Search Results (3,847)

Soft robotics has become a dynamic field that emphasizes adaptability and safe interaction with complex environments. These structures utilize deformable materials and continuum mechanics to adapt their shape, absorb shocks, and perform tasks in unstructured environments. However, the design and optimization of these systems is challenging, primarily due to the nonlinear and discontinuous behavior of granular materials. In this paper, we address the role of simulation frames as an important tool for understanding, designing, and extending the functionality of software robotic devices utilizing granular jamming. The analysis suggests that DEM is essential for capturing particle-level mechanisms, while FEM is more effective for system-level optimization but tends to smooth out the transition of jamming. Hybrid FEM–DEM approaches provide the highest physical accuracy, albeit at an increased computational cost. Overall, the findings emphasize that the choice of framework must be application-oriented and that multiphysics coupling represents the future development. The review gives an up-do-date review of the simulation tools and approaches for granular-jamming-based systems with a specific focus on continuum arms with a granular-jamming-based central backbone. Such methods can be used for the optimization the back-bone geometry and its filling material (shape, porosity, granule size) with possible use in the real-time control of such arms. 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 cemented backfill mining method has progressively become the preferred mining technique for underground metal extraction due to its advantages such as environmental friendliness, high efficiency, and economic viability. The mechanical properties of the backfill are fundamental to ensuring effective strata control and structural stability within backfilled stopes. Hydration reaction serves as the critical factor in the formation of backfill mechanical properties, while temperature influences these properties by governing the progression of the hydration process. This paper systematically reviews five fundamental hydration models (NG, CEMHYD 3D, Krstulovic-Dabic, Heat of Hydration and Thermodynamic Phase Equilibrium), critically analyzing their limitations in predicting performance under extreme geothermal and cryogenic conditions. Distinct from previous reviews, this study reveals the nonlinear mapping between dynamic temperature fields and microstructural evolution. Furthermore, it incorporates recent advancements in multi-field coupling mechanisms and AI-driven strength prediction. Ultimately, this study establishes that with the emergence of advanced modeling software and machine learning algorithms, the investigation of temperature effects on backfill is poised to move toward a more comprehensive, intelligent, and refined direction. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article

Schistosomiasis elimination is increasingly constrained less by the technical efficacy of single interventions than by systemic dynamics in coupled human–animal–environment settings, including nonlinear feedback, spatial heterogeneity, and cross-sectoral govern frictions. We conducted a systematic methodological review (search date: 1 January 2026) across PubMed, Web of Science, Scopus, EconLit, and CNKI to identify studies that (i) addressed schistosomiasis control, (ii) used explicit system-based, causal, or network-oriented analytical structures, and (iii) incorporated economic evaluation with multi-domain outcomes. We synthesized modeling architectures, economic methods, and approaches to trade-offs and uncertainty, and applied an evidence-informed systemic causality framework to assess decision-analytic adequacy. The literature grouped into three related strands: transmission and system dynamics models that capture feedback processes and rebound risks; economic evaluations dominated by cost-effectiveness analyses; and cross-sectoral or surveillance-oriented decision models optimizing implementation under resource constraints. Across strands, elimination-stage investments such as surveillance, environmental management, and coordination exhibit strong externalities and quasi-public-good properties that are systematically undervalued in single-sector, single-metric frameworks. We argue that decision-relevant evaluation should be reframed as a multi-objective resource allocation problem that integrates systemic modeling with economic valuation, explicitly addresses uncertainty, and applies multi-criteria decision analysis to support long-horizon, cross-sectoral decision-making. 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 interaction between plasmonic nanoparticles and organic dye molecules plays an important role in varied photonic and optoelectronic applications. In this work, we systematically investigate the optical properties of a water-soluble perylenediimide derivative, N,N′-di(2-(trimethylammonium iodide) ethylene) perylenediimide (TAIPDI), in the presence of different concentrations of silver nanoparticles (AgNPs) under femtosecond (fs) laser excitation. The AgNPs were synthesized via the laser ablation technique. The influence of AgNP concentration on the linear, fluorescence, and nonlinear optical properties of the TAIPDI dye was explored through UV–visible absorption spectroscopy, fluorescence emission measurements, and open- and closed-aperture Z-scan techniques. The Ag NP–TAIPDI dye hybrid systems (Ag@TAIPDI nanocomposites) exhibited pronounced reverse saturable absorption and self-defocusing behavior, indicating a negative nonlinear refractive index. Both the nonlinear absorption coefficient and refractive index increased markedly with rising AgNP concentration, leading to a significant enhancement in the third-order nonlinear susceptibility. Fluorescence studies further revealed a concentration-dependent emission enhancement due to metal-enhanced fluorescence arising from surface plasmon resonance-induced local field amplification. The Ag@TAIPDI nanocomposites also demonstrated strong optical limiting performance, with the limiting threshold decreasing as the AgNP concentration increased. These findings highlight the synergistic role of plasmon–exciton coupling and thermal lensing in enhancing the nonlinear response of such nanocomposites. The results establish AgNPs–TAIPDI dye hybrid systems as promising materials for all-optical switching, optical limiting, and photonic device applications. Full article

