Search Results (38,342)

This paper presents novel chattering-free robust control strategies for addressing disturbances and uncertainties in a two-degree-of-freedom (2-DOF) unmanned aerial vehicle (UAV) dynamic model, with a focus on the highly nonlinear and strongly coupled nature of the system. The novelty lies in the development of sliding mode control (SMC), integral sliding mode control (ISMC), and terminal sliding mode control (TSMC) laws specifically tailored for the twin-rotor MIMO system (TRMS). These strategies are validated through both simulation and real-time experiments. A key contribution is the introduction of a uniform robust exact differentiator (URED) to recover rotor speed and missing derivatives, combined with a nonlinear state feedback observer to improve system observability. A feedback linearization approach, using lie derivatives and diffeomorphism principles, is employed to decouple the system into horizontal and vertical subsystems. Comparative analysis of the transient performance of the proposed controllers, with respect to metrics such as settling time, overshoot, rise time, and steady-state errors, is provided. The ISMC method, in particular, effectively mitigates the chattering issue prevalent in traditional SMC, improving both system performance and actuator longevity. Experimental results on the TRMS demonstrate the superior tracking performance and robustness of the proposed control laws in the presence of nonlinearities, uncertainties, and external disturbances. This research contributes a comprehensive control design framework with proven real-time implementation, offering significant advancements over existing methodologies. Full article

Studying rotor-bearing systems involving fluid film bearings is essential for designing and assessing the dynamic responses and performance of rotating machinery. They are involved in many applications such as pumps, turbines, and engines. Water-lubricated bearings are often used in many applications where the use of oil-based lubricants is not desirable, such as in environmentally sensitive areas such as water desalination. In this study, dynamic analysis is performed to identify the stability regions that prevent the application of water-lubricated journal bearings. This is achieved by solving the system equations of motion and then using an infinitesimal perturbation method to evaluate the second-order bearing coefficients of a journal bearing. In this paper, a steel shaft supported by two symmetrical journal bearings was used to investigate the system stability analysis. A test rig is designed and manufactured to examine the rotor’s dynamic behavior and verify the theoretical outcomes of the FE model, utilizing the bearing coefficients based on second-order analysis. Furthermore, this study compares the two fluids, both theoretically and experimentally, investigating their impact on the rotor-bearing system at different rotational speeds. The theoretical findings indicate that the threshold speed for journal bearings is significantly higher when using water as the lubricant fluid film compared to using oil as the lubricant fluid. Additionally, because of the low viscosity of water, water-lubricated bearings are susceptible to significant wear and noise in operating conditions. Our experiments show that an oil lubricant provides less response than a water lubricant for unbalanced rotors within the tested speed range. Full article

The reliability of electric vehicles (EVs) is crucial for the performance and safety of modern transportation systems. Electric motors are the driving force in EVs, and their maintenance is critical for efficient EV performance. The conventional fault detection methods for motors often struggle with accurately capturing complex spatiotemporal vibration patterns. This paper proposes a recurrent convolutional neural network (RCNN) for effective defect detection in motors, taking advantage of the advances in deep learning techniques. The proposed approach applies long short-term memory (LSTM) layers to capture the temporal dynamics essential for fault detection and convolutional neural network layers to mine local features from the segmented vibration data. This hybrid method helps the model to learn complicated representations and correlations within the data, leading to improved fault detection. Model development and testing are conducted using a sizable dataset that includes various kinds of motor defects under differing operational scenarios. The results demonstrate that, in terms of fault detection accuracy, the proposed RCNN-based strategy performs better than the traditional fault detection techniques. The performance of the model is assessed under varying vibration data noise levels to further guarantee its effectiveness in practical applications. Full article

The paper presents an approach to solving the problem of unknown motion parameters Bayesian identification for the stochastic dynamic system model with randomly delayed observations. The system identification and the object tracking tasks obtain solutions in the form of recurrent Bayesian relations for a posteriori probability density. These relations are not practically applicable due to the computational challenges they present. For practical implementation, we propose a conditionally minimax nonlinear filter that implements the concept of conditionally optimal estimation. The random delays model source is the area of autonomous underwater vehicle control. The paper discusses in detail a computational experiment based on a model that is closely aligned with this practical need. The discussion includes both a description of the filter synthesis features based on the geometric interpretation of the simulated measurements and an impact analysis of the effectiveness of model special factors, such as time delays and model unknown parameters. Furthermore, the paper puts forth a novel approach to the identification problem statement, positing a random jumping change in the motion parameters values. Full article

