In the ozone group, ozone gas was applied to the implant site for 60 s immediately before implant placement; the control group underwent the same surgical protocol without ozone application. The primary outcome was the change in implant stability quotient from baseline to three months (ΔISQ). Secondary outcomes included baseline and three-month ISQ values, insertion torque, and postoperative pain assessed using a visual analog scale. Results: All 106 implants were analyzed. No significant between-group differences were observed in baseline ISQ (β = −0.49; 95% CI: −2.73 to 1.75; p = 0.668) or insertion torque (β = −0.62 Ncm; 95% CI: −1.96 to 0.72; p = 0.362). At three months, ISQ was higher in the ozone group (β = 1.91; 95% CI: 0.75 to 3.06; p = 0.001). ΔISQ was also significantly greater in the ozone group (β = 2.40; 95% CI: 0.34 to 4.45; p = 0.022; Cohen’s d = 0.42). Implant stability increased significantly in both groups (both p < 0.001). Postoperative pain did not differ between groups (β = −0.23; 95% CI: −0.83 to 0.36; p = 0.446). Although the effect direction was consistent across sensitivity analyses, ΔISQ was not statistically significant in the patient-level paired analysis (p = 0.089). No adverse events were reported. Conclusions: Intraoperative ozone application was associated with a modest increase in early implant stability without a significant difference in postoperative pain. However, given the small effect size and sensitivity of the findings to the analytical method, further large-scale, multicenter randomized controlled trials are required. Clinically, intraoperative ozone may be considered a potential adjunct to standard implant placement protocols, but the current evidence is insufficient to support its routine use. Full article

Accurate actuator modeling is critical for robust design validation and sim-to-real control transfer in humanoid robotics. Yet, in practice, developers rely on simplified actuator models built from sparse datasheets or offline system identification, which often omit internal control logic, saturation, sensor dynamics, and electromechanical actuator dynamics. This limits model fidelity under changing conditions and contributes to sim-to-real failures. We propose actuator Digital Twins (DTs) as a scalable solution for predictive simulation. In this work, predictive simulation is defined as the forward computation of joint position and actuator torque from prescribed reference trajectories, controller parameters, mechanical configuration, and initial conditions, with prediction accuracy evaluated against measurements from the physical actuator. We validate a DT of the Pulsar PULSE115 quasi-direct-drive actuator that reproduces the actuator electromechanical dynamics, physical operating limits, sensing characteristics, and embedded cascaded controller executed at 10 kHz on a 1-DOF pendulum testbed, comparing real-world experiments with simulations using both the DT and a simplified model. Across varying trajectories and configurations, the DT maintains low error-from-real, while the simplified model degrades outside its tuned regime, particularly under changes in trajectory dynamics, mechanical load, and controller gains. We further embed the DT in a 4-DOF humanoid arm simulation and show that it runs significantly faster than the real-time version, achieving a simulation speedup factor of approximately 6.3× on a standard laptop. These results demonstrate that actuator-specific electromechanical and embedded control modeling improves the forward prediction of physical actuator behavior while remaining computationally practical for multi-joint robotic simulation. Full article

Hydropower units operating in isolated grids are characterized by low rotational inertia and weak damping, making it difficult to balance rapid frequency regulation and overshoot suppression. To address this issue, this paper proposes a GA–SQP hybrid optimization control strategy for multiple operating conditions based on a high-fidelity nonlinear dynamic model. Deep feedforward neural networks are first employed to reconstruct the nonlinear torque and discharge characteristics of the hydro-turbine, providing smooth and continuously differentiable mappings for subsequent gradient-based optimization. An improved performance index combining the Integral of Time-Cubed Absolute Error (ITCAE) with a transient overshoot penalty is then formulated to suppress long-tail errors and prioritize smooth responses with reduced transient overshoot. A two-stage optimization framework is further developed, in which the Genetic Algorithm (GA) performs global exploration to identify a promising parameter region, followed by Sequential Quadratic Programming (SQP) for high-precision local refinement. Comparative simulations under low-, rated-, and high-head high-load conditions show that the proposed strategy achieves higher optimization accuracy with fewer iterative resources. Within the investigated operating range, the optimized controller maintains a very low overshoot level while preserving satisfactory response speed, effectively improving the balance between rapidity and stability in isolated-grid frequency regulation. Full article

