Search Results (477)

This research investigates the shear performance of sustainable self-compacting reinforced geopolymer concrete (GPC) beams incorporating granite waste powder (GWP) and ground granulated blast-furnace slag (GGBFS) as eco-friendly binding agents through experimental and numerical analyses. Five geopolymer reinforced concrete beam specimens (100 mm × 150 mm × 1500 mm) were tested under two-point loading conditions to evaluate the influence of longitudinal reinforcement ratio (0.85% to 2.0%) and shear span-to-effective depth ratio on the structural shear performance. The experimental investigation revealed that geopolymer reinforced concrete beams exhibit shear behavior characteristics similar to conventional Portland cement concrete beams, with the 2.0% reinforcement ratio achieving 18.3% higher shear strength compared to the 0.85% reinforcement ratio, while shear capacity increased proportionally with increasing shear span-to-depth ratio. Experimental data, including load–displacement response, shear strength measurements, strain distributions, failure modes, and crack patterns, were studied. Finite element nonlinear analysis was conducted by modifying the concrete modulus and stress–strain relationships to reflect the properties of geopolymer concrete using ABAQUS software integrated with the concrete damaged plasticity model. The results demonstrated that for the tested geopolymer reinforced concrete beams, first cracking load, steel yielding load, and ultimate load capacity increased systematically with increasing tension steel reinforcement ratio and proportionally with higher shear span-to-depth ratios. Full article

To address the challenge that existing vehicle chassis coordinated control methods struggle to balance the nonlinear couplings and control conflicts among Four-Wheel Steering (4WS), Direct Yaw-moment Control (DYC), and Active Suspension Systems (ASS), this paper proposes a Cooperative Distributed Model Predictive Control (Co-DMPC) strategy. First, the 4WS, DYC, and ASS are modeled as three interacting agents that effectively mitigate inter-subsystem control conflicts through information exchange and coupling compensation. Second, a Gaussian Mixture Model (GMM) is utilized to extract features from vehicle state data to enable the real-time grading of instability risks, which dynamically adjusts the control weights of the 4WS, DYC, and ASS agents. Finally, a distributed iterative optimization algorithm is designed to ensure that all agents converge to a global Pareto-optimal solution through rapid negotiation, achieving a balance between control performance and computational burden. Simulation results demonstrate that compared with No-Control and CMPC, the proposed Co-DMPC strategy significantly enhances the comprehensive performance of the vehicle. In terms of path tracking accuracy, the maximum tracking errors under high- and low-adhesion road conditions are reduced by 32.73% and 17%, respectively. Regarding roll stability, the peak roll angles of the vehicle are 0.27 rad and 0.01 rad under the respective conditions. For lateral stability, the proposed method maintains a more compact sideslip angle-yaw rate phase plane envelope, effectively achieving the coordinated optimization of chassis subsystems. Hardware-in-the-Loop (HIL) experiments further validate the performance and effectiveness of the controller. Full article

The optimization of the MOSFET gate drive parameters is crucial for the trade-off between switching loss and electromagnetic interference (EMI). However, the nonlinear coupling among gate drive parameters, board-level parasitic, and switching performance limits the effectiveness of traditional MOSFET drive design methods. This paper proposes an adaptive tuning framework based on the proximal policy optimization (PPO) algorithm. An analytical switching model incorporating board-level parasitics is first derived to analyze the coupling between drive parameters and switching performance. The optimization problem is then formulated as a Markov decision process (MDP). Within this framework, domain randomization is applied during training. This enables the agent to learn a generalizable optimization strategy that remains robust across the varying parasitic inductances encountered in different PCB layouts. Compared to the traditional Non-dominated Sorting Genetic Algorithm II (NSGA-II), the proposed method uses the trained policy for direct inference. This reduces computation time by $98.7\%$ while maintaining a multi-objective performance difference within $10.06\%$. In addition, hardware verification shows a 10.7% average deviation between the measured and simulated results. These results demonstrate that the proposed method provides an efficient and scalable solution for MOSFET gate drive optimization. Full article

The high-speed Electromagnetic Suspension (EMS) maglev guidance system exhibits inherent characteristics of strong nonlinearity, parameter time-variation, and complex external disturbances. To further optimize and improve the control performance of the guidance system for high-speed maglev trains, a novel intelligent control strategy that integrates the Deep Deterministic Policy Gradient (DDPG) algorithm with Super-Twisting Sliding Mode Control (STSMC) is proposed. Focusing on a single-ended guidance unit with differential control of dual electromagnets, an STSMC controller is first designed based on a cascaded control framework. To overcome the limitation of offline parameter tuning in dynamic operational conditions, a reinforcement learning optimization framework employing DDPG is introduced. A multi-objective hybrid reward function is formulated, incorporating error convergence, sliding mode stability, and chattering suppression, thereby realizing the online self-tuning of core STSMC parameters via real-time interaction between the agent and the environment. Numerical simulations under typical disturbance conditions verify that the proposed DDPG-STSMC controller significantly reduces the amplitude of guidance gap variation and accelerates dynamic recovery compared to conventional PID control. Its superior performance in disturbance rejection, control accuracy, and operational adaptability is validated. This study, conducted through high-fidelity numerical simulations based on actual system parameters, provides a robust theoretical foundation for subsequent hardware-in-the-loop (HIL) experimentation. Full article