Following the operational success of the Hywind Scotland, Kincardine, WindFloat Atlantic, and Hywind Tampen floating wind farms, the floating offshore wind industry is expected to play a critical role in the global clean energy transition. However, there is still significant work needed in optimizing the design and implementation of floating offshore wind turbines (FOWTs) to justify the widespread adoption of this technology and ensure that it is commercially viable compared to other more-established renewable energy technologies. The present review explores the application of fully coupled computational fluid dynamics (CFD) modeling approaches for achieving the cost reductions and design confidence necessary for floating wind to fully establish itself as a reliable and practical renewable energy technology. In particular, using these models to better understand and predict the highly nonlinear and integrated environmental loading on FOWT systems and the resulting dynamic responses prior to full-scale implementation is of increased importance. Full article

This paper investigates the problem of fixed-time event-triggered consensus control for a class of multi-agent systems with disturbed and non-linear dynamics. A fixed-time consensus protocol based on an event-triggered strategy is proposed, which can ensure a fixed-time event-triggered consensus, reduce energy consumption, and decrease the frequency of controller updates. The control protocol can also be applied to the case when the systems are free of disturbances; it solves the problem of high convergence time of the systems and reduces energy consumption of the systems. Sufficient conditions are proposed for the multi-agent systems with disturbed and non-linear dynamics to achieve the fixed-time event-triggered consensus by using algebraic graph theory, inequalities, fixed-time stability theory, and Lyapunov stability theory. Finally, simulation results show that the proposed control protocol has the advantages of both event-triggered and fixed-time convergence; compared to previous work, the convergence time of the new control protocol is greatly reduced (about 1.5 s) and the update times are also greatly reduced (less than 50 times), which is consistent with the theoretical results. Full article

Automatic milking systems (AMSs) are revolutionizing the dairy industry by boosting herd efficiency, primarily through an increased milk yield per cow and reduced labor costs. The performance of milking machines, whether traditional or automated, can be evaluated using advanced vacuum meters through dynamic testing. This process involves scrutinizing the system and milking routine to identify critical points, utilizing the VaDia™ logger (BioControl AS, Rakkestad, Norway). Vacuum recordings were downloaded and analyzed using the VaDia Suite™ software under the guidance of a milking specialist. Access to data from AMSs across various manufacturers and herds facilitated a retrospective study aimed at describing and comparing key milk emission parameters for different AMS brands while identifying potential mastitis risk factors. Using the proper statistical procedures of SPSS 29.1 (IBM Corp., Armonk, NY, USA), researchers analyzed data from 4878 individual quarter milkings from cows in 48 dairy herds. Results indicated a significant variability in milking parameters associated with quarter milk yield and AMS brand. Notably, despite AMSs standardizing teat preparation and stimulation, this study revealed a surprisingly high frequency of two major mastitis risk factors—bimodality and irregular vacuum fluctuations—occurring more frequently than in conventional milking systems. This study, one of the few comparing different AMS brands and their performance, highlights the crucial role of dynamic testing in evaluating AMS performance under real-world conditions. Full article

Terahertz spectroscopy systems, which integrate terahertz sources and detectors, have important applications in many fields such as materials science and security checking. Based on highly sensitive frequency up−conversion detection, large dynamic range spectral measurements in a terahertz region are reported. Our system realized the detection sensitivity at a 10 aJ level with a 2−( 3−( 4−hydroxystyryl) −5,5−dime−thylcyclohex−2−enylidene) malononitrile (OH1) crystal and a dynamic range up to seven orders. Based on this system, we verified the validity of the spectral measurement with tests which were conducted on monohydrate glucose, anhydrous glucose and mixed tablet samples with a thickness of 0.8 mm in 1~3 THz, respectively. Also, a mini coppery elbow tube with an inner diameter of 1 mm was used for the transmission of a terahertz wave to simulate some strip biological tissue samples. By allowing terahertz to transmit through this tube filled with 0.5 g glucose powder, we successfully obtained the absorption spectrum with a minimum transmittance at the absorption peak in the order of 10⁻⁴. Full article

