Search Results (1,826)

In disturbance scenarios, wheeled bipedal robots (WBRs) require effective control algorithms to restore balance. To address the trade-off between computational burden and control precision, and to enhance anti-disturbance capability, this paper proposes a soft-constrained Model Predictive Control (MPC) algorithm with optimized horizon parameters tailored to the hardware of the WBR. A cost function is designed, and the Dung Beetle Optimizer (DBO) is employed to optimize the MPC’s prediction and control horizons. An experimental platform is built, and impact and load disturbance experiments are conducted. The experimental results show that, under impact disturbances, the pitch angle and displacement overshoot with optimized MPC are reduced by 58.57% and 42.20%, respectively, compared to unoptimized LQR. Under load disturbances, the pitch angle and displacement overshoot are reduced by 17.09% and 15.53%, respectively, with both disturbances converging to the equilibrium position. Full article

Fujian Tulou, a UNESCO World Heritage Site, is highly vulnerable to environmental and anthropogenic stresses, with its earthen walls prone to surface cracking that threatens both structural stability and cultural value. Traditional manual inspection is inefficient, subjective, and may disturb fragile surfaces, highlighting the need for non-destructive and automated solutions. This study proposes a dual-path framework that integrates lightweight crack detection with independent physical simulation. On the detection side, an improved YOLOv12 model is developed to achieve lightweight and accurate recognition of multiple crack types under complex wall textures. On the simulation side, a two-layer RFPA^3D model was employed to parameterize loading conditions and material thickness, reproducing the four-stage crack evolution process, and aligning well with field observations. Quantitative validation across paired samples demonstrates improved consistency in morphology, geometry, and topology compared with baseline models. Overall, the framework offers an effective and interpretable solution for standardized crack documentation and mechanistic interpretation, providing practical benefits for the preventive conservation and sustainable management of Fujian Tulou. Full article

This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations. Full article

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

To promote energy efficiency and support sustainable electric transportation, this study addresses the challenge of real-time and energy-optimal control of permanent magnet synchronous motors (PMSMs) in electric vehicles operating under variable load conditions, proposing a novel Laguerre-based model predictive control (MPC) strategy integrated with maximum torque per ampere (MTPA) operation. Traditional MPC methods often suffer from limited prediction horizons and high computational burden when handling strong coupling and time-varying loads, compromising real-time performance. To overcome these limitations, a Laguerre function approximation is employed to model the dynamic evolution of control increments using a set of orthogonal basis functions, effectively reducing the control dimensionality while accelerating convergence. Furthermore, to enhance energy efficiency, the MTPA strategy is embedded by reformulating the current allocation process using d- and q-axis current variables and deriving equivalent reference currents to simplify the optimization structure. A cost function is designed to simultaneously ensure current accuracy and achieve maximum torque per unit current. Simulation results under typical electric vehicle conditions demonstrate that the proposed Laguerre-MTPA MPC controller significantly improves steady-state performance, reduces energy consumption, and ensures faster response to load disturbances compared to traditional MTPA-based control schemes. This work provides a practical and scalable control framework for energy-saving applications in sustainable electric transportation systems. Full article

Recent advances in cardiovascular imaging have revolutionized the assessment and management of abdominal aortic aneurysm (AAA) through the integration of sophisticated haemodynamic biomarkers. This comprehensive review evaluates the clinical utility and mechanistic significance of multiple biomarkers in AAA pathogenesis, progression, and treatment outcomes. Advanced cardiac imaging modalities, including four-dimensional magnetic resonance imaging (4D MRI), computational fluid dynamics (CFD), and specialized echocardiography, enable precise quantification of critical haemodynamic parameters. Wall shear stress (WSS) emerges as a fundamental biomarker, with values below 0.4 Pa indicating pathological conditions and increased risk for aneurysm progression. Time-averaged wall shear stress (TAWSS), typically maintaining values above 1.5 Pa in healthy arterial segments, provides crucial information about sustained haemodynamic forces affecting the vessel wall. The oscillatory shear index (OSI), ranging from 0 (unidirectional flow) to 0.5 (purely oscillatory flow), quantifies directional changes in WSS during cardiac cycles. In AAA, elevated OSI values between 0.3 and 0.4 correlate with disturbed flow patterns and accelerated disease progression. The relative residence time (RRT), combining TAWSS and OSI, identifies regions prone to thrombosis, with values exceeding 2–3 Pa⁻¹ indicating increased risk. The endothelial cell activation potential (ECAP), calculated as OSI/TAWSS, serves as an integrated metric for endothelial dysfunction risk, with values above 0.2–0.3 Pa⁻¹ suggesting increased inflammatory activity. Additional biomarkers include the volumetric perivascular characterization index (VPCI), which assesses vessel wall inflammation through perivascular tissue analysis, and pulse wave velocity (PWV), measuring arterial stiffness. Central aortic systolic pressure and the aortic augmentation index provide essential information about cardiovascular load and arterial compliance. Novel parameters such as particle residence time, flow stagnation, and recirculation zones offer detailed insights into local haemodynamics and potential complications. Implementation challenges include the need for specialized equipment, standardized protocols, and expertise in data interpretation. However, the potential for improved patient outcomes through more precise risk stratification and personalized treatment planning justifies continued development and validation of these advanced assessment tools. Full article