Ultrasound examination for developmental dysplasia of the hip (DDH) in infants is highly dependent on operator experience, leading to inconsistent imaging quality and poor reproducibility between sonographers. This study proposes an autonomous robotic ultrasound system to improve the standardization and automation of hip ultrasound examinations. The system consists of a robotic arm, a six-axis force/torque sensor, an RGB-D camera and an ultrasound probe, integrating multiple functions including contact force control, visual localization, deep-learning-based segmentation and ultrasound image screening. To ensure stability and safety during scanning, an admittance-based hybrid force/position control strategy is adopted to achieve constant contact force control. For Graf standard plane acquisition, a stage-wise search strategy is designed, in which the search space is progressively narrowed through femoral head searching and multi-angle scanning. The optimal Graf standard plane is then automatically selected by combining image segmentation with a scoring mechanism. A customized hip phantom was used for validation. Experimental results show that the Dice coefficient for femoral head segmentation reaches 0.872, while the average Dice coefficient for multi-structure segmentation reaches 0.866. In 30 autonomous scanning trials, the success rate of Graf standard plane acquisition is 90.0%. Meanwhile, the system can maintain the contact force stably within the target range during scanning, validating the effectiveness of the force control strategy. These results indicate that the proposed robotic system, image recognition algorithm and visual servo control strategy exhibit favorable safety and feasibility, providing an innovative solution for automated infant hip ultrasound examination of DDH. Full article

In light of the advancement of vehicle electrification and intelligence, four-wheel independent drive (4WID) electric vehicles (EVs) have garnered significant attention as a promising platform. Integrating advanced torque-vectoring (TV) strategies into 4WID EVs can effectively optimize the synergistic performance between handling stability and energy efficiency of the over-actuated system across various driving conditions. In this paper, a hierarchical Combined Sliding Mode Control–Adaptive Nonlinear Model Predictive Control (cSMC-ANMPC) TV strategy is proposed to enhance the comprehensive performance of 4WID EVs and ensure adaptive control across diverse driving conditions. Firstly, a hierarchical control architecture is developed to decouple the complex multi-objective problem. The upper layer performs robust stability decision-making by observing the vehicle’s state errors. The lower layer determines the optimal torque distribution throughout the powertrain. Secondly, a Combined Sliding Mode Controller (cSMC) is developed for the upper layer to promptly generate a robust stability command. By co-regulating both yaw rate and sideslip angle into a single command, it simplifies the lower layer’s task and enhances overall stability. Thirdly, a Soft Actor-Critic (SAC) intelligent tuner is integrated into the lower-layer NMPC to mitigate the effects of varying conditions on the stability–economy trade-off and strengthen the adaptability of the controller. Finally, co-simulation evaluations on the MATLAB R2023b/CarSim 2020.0platform demonstrate that the proposed cSMC-ANMPC strategy can improve comprehensive performance for the studied 4WID EV. Compared with other baselines, the stability enhancement in extreme maneuvers and the long-term energy-saving capability are remarkable, showcasing its promising performance. Full article

In directional coring drilling, toolface adjustment is performed during drilling interruption by rotating the drill string through the top drive. Because the bottom-hole toolface angle cannot be transmitted to the surface in real time, a dynamic prediction model is required to guide toolface control. Existing flexible drill string models, however, generally neglect the nonlinear wellbore friction caused by stick–slip motion, reducing prediction accuracy. To address this issue, a distributed-parameter torsional dynamic model is established and discretized into a multi-degree-of-freedom system. A friction-state-based prediction–correction iterative algorithm is proposed to resolve the strong coupling between wellbore friction and system dynamics. At each time step, the sticking or slipping state is identified from the predicted motion, and the wellbore friction torque is iteratively updated until the friction state and dynamic equilibrium simultaneously converge, enabling accurate prediction of the drill bit toolface angle. Simulation results show that the proposed model captures the key dynamic characteristics of toolface adjustment. Under typical operating conditions, the drill bit start-up delay is 3.53 s, the peak angular velocity reaches 1.73°/s, and the peak transmitted torque is 2.28 kN·m. After the top drive stops, the drill bit continues rotating because of inertia, resulting in a 2.12° toolface overshoot and an angular lag rate of 21.2%. In addition, the effects of weight on bit, top-drive speed, and equivalent damping on toolface adjustment are quantified, providing guidance for parameter optimization. The proposed method provides a theoretical basis for toolface prediction and control in intelligent directional coring drilling. Full article

