Search Results (818)

An essential component of modern musculoskeletal rehabilitation systems is treadmills of various sizes, the control of which may rely either on manual adjustment of treadmill speed, fixed for the entire training session, or on automatic regulation based on analysis of the user’s movements and velocity. The aim of this study was to experimentally compare the control functions of an adaptive treadmill designed for musculoskeletal rehabilitation and to assess the influence of the hardware configuration and tracking systems on user stability and the smoothness of transient processes. Two running platforms (of different lengths, one equipped with handrails and one without), two tracking systems (virtual reality trackers and a computer vision system using the MediaPipe Pose model), and three control functions—linear, nonlinear, and proportional-integral-derivative (PID)—were investigated. A set of metrics with both metrical and physiological interpretability was proposed (including positional stability, duration and amplitude of transient processes in position and velocity, subjective assessment, and others), all integrated into a single quality control criterion. This study presents extensive experimental research comparing various designs of adaptive running platforms and tracking systems, exploring the relationships between the available working area length and user comfort, and determining the optimal parameters for the selected control functions. The optimal control function was identified as the linear law for the tracking system based on virtual reality trackers and the PID function for the computer-vision-based tracking system. The conducted experiments made it possible to formulate recommendations regarding the minimum permissible working area length of treadmill platforms and the selection of tracking systems and control functions for musculoskeletal rehabilitation systems. The obtained results are of practical relevance for developing adaptive rehabilitation simulators and creating control algorithms that ensure smooth and stable treadmill motion, thereby enhancing user comfort, efficiency, and safety during musculoskeletal rehabilitation exercises. Full article

Virtual synchronous generator (VSG) simulates the dynamic characteristics of synchronous generator, offering significant advantages in flexibly adjusting virtual inertia and damping parameters. However, their dynamic stability is susceptible to constraints such as control parameter design, grid disturbances, and the intermittent nature of distributed power sources. This study addresses the degradation of transient performance in traditional sliding mode control for VSG, caused by insufficient multi-parameter cooperative adaptation. It proposes an adaptive sliding mode control strategy based on radial basis function (RBF) neural networks. Through theoretical analysis of the influence mechanism of virtual inertia and damping coefficient perturbations on system stability, the RBF neural network achieves dynamic parameter decoupling and nonlinear mapping. Combined with an integral-type sliding surface to design a weight-adaptive convergence law, it effectively avoids local optima and ensures global stability. This strategy not only enables multi-parameter cooperative adaptive regulation of frequency fluctuations but also significantly enhances the system’s robustness under parameter perturbations. Simulation results demonstrate that compared to traditional control methods, the proposed strategy exhibits significant advantages in dynamic response speed and overshoot suppression. Full article

This paper analyzes the behavior of a three-phase induction motor (IM) during voltage sags in the supply network and its subsequent re-acceleration following voltage recovery. A dynamic mathematical model based on the two-axis (d,q) representation of the IM is developed, taking into account variations in supply voltage, electromagnetic torque, and stator currents over time. The model enables a detailed assessment of motor stability and transient behavior when the supply voltage falls below nominal levels. The analysis covers sag depths of 0.9–0.5 ${\mathrm{U}}_{\mathrm{N}}$ and interruption durations of 0.14 s and 1.14 s, quantifying stator currents and electromagnetic torque both at the instant of the dip and within the first cycles after recovery. Particular attention is given to identifying the conditions under which the IM may fail to re-accelerate or transition into generator mode, depending on the depth and duration of the voltage sag and the type of mechanical load. The study includes simulations for a 0.75 kW IM under both constant and variable torque conditions, as well as different types and durations of short-circuit faults in the supply system. Results show that sag duration has little effect at sag onset but strongly influences recovery inrush and torque oscillations; shorter interruptions yield lower recovery currents. The findings provide practical insights for the design of more robust power supply infrastructures and the refinement of motor control and protection strategies. Full article