To address the issue of output anomalies that frequently occur in position sensors of permanent magnet synchronous motors within electromechanical actuation systems operating in harsh environments and can lead to degradation in system performance or operational interruptions, this paper proposes an integrated method for fault diagnosis and fault-tolerant control based on the Cubature Kalman Filter (CKF). This approach effectively combines state reconstruction, fault diagnosis, and fault-tolerant control functions. It employs a CKF observer that utilizes innovation and residual sequences to achieve high-precision reconstruction of rotor position and speed, with convergence assured through Lyapunov stability analysis. Furthermore, a diagnostic mechanism that employs dual-parameter thresholds for position residuals and abnormal duration is introduced, facilitating accurate identification of various fault modes, including signal disconnection, stalling, drift, intermittent disconnection, and their coupled complex faults, while autonomously triggering fault-tolerant strategies. Simulation results indicate that the proposed method maintains excellent accuracy in state reconstruction and fault tolerance under disturbances such as parameter perturbations, sudden load changes, and noise interference, significantly enhancing the system’s operational reliability and robustness in challenging conditions. Full article

This paper proposes a novel hybrid control strategy integrating a Finite Control Set Model Predictive Controller (FCS-MPC) with a universal droop controller (UDC) for effective load power sharing in inverter-fed microgrids. Traditional droop-based methods, though widely adopted for their simplicity and decentralized nature, suffer from limitations such as steady-state inaccuracies and poor transient response, particularly under mismatched impedance conditions. To overcome these drawbacks, the proposed scheme incorporates detailed modeling of inverter and source dynamics within the predictive controller to enhance accuracy, stability, and response speed. The UDC complements the predictive framework by ensuring coordination among inverters with different impedance characteristics. Simulation results under various load disturbances demonstrate that the proposed approach significantly outperforms conventional PI-based droop control in terms of voltage and frequency regulation, transient stability, and balanced power sharing. The performance is further validated through real-time simulations, affirming the scheme’s potential for practical deployment in dynamic microgrid environments. Full article

The large-scale utilization of wind and solar energy is crucial for achieving carbon neutrality targets. However, as extensive wind and solar power generation is integrated via power electronic devices, the inertia level of power systems continues to decline. This leads to a significant reduction in the system’s frequency regulation capability, posing a serious threat to frequency stability. Optimizing the system is an essential measure to ensure its safe and stable operation. Traditional optimization approaches, which separately optimize transmission and distribution systems, may fail to adequately account for the variability and uncertainty of renewable energy sources, as well as the impact of inertia changes on system stability. Therefore, this paper proposes a two-layer optimization method aimed at simultaneously optimizing the operation of transmission and distribution systems while satisfying minimum inertia constraints. The upper-layer model comprehensively optimizes the operational costs of wind, solar, and thermal power systems under the minimum inertia requirement constraint. It considers the operational costs of energy storage, virtual inertia costs, and renewable energy curtailment costs to determine the total thermal power generation, energy storage charge/discharge power, and the proportion of renewable energy grid connection. The lower-layer model optimizes the spatiotemporal distribution of energy storage units within the distribution network, aiming to minimize total network losses and further reduce system operational costs. Through simulation analysis and computational verification using typical daily scenarios, this model enhances the disturbance resilience of the transmission network layer while reducing power losses in the distribution network layer. Building upon this optimization strategy, the model employs multi-scenario stochastic optimization to simulate the variability of wind, solar, and load, addressing uncertainties and correlations within the system. Case studies demonstrate that the proposed model not only effectively increases the integration rate of new energy sources but also enables timely responses to real-time system demands and fluctuations. Full article

This paper proposes an enhanced Model Predictive Direct Speed Control (MPDSC) framework for Permanent Magnet Synchronous Motor (PMSM) drives, integrating duty ratio optimization and load torque disturbance compensation to significantly improve both transient and steady-state performance. Traditional finite-control-set MPC strategies, which apply a single voltage vector per sampling interval, often suffer from steady-state ripples, elevated total harmonic distortion (THD), and high computational complexity due to exhaustive switching evaluations. The proposed approach addresses these limitations through a novel dual-stage cost function structure: the first cost function optimizes dynamic response via predictive control of speed error, while the second adaptively minimizes torque ripple and harmonic distortion by adjusting the active–zero voltage vector duty ratio without the need for manual weight tuning. Robustness against time-varying disturbances is further enhanced by integrating a real-time load torque observer into the control loop. The scheme is validated through both MATLAB/Simulink R2020a simulations and real-time experimental testing on a dSPACE 1202 rapid control prototyping platform across small- and large-scale PMSM configurations. Experimental results confirm that the proposed controller achieves a transient speed deviation of just 0.004%, a steady-state ripple of 0.01 rpm, and torque ripple as low as 0.0124 Nm, with THD reduced to approximately 5.5%. The duty ratio-based predictive modulation ensures faster settling time, improved current quality, and greater immunity to load torque disturbances compared to recent duty-ratio MPC implementations. These findings highlight the proposed DR-MPDSC as a computationally efficient and experimentally validated solution for next-generation PMSM drive systems in automotive and industrial domains. Full article

