Search Results (666)

This study focuses on the adaptive tracking control issue for nonlinear multi-agent systems (MASs) under the influence of asymmetric input constraints and external disturbances. Firstly, an auxiliary system is proposed, which can ensure flexible prescribed performance under input saturation conditions. Meanwhile, by introducing a transformation function, the distributed errors are freed from initial constraints. Employing the backstepping method, the adaptive technique, and a neural network approximation technology, a finite-time prescribed performance adaptive tracking control algorithm is designed, enabling the tracking errors to stably converge within the prescribed performance bounds. Secondly, a composite disturbance observer is developed to estimate and mitigate the combined disturbances, which include external perturbations and approximation errors from radial basis function neural networks (RBF NNs). It not only achieves effective disturbance compensation but also further suppresses the approximation errors of RBF NNs. Finally, stability analysis using the Lyapunov function demonstrates that all closed-loop signals remain uniformly ultimately bounded (UUB), with adaptive tracking errors converging to a compact region within a finite time. Simulation results and comparative studies confirm the proposed method’s effectiveness and advantages, providing a basis for its practical use in distributed control applications. Full article

High-fidelity finite element analysis (FEA) plays a key role in structural engineering by enabling accurate simulation of displacement, stress, and internal forces under static loads. However, its high computational cost limits applicability in real-time control, iterative design, and large-scale uncertainty quantification. Surrogate modeling provides a computationally efficient alternative by learning input–output mappings from precomputed simulations. Yet, the performance of individual surrogates is often sensitive to data distribution and model assumptions. To enhance both accuracy and robustness, we propose a model averaging framework based on Jackknife Model Averaging (JMA) that integrates six surrogate models: polynomial response surfaces (PRSs), support vector regression (SVR), radial basis function (RBF) interpolation, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Three ensembles are formed: JMA1 (classical models), JMA2 (tree-based models), and JMA3 (all models). JMA assigns optimal convex weights using cross-validated out-of-fold errors without a meta-learner. We evaluate the framework on the Static Analysis Dataset with over 300,000 FEA simulations. Results show that JMA consistently outperforms individual models in root mean squared error, mean absolute error, and the coefficient of determination, while also producing tighter, better-calibrated conformal prediction intervals. These findings support JMA as an effective tool for surrogate-based structural analysis. Full article

Epoxy asphalt is a superior polymer-modified asphalt material; however, significant differences in physicochemical properties, such as solubility parameters and dielectric constants, between epoxy resin and asphalt have led to compatibility issues that hinder its development. This study employed molecular dynamics simulations to investigate the effect of maleic anhydride-grafted styrene-butadiene-styrene (MAH-g-SBS) on the compatibility of epoxy asphalt. By analyzing parameters such as cohesive energy density, solubility parameters, energy distribution, interaction energy, radial distribution function, free volume fraction, and mean square displacement, the molecular mechanism underlying the enhanced compatibility was elucidated. The results indicate that the amphiphilic molecular structure of MAH-g-SBS significantly improves the thermodynamic compatibility between asphalt and epoxy resin, enhances interfacial affinity and stability, reduces the system’s total interaction and nonbonded energies, facilitates the dispersion and permeation of epoxy molecules into asphalt, and increases molecular mobility, thereby comprehensively enhancing the compatibility of the epoxy asphalt blend. Segregation tests and fluorescence microscopy further verified the simulation results, demonstrating that MAH-g-SBS improves the storage stability and phase uniformity of the epoxy asphalt system. Full article