The increasing integration of power-electronic devices, such as voltage source converter-based high-voltage direct current (VSC-HVDC) systems and inverter-interfaced renewable energy sources (RESs), has rendered conventional short-circuit current (SCC) calculation methods inadequate. This paper proposes a novel analytical model that explicitly incorporates the current-limiting control dynamics of voltage source converters to accurately determine SCCs. The key contribution is a simplified yet accurate formulation that captures the transient behavior during faults, offering a more realistic assessment compared to traditional quasi-steady-state approaches. The proposed model was rigorously validated through electromagnetic transient (EMT) simulations and large-scale case studies. The results demonstrate that the method reduces the SCC calculation error to below 4%. Furthermore, when applied to the real-world provincial power grids of ZJ and JS, all computations converged within 10 iterations, confirming its robust numerical stability. These findings offer valuable insights for protection coordination studies and verify the model’s effectiveness as a reliable tool for planning future power systems with high power-electronics penetration. Full article

The rapid global expansion of photovoltaic (PV) generation has increased the prevalence of PV-dominated weak-grid systems, where wideband oscillations (WBOs) pose a significant challenge to secure and reliable operation. Unlike conventional electromechanical oscillations, WBOs originate from inverter control loops and multi-inverter interactions, spanning sub-Hz to kHz ranges. This review provides a PV-focused and mitigation-oriented analysis that addresses this gap. First, it clarifies the mechanisms of WBOs by mapping oscillatory drivers such as phase-locked loop dynamics, constant power control, converter–grid impedance resonance, and high-frequency switching effects to their corresponding frequency bands, alongside their engineering implications. Second, analysis methods are systematically evaluated, including eigenvalue and impedance-based models, electromagnetic transient simulations, and measurement- and data-driven techniques, with a comparative assessment of their strengths, limitations, and practical applications. Third, mitigation strategies are classified across converter-, plant-, and system-levels, ranging from adaptive control and virtual impedance to coordinated PV-battery energy storage systems (BESS) operation and grid-forming inverters. The review concludes by identifying future directions in grid-forming operation, artificial intelligence (AI)-driven adaptive stability, hybrid PV-BESS-hydrogen integration, and the establishment of standardized compliance frameworks. By integrating mechanisms, methods, and mitigation strategies, this work provides a comprehensive roadmap for addressing oscillatory stability in PV-dominated weak grids. Full article

Background/Objectives: Whole-gland surgery or radiotherapy for localized prostate cancer (PCa) can cure the disease but often impair urinary and sexual function. Focal therapy with high-intensity focused ultrasound (HIFU) seeks to eradicate the tumor while sparing uninvolved tissue. We prospectively evaluated oncological control, functional outcomes and safety of MRI-guided focal HIFU in patients with low- or intermediate-risk PCa. Methods: In this prospective, single-arm, phase II feasibility trial (three Austrian centres, 2021–2024), treatment-naive patients with D’Amico low/intermediate-risk, PSA ≤ 15 ng/mL, clinical stage ≤ T2 and MRI-targeted, biopsy-confirmed index lesions underwent lesion-targeted HIFU (Focal One™). The primary endpoint was failure-free survival (FFS: absence of salvage whole-gland or systemic therapy, metastasis or PCa-specific death). Secondary endpoints included biopsy-proven cancer, prostate-specific antigen (PSA), patient-reported symptoms as International Prostate Symptom Score (IPSS), 5-item International Index of Erectile Function (IIEF), Gaudenz Incontinence Questionnaire and adverse events. Planned follow-up was 24 months with PSA every 3 months, mpMRI and biopsies at 12 months, and imaging- or PSA-triggered biopsies thereafter. Results: Fifty-one men were analysed in the per-protocol cohort (median age 67 years, median PSA 7.55 ng/mL). Median treated volume was 12 mL; median procedure time 85 min. At 24 months, FFS was 94.1%: 3/51 patients (5.9%) required salvage radiotherapy. Among 31 patients who underwent follow-up biopsy, 26 (83.9%) had no cancer; the five positives included three ISUP 1, one ISUP2 and one ISUP 4 lesion. Mean PSA fell by 69% at 3 months (to 2.3 ng/mL) and then stabilized under 3 ng/mL, with a mean of 2.7 ± 1.5 ng/mL at 24 months. Transient acute urinary retention occurred in 11/51 (21.6%); no Clavien–Dindo grade ≥ 4 events were reported. IPSS returned to or improved beyond baseline, erectile function largely recovered by 6–12 months, and only one new case of grade 2 incontinence was observed. Conclusions: MRI-guided focal HIFU achieved high two-year failure-free survival with low morbidity and preserved quality of life in carefully selected patients with low- or intermediate-risk PCa. These data support further randomized and longer-term investigations of focal HIFU as an organ-sparing alternative to whole-gland treatment. Full article