This study introduces a novel robust finite-time adaptive sliding mode control (FTSMC) strategy, emphasizing its contributions to the synchronized deployment of hydraulically actuated multi-arm systems in confined environments, such as coal bunker cleaning. Key innovations include the integration of adaptive sliding mode control with guaranteed finite-time convergence, a distributed leader–follower framework, and a graph-theoretical communication topology for localized interactions. Specifically, we developed a dynamic model for a multi-agent system comprising one leader and multiple followers, incorporating nonlinear dynamics and unknown external disturbances. The proposed controller ensures rapid finite-time convergence of tracking errors while maintaining robustness against parameter uncertainties, frictional forces, and external perturbations. The theoretical analysis, based on Lyapunov stability, rigorously proves the boundedness and convergence of all system states. Simulation results on a three-arm robotic platform validate the method’s superiority, demonstrating higher tracking accuracy, faster convergence, and stronger disturbance rejection compared with baseline controllers, including SMC, ETASMC, PID, Fixed-Time Consensus Control (FTCC), Disturbance Observer-Based Control (DOBC), and Adaptive Sliding Mode Control (ASMC). This research provides a practical and scalable solution for multi-arm coordination in unstructured environments, significantly advancing the autonomy and reliability of industrial robotic systems. Full article

Addressing the multi-constraint, nonlinear optimization challenge of trajectory planning for multi-rotor agents in urban high-rise environments, this paper proposes an improved hybridization of Harris hawks optimization (HHO) with a pigeon-inspired optimization (PIO) algorithm, termed improved hybridization of Harris hawks with pigeon-inspired optimization (IHHHPIO). Conventional intelligent optimization algorithms often suffer from slow convergence rates or susceptibility to local optima in such complex scenarios. This research establishes a hierarchical collaborative search framework, where the HHO algorithm acts as a top-level coordinator for global exploration and region allocation, while the PIO algorithm functions as a bottom-level searcher for fine-grained optimization within designated areas. The two algorithms collaborate through a bidirectional information exchange mechanism: HHO guides the local search direction of each PIO group with global best-position information, and each PIO group feeds back its locally optimal solutions to HHO for updating the global optimum. Simulation results demonstrate that the proposed IHHHPIO algorithm significantly outperforms both standard PIO and HHO algorithms in terms of convergence speed, solution accuracy, and stability, effectively planning safe, efficient, and collision-free flight trajectories. This work provides a reliable solution for agent logistics applications in complex urban environments. A certain limitation of this work lies in its validation solely through simulation, without physical experimental verification. Full article

While it is a global imperative that firms should achieve superior environmental, social, and governance (ESG) performance, the specific impact of ESG on export product quality remains under-explored. Based on stakeholder theory and principal–agent theory, this paper utilizes a sample of Chinese listed companies and the High-Dimensional Fixed Effects (HDFE) Model to empirically examine the impact and underlying mechanisms of ESG performance on export product quality. The results indicate a U-shaped relationship between ESG performance and export product quality, a non-linear correlation that has received limited attention in the previous literature. This U-shaped relationship is more pronounced among state-owned enterprises (SOEs), firms producing non-high-tech products, and those in heavy-polluting industries. Mechanism analysis reveals that ESG performance influences export product quality primarily through three channels: innovation levels, total factor productivity (TFP), and supply chain stability. By unveiling these non-linear dynamics and their underlying pathways, this study provides a novel theoretical framework and critical empirical evidence that reconcile conflicting views on ESG effects. These findings offer important insights for policymakers and exporters seeking to align ESG practices with export objectives, thereby contributing to more sustainable and high-quality development of foreign trade in China and beyond. Full article