In this paper, the molecular dynamics simulation method was adopted to systematically study the microstructure evolution behavior of TiAl alloys under impact compression under three typical crystal orientations ([001], [110], [111]). By analyzing the characteristics of structural phase transition, defect type evolution, dislocation expansion, and radial distribution function, the anisotropic response mechanism under the joint regulation of crystal orientation and impact velocity was revealed. The results show that the [111] crystal orientation is most prone to local amorphous transformation at high strain rates, and its structural collapse is due to the rapid accumulation and limited reconstruction of dislocations/faults. The [001] crystal orientation is prone to forming staggered stacking of layers and local HCP phase transformation, presenting as a medium-strength structural disorder. Under the strain regulation mechanism dominated by twinning, the [110] orientation exhibits superior structural stability and anti-disorder ability. With increases in the impact velocity, the defect type gradually changes from isolated dislocations to large-scale HCP regions and amorphous bands, and there are significant differences in the critical velocities of amorphous transformation corresponding to different crystal orientations. Further analysis indicates that the HCP structure and the formation of layering faults are important precursor states of amorphous transformation. The evolution of the g(r) function verifies the stepwise disintegration process of medium and long-range ordered structures under shock induction. It provides a new theoretical basis and microscopic perspective for the microstructure regulation, damage tolerance improvement, and impact resistance design of TiAl alloys under extreme stress conditions. Full article

The development of high-performance solid electrolytes is critical to advancing solid-state lithium-ion batteries (SSBs), with lithium lanthanum zirconium oxide (LLZO) emerging as a leading candidate due to its chemical stability and wide electrochemical window. In this study, we systematically investigated the effects of cation dopants, including aluminum (Al³⁺), tantalum (Ta⁵⁺), gallium (Ga³⁺), and rubidium (Rb⁺), on the structural, electronic, and ionic transport properties of LLZO using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. It appeared that, among all simulated results, Al-LLZO exhibits the highest ionic conductivity of 1.439 × 10⁻² S/cm with reduced activation energy of 0.138 eV, driven by enhanced lithium vacancy concentrations and preserved cubic-phase stability. Ta-LLZO follows, with a conductivity of 7.12 × 10⁻³ S/cm, while Ga-LLZO and Rb-LLZO provide moderate conductivity of 3.73 × 10⁻³ S/cm and 3.32 × 10⁻³ S/cm, respectively. Charge density analysis reveals that Al and Ta dopants facilitate smoother lithium-ion migration by minimizing electrostatic barriers. Furthermore, Al-LLZO demonstrates low electronic conductivity (1.72 × 10⁻⁸ S/cm) and favorable binding energy, mitigating dendrite formation risks. Comparative evaluations of radial distribution functions (RDFs) and XRD patterns confirm the structural integrity of doped systems. Overall, Al emerges as the most effective and economically viable dopant, optimizing LLZO for scalable, durable, and high-conductivity solid-state batteries. Full article

Parametric changes in the first coordination shell (FCS) of a vitreous metallic Pd_42.5Cu₃₀Ni_7.5P₂₀ alloy are analysed, aiming to confirm the identification of the glass transition temperature (T_g) via processing of XRD patterns utilising radial and pair distribution functions (RDFs and PDFs) and their evolution with temperature. The Wendt–Abraham empirical criterion of glass transition and its modifications are confirmed in line with previous works, which utilised the kink of the temperature dependences of the minima and maxima of both the PDF and the maxima of the structure factor S(q). Massive fluctuations are, however, identified near the T_g of the derivatives of the minima and maxima of the PDF and maxima of S(q), which adds value to understanding the glass transition in the system as a true second-order-like phase transformation in the non-equilibrium system of atoms. Full article

The dynamic model of a wheeled wall-climbing robot exhibits stage-specific changes when traversing different types of obstacles and during various stages of obstacle negotiation. Previous studies often employed remote control methods for obstacle-crossing control, which fail to dynamically adjust the torque distribution of magnetic wheels in response to real-time changes in the dynamic model. This limitation makes it challenging to precisely control the robot’s speed and attitude angles during the obstacle-crossing process. To address this issue, this paper first establishes a staged dynamic model for the wall-climbing robot under typical obstacle-crossing scenarios, including steps, 90° concave corners, 90° convex corners, and thin plates. Secondly, an adaptive controller based on a radial basis function neural network (RBFNN) is designed to effectively compensate for variations and uncertainties during the obstacle-crossing process. Finally, comparative simulations and physical experiments demonstrate the effectiveness of the proposed method. The experimental results show that this method can quickly respond to the dynamic changes in the model and accurately track the trajectory, thereby improving the control precision and stability during the obstacle-crossing process. Full article