Reliable multilevel inverter IGBT modules require precise loss and heat management, particularly in severe traction applications. This paper presents a comprehensive modeling framework for three-level T-type neutral-point clamped (TNPC) inverters using a high-power Insulated Gate Bipolar Transistor (IGBT) module that combines model predictive control (MPC) with space vector pulse width modulation (SVPWM). The particle swarm optimization (PSO) algorithm is used to methodically tune the MPC cost function weights for minimization, while achieving a balance between output current tracking, stabilization of the neutral-point voltage, and, consequently, a uniform distribution of thermal stress. The proposed SVPWM-MPC algorithm selects optimal switching states, which are then utilized in a chip-level loss model coupled with a Cauer RC thermal network to predict transient chip-level junction temperatures dynamically. The proposed framework is executed in MATLAB R2024b and validated with experiments, and the SemiSel industrial thermal simulation tool, demonstrating both control effectiveness and accuracy of the electro-thermal model. The results demonstrate that the proposed control method can sustain neutral-point voltage imbalance of less than 0.45% when operating at 25% load and approximately 1% under full load working conditions, while accomplishing a uniform junction temperature profile in all inverter legs across different working conditions. Moreover, the results indicate that the proposed control and modeling structure is an effective and common-sense way to perform coordinated electrical and thermal management, effectively allowing for predesign and reliability testing of high-power TNPC inverters. Full article

Primary meningeal melanocytoma is an uncommon, pigmented neoplasm that rarely undergoes malignant transformation, and therapeutic guidelines remain undefined. We report a 43-year-old woman who initially presented with a sudden headache and a right temporal intraparenchymal mass. Subtotal resection revealed a melanocytoma (WHO grade I); residual tumor was treated with Gamma Knife. About 15 months later, she deteriorated rapidly due to malignant transformation with cerebral hemorrhage and spinal leptomeningeal metastasis. Pembrolizumab was initiated within four weeks of the malignant diagnosis and produced transient neurological improvement. Due to symptomatic progression, ipilimumab plus nivolumab was commenced and achieved temporary radiographic stabilization, but the patient succumbed to diffuse progression later. Including this case, only five intracranial melanocytomas with malignant transformation treated with immune checkpoint inhibitors have been reported. Our experience supports initiating immunotherapy promptly after malignant transformation and suggests that sequential dual-agent blockade may modestly extend disease control. Full article

This paper proposes a stepwise expert-teaching reinforcement learning framework for intelligent frequency control in hydro–thermal–wind–solar–compressed air energy storage (CAES) integrated systems under high renewable energy penetration. The proposed method addresses the frequency stability challenge in low-inertia, high-volatility power systems, particularly in Southwest China, where large-scale renewable-energy-based energy bases are rapidly emerging. A load frequency control (LFC) model is constructed to serve as the training and validation environment, reflecting the dynamic characteristics of the hybrid system. The stepwise expert-teaching PPO (SETP) framework introduces a stepwise training mechanism in which expert knowledge is embedded to guide the policy learning process and training parameters are dynamically adjusted based on observed performance. Comparative simulations under multiple disturbance scenarios are conducted on benchmark systems. Results show that the proposed method outperforms standard proximal policy optimization (PPO) and traditional PI control in both transient response and coordination performance. Full article