With the advancement of smart education and carbon neutrality goals, optimizing Indoor Environmental Quality (IEQ) while minimizing energy consumption is critical. Traditional PID or rule-based strategies struggle with the strong non-linearity and time delays of photothermal coupling in high-density classrooms. This paper proposes an adaptive closed-loop control framework fusing Digital Twin (DT) and Deep Reinforcement Learning (DRL). A high-fidelity multi-physics model is constructed as a virtual testbed, utilizing the Proximal Policy Optimization (PPO) algorithm to learn multi-objective strategies. The trained agent is deployed to an edge gateway for real-time inference. Experimental results from a field study distinguish this work from pure simulations. Results demonstrate that compared to PID baselines, the proposed strategy reduces energy consumption by 28.4% while maintaining thermal comfort (PMV) and visual comfort compliance. Furthermore, the variance of PMV is reduced by 66.7%, and system recovery time under stochastic disturbances is shortened by 31.4%. Full article

With more renewables being integrated into distribution grids, the problem of voltage fluctuation has become prominent. Effective Volt/VAR regulation is a commonly used method to ensure the safe operation of distribution networks. Model-based approaches tend to work well only if detailed network parameters are available, while data-driven approaches can suffer from overfitting and may not generalize well. We created the PHY-GAT-SAC framework to address these issues. Physics-regularized reinforcement learning uses bidirectional graph attention, which combines a physics-informed model with a safety projection method that relies on sensitivity matrices. This makes it so that the voltage regulation is practical, interpretable, and secure. The framework works with two combined branches. One branch takes care of the nonlinear mapping from power injections to voltage states using a forward graph encoder and a reverse consistency constraint. At the same time, another branch extracts features directly from the voltages to improve the perception of system violation risk. The framework has a sensitivity-based safety layer as well. This layer projects every control action into a feasible area formed by linearized voltage restrictions, thus securing operation safety. Experiments on an IEEE 33-node system show that the framework works well. A safety layer guarantees a safe operating range without exact impedance values. And PHY-GAT-SAC greatly lowers voltage violations compared to multi-agent deep reinforcement learning. By successfully combining physics with learning, this study gives a unified framework for merging graph neural networks and reinforcement learning within intricate grid management. Full article

To address the limited dynamic voltage regulation performance of LLC resonant converters under wide input voltage and load variations, a reinforcement learning-based voltage control strategy is proposed in this paper. The twin delayed deep deterministic policy gradient (TD3) algorithm is adopted to learn the nonlinear mapping between system states and control actions, enabling adaptive adjustment of the converter operating parameters. Based on the established LLC resonant converter simulation model, the state space, action space, and reward function of the agent are designed to ensure rapid control response to abrupt changes in input voltage and load. Compared with the conventional PI control strategy, the proposed TD3-based strategy provides faster control actions during operating condition transitions, effectively suppressing output voltage overshoot and undershoot, and shortening the settling time. Simulation results verify that the proposed method achieves improved dynamic response performance under various operating conditions, demonstrating its effectiveness and superiority in LLC resonant converter voltage regulation. Full article

This paper introduces a distributed sensitivity-conditioning approach for bilevel optimization in networked microgrids. The proposed method enhances the coordination between subsystems by embedding sensitivity-based predictive terms into the dynamic updates, thereby improving convergence stability without requiring strict time-scale separation. Unlike conventional singular perturbation techniques, the sensitivity-conditioning formulation enables faster and more robust convergence of the distributed dynamics under heterogeneous subsystem speeds. The approach is applied to a networked microgrid scenario where local agents perform decentralized optimization considering both internal generation and energy exchange with neighboring microgrids. Simulation results demonstrate that the proposed algorithm achieves efficient coordination, reduces convergence time, and maintains stability under diverse operating conditions. The results highlight the method’s potential as a scalable and computationally efficient alternative for real-time distributed energy management and bilevel control in power network applications. Full article

Biodegradable electrospun nanofibrous scaffolds (BENS) have emerged as a highly advanced class of wound dressings owing to their close structural and morphological resemblance to the native extracellular matrix and their tunable physicochemical and mechanical characteristics. However, the successful translation of electrospun wound-healing platforms from laboratory concepts to clinically viable products necessitates a quantitative understanding of how formulation and processing variables dictate scaffold architecture, mechanical performance, and antibacterial functionality. In this study, hydrophobic poly(ε-caprolactone) (PCL) and hydrophilic poly(ethylene glycol) (PEG35000) were blended at different weight ratios and fabricated into electrospun nanofibrous scaffolds, with amoxicillin trihydrate (AMX) incorporated as a model antibacterial agent. Blank and drug-loaded systems were systematically characterized with respect to solution rheology, fiber morphology, thermal behavior, crystallinity, mechanical performance, surface wettability, and antibacterial activity. Quantitative correlation analyses and statistical comparisons revealed that solution viscosity is a strong predictor of mechanical response, while PEG fraction governs baseline stiffness and crystallinity in a non-linear manner. AMX loading acted as a secondary structural modifier, producing statistically significant increases in stiffness and wettability, accompanied by reduced crystallinity and concentration-dependent antibacterial efficacy. Among the investigated formulations, a PCL: PEG ratio of 3:1 provided the most balanced mechanophysical profile for effective drug incorporation. These findings establish validated structure–property–function relationships that support the rational design of electrospun antibacterial wound dressings. Full article