Photovoltaic (PV) pumping systems offer a sustainable alternative to diesel solutions, yet their nonlinearity, intermittent irradiation, and complex motor-pump dynamics challenge energy extraction and reliability. Currently, these systems predominantly rely on classical Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (INC), which suffer from slow convergence, steady-state oscillations, and an inability to track Global MPP (GMPP) under uniform irradiance variation conditions. Furthermore, existing studies typically address MPPT optimization and motor control in isolation, without considering their coupled interaction, and rarely incorporate economic viability assessments. To address these limitations, this paper proposes an innovative control architecture integrating four advanced metaheuristic MPPT techniques, namely the Genetic Algorithm (GA), Gray Wolf Optimizer (GWO), Cuckoo Search (CS) algorithm, and Horse Herd Optimization Algorithm (HOA), with Model Predictive Control (MPC) for a Brushless DC (BLDC) motor-driven pumping system, supplemented by battery storage. Comprehensive simulations were conducted under both constant and variable irradiance profiles (1000 to 500 to 1000 W/m²) to evaluate dynamic performance, tracking accuracy, and system robustness. The results demonstrate that HOA and GWO significantly outperform GA and CS, achieving superior DC bus voltage stability with ripple values below 2.4 V, faster convergence times, reduced electromagnetic torque oscillations, and enhanced MPPT efficiency exceeding 99%. Under variable irradiance, HOA exhibits the fastest stabilization with minimal overshoot and superior disturbance rejection, while GA suffers from severe oscillations and CS displays sawtooth ripple patterns. A techno-economic analysis further confirms the economic viability of the proposed system, with HOA and GWO strategies yielding lower lifecycle costs, extended converter lifespans from 5 to over 12 years, and improved return on investment compared to conventional approaches. This integrated framework offers a robust, efficient, and economically sustainable solution for autonomous PV water pumping applications. Full article

Induction motors remain the workhorse of modern industry thanks to their robustness, cost effectiveness and high efficiency, but the growing demands of electrified transport, high-performance automation and Industry 4.0 impose increasingly stringent control requirements. This paper presents an integrated, application-oriented survey of control strategies for high-performance induction motor drives, covering classic scalar V/f control as a baseline and advanced field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC), nonlinear/robust schemes and intelligent/data-driven and digital twin-assisted solutions. The methods are analyzed within a unified framework in terms of dynamic response, torque and flux ripple, current harmonic distortion, efficiency, robustness, implementation complexity and suitability for sensorless and fault-tolerant operation. Emphasis is placed on hybrid strategies that combine classical vector or DTC structures with MPC, fuzzy and neuro-fuzzy logic, neural network-based observers, reinforcement learning and digital twin-enabled monitoring to reconcile fast dynamics with high efficiency, low ripple and lifecycle reliability. Consolidated comparison tables and a hybrid control map highlight typical performance trends, trade-offs between simplicity and performance, and the complementary roles of AI and digital twins as system-level enablers. The survey also outlines promising research directions toward systematically designed hybrid controllers, lightweight digital twins for embedded platforms and experimentally validated benchmarks that can accelerate the industrial uptake of next-generation induction motor drives. Full article

Gas-assisted discharge of granular materials plays a critical role in shaft furnaces, moving-bed reactors, and other industrial multiphase systems, where interstitial gas flow strongly influences arch stability and may induce progressive flow blockage. Existing analytical models generally neglect aerodynamic gas–particle interactions, whereas CFD–DEM simulations provide high predictive accuracy at the expense of substantial computational cost. To bridge this gap, the present study develops and validates a physically based Torque-Balance Model for predicting gas-assisted granular discharge, arch stability, and flow blockage. A comprehensive experimental investigation was performed using a quasi-two-dimensional transparent apparatus and a thermally stabilized shaft model operated under controlled conditions. Gas-assisted discharge was examined for different gas-flow directions, gas properties, outlet geometries, and particulate materials using hydrogen, helium, and air. High-speed imaging together with gravimetric measurements enabled detailed characterization of discharge regimes and arch evolution. The proposed analytical framework explicitly incorporates interparticle mechanical interactions, aerodynamic drag, outlet geometry, and gas-pressure effects within a unified torque-balance formulation. The model describes successive stages of the discharge process, including stable discharge, transition to blockage, and complete flow suppression, while maintaining computational efficiency suitable for engineering calculations. Experimental results demonstrated that gas-flow direction governs arch stability and discharge behavior. Co-current gas flow promoted repeated arch collapse and enhanced discharge, whereas counter-current flow progressively stabilized the granular arch and ultimately produced complete flow blockage. Validation against the complete experimental database demonstrated excellent agreement between theoretical predictions and experimental observations, yielding an average prediction error below 10%, a maximum deviation within ±20%, and a coefficient of determination of R² = 0.96. The proposed Torque-Balance Model provides a computationally efficient and physically interpretable engineering framework that bridges the gap between simplified empirical correlations and computationally intensive CFD–DEM simulations and can be applied to the prediction and optimization of gas-assisted granular discharge in industrial multiphase systems. Full article