Background: Autism Spectrum Disorder (ASD) is characterized by atypical brain oscillatory dynamics and altered connectivity, impairing sensory integration, socio-communicative responsiveness, and behavioral regulation. Methods: Radio Electric Asymmetric Conveyer (REAC) technology delivers non-invasive neurobiological modulation through standardized, operator-independent protocols. The Brain Wave Optimization Neurodevelopment–Autism (BWO ND-A) protocol was designed to address oscillatory patterns frequently altered in ASD, aiming to promote network coherence and multidomain functional improvement. This retrospective pre–post single-arm study evaluated 39 children with ASD (31 males, 8 females; mean age 7.85 ± 2.90 years). All received one Neuro Postural Optimization (NPO) session to prime central nervous system adaptive capacity, followed by BWO ND-A (18 sessions, ~8 min each), administered 3–4 times daily over ~two weeks. The primary outcome was the Autism Treatment Evaluation Checklist (ATEC) total score; secondary outcomes were its four subscales. Results: Mean total ATEC decreased from 67.76 ± 16.11 to 56.25 ± 23.66 (mean change −11.51 ± 14.48; p < 0.0001; Cohen’s dz = 0.78). Clinically meaningful improvement (≥8-point reduction) occurred in 59% of participants. In 10.3% of cases, caregiver ratings indicated an apparent worsening (≥8-point increase). However, no objective deterioration or adverse effects were observed. This pattern was most likely related to a transient phase of functional re-adaptation, during which emerging changes may initially be perceived by caregivers as worsening before stabilizing into improvement. Conclusions: While these findings suggest promising short-term real-world efficacy and safety, the absence of a control group, lack of objective neurophysiological measures, and no long-term follow-up limit causal inference. Future controlled studies with neurophysiological monitoring are needed to confirm the targeted neuromodulatory action and durability of effects. Full article

Large-scale production systems (LSPS) operate under growing complexity driven by digital transformation, tighter environmental regulations, and the demand for resilient and resource-efficient operation. Conventional control strategies, particularly PID and isodromic regulators, remain dominant in industrial automation due to their simplicity and robustness; however, their capability to achieve near-optimal performance is limited under constraints on control amplitude, rate, and energy consumption. This study develops an analytical–computational approach for the approximate realization of optimal nonlinear control using standard regulator architectures. The method determines switching moments analytically and incorporates practical feasibility conditions that account for nonlinearities, measurement noise, and actuator limitations. A comprehensive robustness analysis and simulation-based validation were conducted across four representative industrial scenarios—energy, chemical, logistics, and metallurgy. The results show that the proposed control strategy reduces transient duration by up to 20%, decreases overshoot by a factor of three, and lowers transient energy losses by 5–8% compared with baseline configurations, while maintaining bounded-input–bounded-output (BIBO) stability under parameter uncertainty and external disturbances. The framework provides a clear implementation pathway combining analytical tuning with observer-based derivative estimation, ensuring applicability in real industrial environments without requiring complex computational infrastructure. From a broader sustainability perspective, the proposed method contributes to the reliability, energy efficiency, and longevity of industrial systems. By reducing transient energy demand and mechanical wear, it supports sustainable production practices consistent with the following United Nations Sustainable Development Goals—SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production). The presented results confirm both the theoretical soundness and practical feasibility of the approach, while experimental validation on physical setups is identified as a promising direction for future research. Full article

Small hydropower-dominated microgrids enable power exchange with the main grid during grid-connected operation but face frequency stability challenges during sudden islanding (e.g., line faults), requiring prompt generation curtailment or load shedding. In communication-constrained mountainous regions, conventional methods such as high-frequency tripping or low-frequency load shedding often struggle to achieve precise frequency regulation A hierarchical strategy integrating master station centralized decision making and substation local control is proposed. This study theoretically analyzes the post-islanding frequency dynamics of small hydropower microgrids. The master station formulates optimal shedding decisions using regional power flow data, while substations execute decisions via local measurements to mitigate communication issues. A constrained mathematical model is established, solved using a heuristic algorithm, validated through electromagnetic transient simulations, and compared with traditional schemes. The proposed scheme achieves precise surplus capacity shedding, enhancing frequency stability during abrupt islanding with reduced over-/under-tripping compared to that of conventional methods. This hierarchical strategy enhances frequency regulation capability under communication constraints, ensuring reliable operation of small hydropower microgrids during sudden islanding and providing a practical solution for remote regions with limited communication infrastructure. Full article