The geometric design of flow channels in bipolar plates is one of the critical features of proton exchange membrane fuel cells (PEMFCs), as it determines the power output of the fuel cell and has a significant impact on its performance and durability. The function of the bipolar plate is to guide the transfer of reactant gases to the gas diffusion layer and catalytic layer inside the PEMFC, while removing unreacted gases and gas–liquid byproducts. Therefore, the design of the bipolar plate flow channel is directly related to the water and thermal management of the PEMFC. In order to improve the comprehensive performance of PEMFCs and ensure their safe and stable operation, it is necessary to design the flow channels in bipolar plates rationally and effectively. This study addresses the limitations of existing bipolar plate flow channels by proposing a new coupling of serpentine and radial channels. The distribution of oxygen, water concentrations, and temperature inside the channel is simulated using the multi-physics simulation software COMSOL Multiphysics 6.0. The performance of this novel design is compared with conventional flow channels, with a particular focus on the pressure drop and current density to evaluate changes in the output performance of the PEMFC. The results show that the maximum current density of this novel design is increased by 67.36% and 10.43% compared to straight channel and single serpentine channels, respectively. The main contribution of this research is the innovative design of a new coupling of serpentine and radial channels in bipolar plates, which improves the overall performance of the PEMFC. This study provides theoretical support for the design of bipolar plate flow channels in PEMFCs and holds significant importance for the green development of energy. Full article

With the large-scale integration of renewable energy sources, traditional distribution networks are gradually evolving into a new form of flexible interconnection distribution networks. To enhance the rapidity and accuracy of power flow control through flexible interconnection devices, there is an increasing demand for precise grid equivalent models. Existing grid equivalent models predominantly adopt single-port configurations for radial networks, while there is limited research on two-port network equivalent models tailored for flexible interconnection distribution networks. Focusing on the scenario of flexible interconnection distribution networks integrated with Rotary Power Flow Controllers (RPFCs), this paper proposes an equivalent modeling method of two-port networks based on the superposition theorem under small disturbance conditions. A flexible interconnection distribution network model incorporating RPFCs and its corresponding two-port equivalent model are developed. The parameters of the two-port equivalent model are derived through superposition theorem calculations, enabling the realization of power decoupling control functionality for RPFCs. The simulation results show that the deviations between the set value of active power and the actual value remains at about 3%, and the deviations between the set value of reactive power and the actual value is between 4% and 7%, thereby verifying the effectiveness of the constructed two-port model in power flow control and further supporting the accuracy of the proposed method. Full article

This work studies the issue of distributed fault-tolerant control for a vehicle platoon with actuator faults, saturation, and external disturbances. As the degrees of wear, age, and overcurrent of a vehicle actuator might change during the working process, it is more practical to consider the actuator faults to be time-varying rather than constant. Considering a situation in which actuator faults may cause partial actuator effectiveness loss, a novel adaptive updating mechanism is developed to estimate this loss. A new nonlinear observer is proposed to estimate external disturbances without requiring us to know their upper bounds. Since non-zero initial spacing errors (ISEs) may cause instability of the vehicle platoon, a novel exponential spacing policy (ESP) is devised to mitigate the adverse effects of non-zero ISEs. Based on the developed nonlinear observer, adaptive updating mechanism, radial basis function neural network (RBFNN), and the ESP, a novel nonlinear observer-based distributed adaptive fault-tolerant control strategy is proposed to achieve the objectives of platoon control. Lyapunov theory is utilized to prove the vehicle platoon’s stability. The rightness and effectiveness of the developed control strategy are validated using a numerical example. Full article