Accurately predicting the payload movement and ensuring efficient control during dynamic tower crane operations are crucial for crane safety, including the ability to predict payload mass within a safe or normal range. This research utilizes deep learning to accurately predict the normal and abnormal payload movement of tower cranes. A scaled-down tower crane prototype with a systematic data acquisition system is built to perform experiments and data collection. The data related to 12 test case scenarios are gathered, and each test case represents a specific combination of hoisting and slewing motion and payload mass to counterweight ratio, defining tower crane operational variations. This comprehensive data is investigated using a novel attention-based deep neural network with Tree-Structured Parzen Estimator optimization (TPE-AttDNN). The proposed TPE-AttDNN achieved a prediction accuracy of 0.95 with a false positive rate of 0.08. These results clearly demonstrate the effectiveness of the proposed model in accurately predicting the tower crane payload moving condition. To ensure a more reliable performance assessment of the proposed AttDNN, we carried out ablation experiments that highlighted the significance of the model’s individual components. Full article

Radar echo extrapolation (REE) is a crucial method for convective nowcasting, and current deep learning (DL)-based methods for REE have shown significant potential in severe weather forecasting tasks. Existing DL-based REE methods use extensive historical radar data to learn the evolution patterns of echoes, they tend to suffer from low accuracy. This is because data of radar modality face difficulty adequately representing the state of weather systems. Inspired by multimodal learning and traditional numerical weather prediction (NWP) methods, we propose a Multimodal Asymmetric Fusion Network (MAFNet) for REE, which uses data from radar modality to model echo evolution, and data from satellite and ground observation modalities to model the background field of weather systems, collectively guiding echo extrapolation. In the MAFNet, we first extract overall convective features through a global shared encoder (GSE), followed by two branches of local modality encoder (LME) and local correlation encoders (LCEs) that extract convective features from radar, satellite, and ground observation modalities. We employ an multimodal asymmetric fusion module (MAFM) to fuse multimodal features at different scales and feature levels, enhancing radar echo extrapolation performance. Additionally, to address the temporal resolution differences in multimodal data, we design a time alignment module based on dynamic time warping (DTW), which aligns multimodal feature sequences temporally. Experimental results demonstrate that compared to state-of-the-art (SOTA) models, the MAFNet achieves average improvements of 1.86% in CSI and 3.18% in HSS on the MeteoNet dataset, and average improvements of 4.84% in CSI and 2.38% in HSS on the RAIN-F dataset. Full article

This study investigates the probabilistic seismic damage characteristics of a five-span RC simply supported girder bridge with double-column piers designed for a high-speed railway (HSR). The objective is to assess the bridge’s fragility by developing a refined nonlinear numerical model using the OpenSEES (Version 3.3.0) platform. Incremental dynamic analysis (IDA) was conducted with peak ground accelerations (PGA) ranging from 0.05 g to 0.5 g, and fragility curves for pier columns, tie beams, and bearings were developed. Additionally, a series–parallel relationship and a hierarchically iterated pair copula model were established to evaluate system fragility. The results indicate that as PGA increases, the damage probability of all bridge components rises, with bearings being the most vulnerable, followed by pier columns, and tie beams exhibiting the least damage. The models accurately simulate the correlations between members and system fragility, offering valuable insights into the bridge’s performance under seismic conditions. Full article

This paper presents an analysis of the Hamiltonian formulation for continuous systems with second-order derivatives derived from Dirac’s theory. This approach offers a unique perspective on the equations of motion compared to the traditional Euler–Lagrange formulation. Focusing on Podolsky’s generalized electrodynamics, the Hamiltonian and corresponding equations of motion are derived. The findings demonstrate that both Hamiltonian and Euler–Lagrange formulations yield equivalent results. This study highlights the Hamiltonian approach as a valuable alternative for understanding the dynamics of second-order systems, validated through a specific application within generalized electrodynamics. The novelty of the research lies in developing advanced theoretical models through Hamiltonian formalism for continuous systems with second-order derivatives. The research employs an alternative method to the Euler–Lagrange formulas by applying Dirac’s theory to study the generalized Podolsky electrodynamics, contributing to a better understanding of complex continuous systems. Full article