Misalignment between the rotation axis of a lower-limb exoskeleton knee joint and the human knee joint in the sagittal plane introduces additional human-exoskeleton interaction forces, affecting gait and assistance efficiency. This study proposes an equivalent stiffness-damping scheme that simultaneously accounts for the serial characteristics of both the exoskeleton’s strapping and human soft tissues, and establishes a human-exoskeleton coupling model based on OpenSim that allows for precise adjustment of the misalignment magnitude along the sagittal coordinate system. Simulation results indicate that the effects of misalignment exhibit significant directional dependence: at a misalignment of 0.05 m, the peak increase in extension torque in the positive y-axis direction reached 38.9%, while the peak increase in flexion torque in the negative x-axis direction reached 211.2%. The trends in human-exoskeleton interaction torque and electromyographic signals measured experimentally were consistent with the simulation results. Considering that, at a misalignment of 0.02 m, the maximum difference in torque in the negative x-axis direction was 54.65% and the assist efficiency in the positive x-axis direction dropped to −5.13%, it is recommended that the misalignment of the exoskeleton knee joint be controlled within 0.02 m. This provides quantitative evidence for the structural design and wear calibration of the exoskeleton. Full article

Independently rotating wheelsets (IRWs) are increasingly adopted in low-floor urban rail vehicles because they enable compact bogie layouts and improved curving performance. However, their limited natural self-centering capability can lead to lateral offset from the track centerline, increased flange contact, and degraded lateral guidance performance. This paper presents an LQ-servo-based differential-torque control method for active centering of IRWs using a control-oriented state-space model derived from linearized wheel–rail creep-force relations. Rather than proposing a new control algorithm, this study establishes and experimentally validates an integrated modeling–control–validation framework for differential-torque active centering, providing new quantitative evidence of its effectiveness. The left and right wheel torques are employed as actuation inputs to regulate the lateral displacement of the wheelset, while a conventional proportional–integral–derivative (PID) controller is implemented as a baseline for comparison. The two controllers are evaluated through numerical simulations and validated experimentally using a 1/5-scale IRW roller rig. Compared with the PID controller, the LQ-servo controller achieves faster centering, improved yaw damping, and reduced steady-state lateral offset. In particular, under representative parameter variations, the fixed-gain PID controller loses stability, whereas the LQ-servo controller remains stable and maintains accurate centering, indicating lower sensitivity to the considered parameter variations in the simulation study, while the hardware experiments confirmed real-time implementation in the presence of unmodeled physical effects. These findings support the effectiveness of the proposed modeling and control approach for active centering of independently rotating wheelsets. Full article

Population aging and the rising prevalence of motor disorders are driving demand for assistive lower-limb robotic systems capable of decoding user intention rather than merely providing mechanical support. We present CM-FuseNet, an attention-augmented hybrid Brain–Computer–Muscle Interface (BCMI) that simultaneously fuses cortical concentration indices extracted from electroencephalography (EEG) and lower-limb intention patterns derived from electromyography (EMG) to adaptively control a 4-DOF assistive lower-limb exoskeleton. To eliminate the burden of human-subject ethics review and to ensure reproducibility of the proposed methodology, all validation is performed exclusively on (i) permissively licensed open-access biomedical datasets, (ii) high-fidelity OpenSim 4.5 and MuJoCo 3.1 musculoskeletal–exoskeleton co-simulation, and (iii) limited self-experimentation by the corresponding author with non-invasive consumer-grade devices. Three components are introduced: (i) a log-tanh normalized concentration index CI in (0, 1) derived from the $(P_{SMR}+P_{Mid-Beta})/P_{Theta}$ ratio; (ii) a bidirectional Cross-Modal Transformer (CMT) with eight-head self- and cross-attention; and (iii) a Soft Actor-Critic (SAC) reinforcement-learning controller that adaptively tunes four servo PID gains using a concentration-weighted state. Experiments on the PhysioNet EEGMMIDB, Ninapro DB2/DB7, HuMoD and WAY-EEG-GAL datasets (combining N = 162 trial sessions, 47,520 windows, and five-fold cross-validation) yield a gait-phase classification accuracy of 96.84 ± 1.18%, torque-tracking RMSE of 0.072 ± 0.008 N·m, information transfer rate of 38.6 bits/min, end-to-end latency of 9.4 ms, and a 27.4% reduction in simulated metabolic cost over an EMG-only PID baseline (one-way ANOVA: F(4, 75) = 47.83, p < 0.001; Tukey HSD: p < 0.01 against all baselines). Under high cognitive load, CM-FuseNet preserves accuracy with only a 4.63 percentage-point degradation versus 13.22 percentage points for the EMG-only baseline. Full article