(1) Background: A severe decline in knee joint function significantly affects the mobility of the elderly, making it a key concern in the field of geriatric health. To alleviate the pressure on the knee joints of the elderly during daily movements such as sitting and standing, effective biomechanical solutions are required. (2) Methods: In this study, a biomechanical framework was established based on mechanical analysis to derive the transfer relationship between the ground reaction force and the knee joint moment. Experiments were designed to collect knee joint data on the elderly during the sit-to-stand process. Meanwhile, magnetic resonance imaging (MRI) images were processed through a medical imaging control system to construct a detailed digital 3D knee joint model. A finite element analysis was used to verify the model to ensure the accuracy of its structure and mechanical properties. An improved radial basis function was used to fit the pressure during the entire sit-to-stand conversion process to reduce the computational workload, with an error of less than 5%. In addition, a small-target human key point recognition network was developed to analyze the image sequences captured by the camera. The knee joint angle and the knee joint pressure distribution during the sit-to-stand conversion process were mapped to a three-dimensional interactive platform to form a digital twin system. (3) Results: The system can effectively capture the biomechanical behavior of the knee joint during movement and shows high accuracy in joint angle tracking and structure simulation. (4) Conclusions: This study provides an accurate and comprehensive method for analyzing the biomechanical characteristics of the knee joint during the movement of the elderly, laying a solid foundation for clinical rehabilitation research and the design of assistive devices in the field of rehabilitation medicine. Full article

In the framework of harmonic and Clifford analysis, specific distributions in Euclidean space of arbitrary dimension, which are of particular importance for theoretical physics, are once more thoroughly studied. Indeed, actions involving spherical coordinates, such as the radial derivative and multiplication and division by the radial distance, only make sense when considering so-called signumdistributions, that is, bounded linear functionals on a space of test functions showing a singularity at the origin. Introducing these signumdistributions, the actions of a whole series of scalar and vectorial differential operators on the distributions under consideration, lead to a number of surprising results, as illustrated by some examples in three-dimensional mathematical physics. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

The ocean encompasses the majority of the Earth’s surface and harbors substantial energy resources. Nevertheless, the intricate and asymmetrically distributed underwater environment renders existing target detection performance inadequate. This paper presents an enhanced YOLOv8s approach for underwater robot object detection to address issues of subpar image quality and low recognition accuracy. The precise measures are enumerated as follows: initially, to address the issue of model parameters, we optimized the ninth convolutional layer by substituting certain conventional convolutions with adaptive deformable convolution DCN v4. This modification aims to more effectively capture the deformation and intricate features of underwater targets, while simultaneously decreasing the parameter count and enhancing the model’s ability to manage the deformation challenges presented by underwater images. Furthermore, the Triplet Attention module is implemented to augment the model’s capacity for detecting multi-scale targets. The integration of low-level superficial features with high-level semantic features enhances the feature expression capability. The original CIoU loss function was ultimately substituted with Shape IoU, enhancing the model’s performance. In the underwater robot grasping experiment, the system shows particular robustness in handling radial symmetry in marine organisms and reflection symmetry in artificial structures. The enhanced algorithm attained a mean Average Precision (mAP) of 87.6%, surpassing the original YOLOv8s model by 3.4%, resulting in a marked enhancement of the object detection model’s performance and fulfilling the real-time detection criteria for underwater robots. Full article

We propose a probabilistic shaping (PS) scheme based on a single-layer lookup table (LUT) that employs only one LUT for symbol mapping while achieving favorable system performance. This scheme reduces the average power of the signal by adjusting the symbol distribution using a specialized LUT architecture and a flexible shaping proportion. The simulation results indicate that the proposed PS scheme delivers performance comparable to that of the conventional constant-composition distribution-matching-based probabilistic shaping (CCDM-PS) algorithm. Specifically, it reduces the bit error rate (BER) from 1.2376 $\times {10}^{-4}$ to 6.3256 $\times {10}^{-5}$, corresponding to a 48.89% improvement. The radial basis function neural network (RBFNN) effectively compensates for nonlinear distortions and further enhances transmission performance due to its simple architecture and strong capacity for nonlinear learning. In this work, we combine lookup-table-based probabilistic shaping (LUT-PS) with RBFNN-based nonlinear equalization for the first time, completing the transmission of 16-QAM OFDM signals over a photonic terahertz-over-fiber system operating at 400 GHz. Simulation results show that the proposed approach reduces the BER by 81.45% and achieves a maximum Q-factor improvement of up to 23 dB. Full article