This paper investigates the distributed event-triggered fixed-time time-varying formation control problem for a class of nonlinear multi-agent systems subject to model uncertainties and unknown time-varying disturbances. To address issues in traditional formation control methods, such as convergence time dependence on initial states and high communication resource consumption, a distributed cooperative control scheme integrating fixed-time control, event-triggered mechanisms, and dynamic surface control is proposed. Firstly, a fixed-time disturbance observer is designed to accurately estimate the agents’ lumped disturbances within a fixed time independent of initial conditions. Secondly, by incorporating dynamic surface control techniques, a distributed event-triggered formation control law is constructed, effectively reducing communication and computational resource usage. Furthermore, using Lyapunov stability theory, the closed-loop system is proven to exhibit practical fixed-time stability, and the existence of a positive lower bound for triggering intervals precludes Zeno behavior. Finally, numerical simulations validate the superiority of the proposed method in terms of convergence speed, control accuracy, and resource efficiency. This research provides an efficient, robust, and resource-friendly solution for cooperative control of multi-agent systems in complex environments. Full article

One of the major challenges in autonomous path following for unmanned surface vehicles (USVs) is the impact of stochastic environmental forces—primarily wind, waves and currents—which introduce nonlinearities that affect control models. Conventional strategies often rely on minimizing cross-track error, resulting in a reactive system that corrects heading only after a disturbance has displaced the vessel, potentially leading to oscillatory behavior and reduced precision. Deep Reinforcement Learning (DRL) is successfully used for a wide range of nonlinear control tasks. It has already been shown that robust solutions that can handle disturbances such as sensor noise or changes in system dynamics can be obtained. This study investigates whether an agent, provided it can explicitly observe disturbances, can go beyond simply correcting deviations and autonomously learn the correlation between environmental conditions and necessary counter-forces. We show that integrating the wind vector directly into the agent’s observation space allows a Proximal Policy Optimization (PPO) policy to decouple the environmental cause from the kinematic effect, facilitating drift compensation before significant errors accumulate. By systematically comparing agents trained with randomized wind scenarios, we found that agents that can observe the wind can achieve goal reaching rates of up to 99.0% and reduce the spread of path deviation and velocity in our tested scenarios. Furthermore, our results quantify a distinct Pareto frontier between navigational velocity and tracking precision, demonstrating that explicit disturbance perception improves consistency, although robust implicit training already provides substantial resilience. These findings indicate that augmenting state observations with environmental data enhances the stability of learning-based controllers. Full article

Underground coal gasification (UCG) is a coal utilization technology that has attracted extensive attention over the years. In order to study the distribution and evolution law of the growth boundary of a coal gasification cavity under UCG, COMSOL numerical simulation software was used to conduct a multi-physical field-coupling numerical simulation of its growth process. In this study, we established a gasification reaction model of the cavity, and after simulation calculation, the growth boundary of the gasification cavity was obtained. Multiple data points were taken from the growth boundary of the gasification cavity for the fitting calculation, and the fitting function $y=F\left(x\right)$ of the gasification boundary growth was obtained. The core insight from this study is that a gasification boundary growth fitting function $y=F\left(x\right)$ was cross-fitted based on seven different gasification times $t$ (5 d, 20 d, 40 d, 60 d, 80 d, 110 d, 150 d) and 10 different gasification agent inflow velocities $v$ (0.1 m/s, 0.3 m/s, 0.5 m/s, 0.7 m/s, 1 m/s, 2 m/s, 4 m/s, 6 m/s, 8 m/s, 10 m/s) as orthogonal independent variables. An innovative multi-parameter fitting equation was constructed, $y=F\left(x,t,v\right)$, with the gasification time $t$ and the gasification agent inflow velocity $v$ as independent variables. This fitting equation, $y=F\left(x,t,v\right)$, can dynamically depict the gasification cavity boundary during the UCG process when different gasification times $t$ and gasification agent inflow velocities $v$ are inputted. The novelty of this study lies in the fact that it breaks through the limitations of traditional numerical simulation models that rely on a single variable, have limited adaptability, and focus on gasification cavities that lie mostly in the side-view direction. Moreover, through a multi-physics field-coupling numerical simulation in the top-view direction of the gasification cavity, we have improved the construction of the UCG numerical simulation model and cross-fitted the gasification boundary with respect to the gasification time $t$ and gasification agent inflow velocity $v$ to construct a fitting equation, achieving the quantitative representation of the nonlinear relationship between variables. Full article