This study addresses the issues related to the inaccurate assessment of transient voltage stability margins and the limited participation of resources involved in regulation during high-voltage direct current (HVDC) receiving-end grid faults under high-penetration new energy integration. This paper proposes a method for transient voltage emergency control at the HVDC receiving-end grid, utilizing a multi-resource approach based on transient voltage similarity partitioning with a multiple-two-element notation criterion. First, the transient voltage stability margin and the new energy transient off-grid margin index, based on the multiple-two-element notation criterion, are introduced. Second, a grid partitioning scheme is employed, which clusters nodes based on the similarity of their transient voltage features, and the impact of multiple resources on the transient voltage stability of the HVDC receiving-end system is analyzed using trajectory sensitivity. On this basis, a multi-resource optimization model for transient voltage emergencies is established with the aim of minimizing the control cost, considering the transient voltage stability, off-grid new energy, and other safety evaluation constraints, in order to coordinate multiple resources participating in transient voltage control until the stability requirements are met. Finally, the validity of the proposed control scheme is verified using the improved frequency stability benchmark test system (Chinese Society for Electrical Engineering—Frequency Stability, CSEE-FS). The research results demonstrate that the scheme proposed in this study can be utilized to accurately assess the transient voltage stability and off-grid potential of renewable energy units following failure at the HVDC receiving-end system. Additionally, it can reasonably partition the grid based on transient operating conditions while fully exploiting the potential of multiple resources within the faulted partition to control transient voltage emergencies in the grid. Full article

This paper proposes a predefined time transient coordinated control strategy based on an adaptive nonlinear extended state observer (ANLESO) to address the adaptability challenges of mode transition control in power-split hybrid electric vehicles (PS-HEVs). Firstly, building upon a conventional dynamic coordinated control framework, the influence of varying acceleration conditions and external disturbances on mode transition performance is analyzed. To enhance disturbance estimation under both positive and negative as well as large and small errors, an ANLESO is developed, which not only improves the speed and accuracy of disturbance observation but also guarantees symmetric convergence performance with respect to estimation errors. Subsequently, a predefined time feedback controller is developed based on the theory of predefined time control. Theoretical stability analysis demonstrates that the convergence time of the system is independent of the initial state and can be guaranteed within a predefined time. Finally, the feasibility and superiority of the proposed control strategy are validated through Hardware-in-the-Loop (HIL) testing and vehicle experimentation. The results show that, compared with PID control based on a linear expansion state observer, the proposed strategy reduces the mode transition time by 45.7% and mitigates drivability shock by 59.2%. Full article

Ensuring reliable power system control demands innovative architectural solutions. This research introduces a fault-tolerant hybrid parallel compensator architecture for load frequency control (LFC), combining a Proportional–Integral–Derivative with Filter (PIDF) compensator with a Fuzzy Fractional-Order PI-PD (Fuzzy FOPI–FOPD) module. Particle Swarm Optimization (PSO) determines optimal PID gains, while the Catch Fish Optimization Algorithm (CFOA) tunes the Fuzzy FOPI–FOPD parameters—both minimizing the Integral Time Absolute Error (ITAE) index. The parallel compensator structure guarantees continuous operation during subsystem faults, substantially boosting grid reliability. Rigorous partial failure tests confirm uncompromised performance-controlled degradation. Benchmark comparisons against contemporary controllers reveal the proposed architecture’s superiority, quantifiable through transient metric enhancements: undershoot suppression (−9.57 × 10⁻⁵ p.u. to −1.17 × 10⁻⁷ p.u.), settling time improvement (8.8000 s to 3.1511 s), and ITAE reduction (0.0007891 to 0.0000001608), verifying precision and stability gains. Resilience analyses across parameter drift and step load scenarios, simulated in MATLAB/Simulink, demonstrate superior disturbance attenuation and operational stability. These outcomes confirm the solution’s robustness, dependability, and field readiness. Overall, this study introduces a transformative LFC strategy with high practical viability for modern power networks. Full article