Under electrical overload conditions, the molten dripping of thermoplastic wire insulation materials—particularly crosslinked polyethylene (XLPE)—poses a severe fire hazard and significantly complicates fire prevention and control. This study systematically investigated the formation mechanism, periodic characteristics, and flame interaction behavior of molten dripping in XLPE-insulated wires subjected to varying overload currents (0–80 A). Experiments were conducted using a custom-designed test platform equipped with precise current regulation and high-resolution video imaging systems. Key dripping parameters—including the initial dripping time, dripping frequency, and period—were extracted and analyzed. The results indicate that increased current intensifies Joule heating within the conductor, accelerating the softening and pyrolysis of the insulation, thus resulting in earlier and more frequent dripping. A thermodynamic prediction model was developed to reveal the nonlinear coupling relationships between the dripping frequency, period, and current, which showed strong agreement with the experimental data, especially at high current levels. Further flame morphology analysis showed that molten dripping induced pronounced vertical flame disturbances, while the lateral flame spread remained relatively unchanged. This phenomenon promotes vertical flame propagation and can trigger multiple ignition points, thereby increasing the fire complexity and hazard. The study enhances our understanding of the coupling mechanisms between electrical loading and molten dripping behavior and provides theoretical and experimental foundations for fire-safe wire design and early-stage risk assessment. Full article

This paper addresses the issue of rotor swings in a high-power synchronous generator during stable operation with a stiff power grid. The analysis of electromechanical swings was conducted using a circuit model incorporating fractional-order derivatives. Assuming that variations in the load angle under small disturbances from a stable equilibrium are minor, a linearized differential equation describing the electrodynamic state of the synchronous machine was derived. Based on this linearized equation of motion and the identified parameters of the equivalent circuit, calculations were performed for a 200 MW turbogenerator. The results indicate that the electromechanical swings are characterized by a constant pulsation and a low damping factor. Calculations were also carried out using a lumped-parameter equivalent circuit model. Based on the obtained results, it can be stated that the fractional-order model provides a more accurate fit of the frequency characteristics compared with the classical model with the same number of rotor equivalent circuits. The relative approximation errors for the fractional-order model are, for the d-axis (one rotor equivalent circuit), relative magnitude error δ_m = 1.53% and relative phase error δ_φ = 6.32%, and for the q-axis (two rotor equivalent circuits), δ_m = 3.2% and δ_φ = 8.3%. To achieve comparable approximation accuracy for the classical model, the rotor electrical circuit must be replaced with two equivalent circuits in the d-axis and four equivalent circuits in the q-axis, yielding relative errors of δ_m = 2.85% and δ_φ = 6.51% for the d-axis, and δ_m = 1.86% and δ_φ = 5.49% for the q-axis. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

To address the challenge of asymmetric deformation and failure in the surrounding rock of main roadways within extra-thick coal seams caused by level differences under intense mining disturbance, this study systematically analyzed the evolution laws of principal stress fields, deviatoric stress fields, and their impact on surrounding rock stability in upper-, middle-, and lower-level roadways within a 20 m extra-thick coal seam during mining retreat. The analysis employed numerical simulation, similarity simulation, and field monitoring. Key findings include the following: ① As the working face advances, the principal stress vector lines deflect following a bias-unloading pattern, while the peak value of the deviatoric stress field (PVDSF) exhibits asymmetric bias-loading characteristics. The lower-layer roadway emerges as the primary load-bearing layer controlling surrounding rock stability. ② The evolution trend of the maximum principal stress vector orientation is consistent across different layers. The deflection trajectory manifests as “the deflection of the goaf side → the near layer orientation → the deflection of the solid coal side”. ③ The deviatoric stress peak zones (DSPZs) at all layers exhibit a characteristic “three-stage” evolution. The deviatoric loading pattern for the lower-layer roadway surrounding rock is the following: initial state double peak region crescent-shaped non-layer distribution type → the range of the bimodal region and the extreme value increased simultaneously, distributed in a non-layer manner → the asymmetrical distribution type of steep drop in the peak area of non-mining deviator stress. ④ The junctions between the mining-side rib and floor and the non-mining-side rib and roof were identified as critical control zones. An innovative zonal asymmetric directional anchoring control technology, “anchor cable foundation support + concrete floor + asymmetric reinforcing anchor cable support”, along with a “One Directional Penetration and Three Synergies” control methodology, was proposed. Field monitoring confirmed the significant effectiveness of the optimized support system. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article