Low-frequency oscillation (LFO) poses significant challenges to the dynamic performance of hydropower unit regulation systems (HURS) in hydropower units sharing a tailwater system. Previous methods have struggled to effectively suppress LFO, due to limitations in governor parameter optimization strategies. To address this issue, this paper proposes a governor parameter optimization strategy based on the crayfish optimization algorithm (COA). Considering the actual water diversion layout (WDL) of a HURS, a comprehensive mathematical model of the WDL is constructed and, combined with models of the governor, turbine, and generator, an overall HURS model for the shared tailwater system is derived. By utilizing the efficient optimization performance of the COA, the optimal PID parameters for the HURS controller are quickly obtained, providing robust support for PID parameter tuning. Simulation results showed that the proposed strategy effectively suppressed LFOs and significantly enhanced the dynamic performance of the HURS under grid-connected conditions. Specifically, compared to before optimization, the optimized system reduced the oscillation amplitude by at least 30% and improved the stabilization time by at least 25%. Additionally, the impact of the power grid system parameters on oscillations was studied, providing guidance for the optimization and tuning of specific system parameters. Full article

The Arabian Peninsula (AP) is notable for its unique meteorological and climatic patterns and plays a pivotal role in understanding regional climate dynamics and dust emissions. The scarcity of ground-based observations makes atmospheric data essential, rendering reanalysis and satellite products invaluable for understanding weather patterns and climate variability. However, the accuracy of these products in the AP’s desert environment has not been extensively evaluated. This study undertakes the first comprehensive validation of reanalysis products—the European Centre for Medium-Range Weather Forecasts’ European Reanalysis version 5 (ERA5) and ERA5 Land (ERA5L), along with Clouds and Earth’s Radiant Energy System (CERES) radiation fluxes—against measurements from the Liwa desert in the UAE. The data, collected during the Wind-blown Sand Experiment (WISE)–UAE field experiment from July 2022 to December 2023, includes air temperature and relative humidity at 2 m, 10 m wind speed, surface pressure, skin temperature, and net radiation fluxes. Our analysis reveals a strong agreement between ERA5/ERA5L and the observed diurnal T2m cycle, despite a warm night bias and cold day bias with a magnitude within 2 K. The wind speed analysis uncovered a bimodal distribution attributed to sea-breeze circulation and the nocturnal low-level jet, with the reanalysis overestimating the nighttime wind speeds by 2 m s⁻¹. This is linked to biases in nighttime temperatures arising from an inaccurate representation of nocturnal boundary layer processes. The daytime cold bias contrasts with the excessive net radiation flux at the surface by about 50–100 W m⁻², underscoring the challenges in the physical representation of land–atmosphere interactions. Full article

Magnetic nanoparticles (MNPs) can be functionalized with antibodies to give them an affinity for a biomarker of interest. Functionalized MNPs (fMNPs) cluster in the presence of a multivalent target, causing a change in their magnetization. Target concentration can be proportional to the 3rd harmonic phase of the fMNP magnetization signal. fMNP clustering can also be induced with salt. Generally, salt can alter the stability of charge stabilized fMNPs causing a change in magnetization that is not proportional to the target concentration. We have developed a model system consisting of biotinylated MNPs (biotin-MNPs) that target streptavidin to study the effects of salt concentration on fMNP-based biosensing in simulated in vivo conditions. We have found that biotin-MNP streptavidin targeting was independent of salt concentration for 0.005x to 1.00x phosphate buffered saline (PBS) solutions. Additionally, we show that our biosensor’s measurable concentration range (dynamic range) can be tuned with biotin density. Our results can be leveraged to design an in vivo nanoparticle (NP)-based biosensor with enhanced efficacy in the event of varying salt concentrations. Full article