Contact-rich manufacturing processes, such as surface cleaning, deburring, and polishing, require precise force regulation and complex trajectory tracking that are challenging to automate using conventional robot programming methods. Learning from Demonstration (LfD) offers a powerful alternative to transfer these expert skills from human operators to robotic systems. The objective of this study is to systematically map academic publications addressing LfD applications in robotic deburring and polishing between 2016 and 2026, classify the algorithmic structures, sensory modalities, and control configurations employed, and identify key industrial integration challenges. In accordance with the PRISMA 2020 guidelines, a systematic search was conducted across Scopus, Web of Science, IEEE Xplore, and Google Scholar databases. Out of the 288 initially retrieved records, duplicate removal and a two-stage screening process (Title/Abstract review, followed by full-text review) resulted in a final corpus of 24 primary studies included for qualitative synthesis. The included studies were classified into five algorithmic clusters: Dynamic Movement Primitives (DMPs) and variants (9 out of 24 studies, 38%), probabilistic and statistical models (8 out of 24 studies, 33%), deep learning and generative AI architectures (4 out of 24 studies, 17%), autonomous dynamical systems (2 out of 24 studies, 8%), and direct impedance control (1 out of 24 studies, 4%). Force/torque sensing remains the dominant modality; it was utilized exclusively in 71%—17 out of 24—of studies and in 87.5% of studies as any configuration (either as a sole modality or in multimodal setups). However, recent years have documented a trend toward multimodal perception and generative action policies (e.g., Diffusion Policies). The findings suggest that while LfD offers potential cost-reduction and flexibility benefits for small- and medium-sized enterprises (SMEs), technical barriers, such as the sim-to-real transfer gap, high-frequency impact dynamics in deburring, and the autonomous identification of local non-polishing areas (LNP areas), continue to limit widespread industrial deployment. Full article

To address delayed attitude correction, limited adaptability of single-actuator systems, and reduced tunneling efficiency in full-face shaft-boring machines (SBMs), this study proposes a PSO-LSTM-based hybrid steel strand–support shoe attitude correction strategy. A coupled dynamic model with a 45° offset configuration is developed to enable coordinated multi-actuator control. A PSO-optimized Long Short-Term Memory (PSO-LSTM) network is employed to predict inclination deviation over a 5 s horizon, providing anticipatory information for proactive control. Based on this prediction, a hierarchical control strategy with adaptive torque allocation is designed to seamlessly coordinate fine correction via steel strand cables and high-torque correction via support shoes. Simulation results demonstrate that the proposed model achieves a prediction accuracy within ±0.02°. Under inclination conditions of 0.05°, 0.3°, and 1.0°, rapid attitude correction is achieved. Compared with independent support shoe control, the maximum horizontal displacement is reduced from 64 mm, 131 mm, and 160 mm to 6.3 mm, 65 mm, and 100 mm, corresponding to reductions of 90.2%, 50.4%, and 37.5%, respectively. The results further indicate that small-angle deviations can be compensated by the steel-strand system without additional support-shoe operations, while medium- and large-angle deviations can be regulated through coordinated actuation of multiple correction systems according to deviation magnitude. Simulation results demonstrate that the proposed method improves attitude correction performance and dynamic response under the investigated simulation conditions. The proposed framework provides a potential solution for intelligent attitude control of SBMs, while further field validation is required before practical engineering deployment. Full article

Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation, and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF, or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner that re-estimates the gains from a frequency response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action that the auto-tuner cannot provide during its estimation interval. The novelty of this work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA)-tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and a consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work. Full article