Transverse mode instability (TMI) in high-power ytterbium-doped double-clad fiber lasers is widely interpreted as being a consequence of a thermo-optic nonlinear phenomenon driven by stimulated thermal Rayleigh scattering. This work presents a coupled optical–thermal model for a continuous-wave forward-pumped (${\lambda }_p=976\mathrm{nm}$) fiber amplifier emitting at ${\lambda }_s=1064\mathrm{nm}$ over an optimal length of 12 m. The formulation explicitly resolves the three radial regions of a double-clad fiber, avoiding single-clad approximations. Modal fields are computed using the weakly guiding approximation (WGA) in the core combined with the semi-WGA at the cladding interfaces, enabling accurate calculation of higher-order modes of penetration into the inner cladding and of the transverse eigenvalues $U_{01}$ and $U_{mn}$ relevant to TMI. Within this framework, the nonlinear stimulated thermal Rayleigh scattering coupling coefficient is evaluated, including gain saturation and the thermal eigenmodes of the multi-layer geometry. The results show that the inner cladding modifies both the optical and thermal mode structures, altering the optical–thermal overlap between ${\mathrm{LP}}_{01}$ and higher-order modes and changing the effective strength of STRS, directly influencing the predicted TMI threshold. The proposed formulation provides a quantitative and physically consistent tool for analyzing thermo–optic dynamics in Yb-double-clad fiber amplifiers and supports the design of next-generation high-power fiber lasers with improved modal stability. Full article

Professors, students, and researchers from universities around the world use software distributed under licenses for numerical simulation purposes, which requires a computer with considerable hardware capabilities. This implies a high cost of simulations in engineering applications that require dynamic modeling using numerical methods, particularly in robotics and nonlinear control. This article compares and analyzes the performance of a frugal simulation scheme based on the use of low-cost, free, and open-source technology, specifically a low-power, single-board minicomputer (Raspberry Pi) in conjunction with GNU-Octave software. The benchmark is a numerical simulation of trajectory tracking control in the joint space of a Selective Conformal Assembly Robot Arm (SCARA). To perform this task, a system of coupled nonlinear differential equations is solved in matrix form using a numerical method known as an ODE solver. This solution includes the control law and the dynamic system model derived from Euler–Lagrange formalism. The time complexity and accuracy are analyzed to compare the performance of the frugal simulation tool with that of a conventional simulation setup consisting of a personal computer and MATLAB^TM running the same simulation code. The analysis shows minimal deviations in the numerical solutions and reasonable time complexity. Moreover, the frugality score of this approach and the low acquisition cost of the simulation tool enable the creation of simulation laboratories at universities with limited budgets for education and research. Full article

Many industrial machines inevitably suffer from external impacts which can change the meshing state of gears and thus affect the vibration characteristics of the gear transmission system. Previous studies mostly directly applied external impact excitation to the gear pair, with few considering the gear–shaft-bearing system. In reality, external impact excitation first acts on the bearing ends and then is transmitted to the gear ends through the transmission shaft. Therefore, the paper established a bending–torsion coupled dynamic model of the gear–shaft-bearing transmission system, taking into account external impacts, gear eccentricity, time-varying meshing stiffness, transmission error, shafts elastic deformation and nonlinear reactions forces. The vibration characteristics of the bending–torsion coupled gear–shaft-bearing transmission system under external impacts were analyzed in the time and frequency domains. Additionally, the effects of impact load amplitude and impact duration on gear vibration characteristics were investigated. External impacts instantaneously amplified the vibrational energy of the gear pair, which promotes the generation of impact components and increases the vibration acceleration signal amplitude in the time domain. Distinct sidebands emerge in the frequency domain, with meshing impacts intensified during gear operation. Furthermore, as the impact load amplitude increases and the impact duration is shortened, the vibration characteristics of the gear transmission system become more pronounced. The findings provide important theoretical insights and practical engineering significance for improving the reliability and service life of gear transmission systems. Full article

To mitigate UAV (unmanned aerial vehicle) range limitation risks and scheduling disruptions caused by complex wind fields in urban instant delivery, this paper proposes a UAV–rider collaborative dispatching framework. By incorporating aerodynamic-based nonlinear energy dynamics, the model accurately characterizes power variations under stochastic wind conditions, significantly enhancing the operational reliability of urban delivery missions. First, an aerodynamic-based nonlinear energy function is constructed, coupling payload, airspeed, and random wind vectors to accurately characterize power variations. Second, a scenario-based two-stage stochastic programming framework is adopted, where the rider’s deterministic path is optimized in the first-stage decision to ensure stability, and the UAV’s scenario-dependent flight plan is resolved in the second stage to adapt to wind uncertainty. An improved branch-and-price (IBP) algorithm is designed to solve this large-scale model, where nonlinear energy is evaluated during label extension in the pricing sub-problem, effectively avoiding linearization errors. The numerical results demonstrate that the proposed framework improves the mission success probability (the likelihood of completing delivery routes without battery exhaustion across all considered wind scenarios) by 25% under strong-wind conditions by effectively avoiding power failure risks. Furthermore, the IBP algorithm outperforms traditional exact solvers by over 40% in solution efficiency for large-scale cases. These findings demonstrate that energy-aware stochastic dispatching significantly improves the reliability and robustness of UAV-assisted last-mile delivery in windy urban environments, thereby providing an effective operational solution for real-world drone delivery logistics. Full article

To address the trajectory tracking problem of underwater manipulators operating in complex marine environments with strong multi-degree-of-freedom coupling, pronounced nonlinearities, and actuator saturation constraints, this paper proposes a super-twisting sliding mode control scheme integrated with an extended state observer and an anti-saturation auxiliary system. A dynamic model of the underwater manipulator incorporating major hydrodynamic effects (added mass and drag) is first established. Based on this model, a super-twisting sliding mode controller is designed to achieve fast convergence of the tracking errors while effectively alleviating the chattering phenomenon associated with conventional sliding mode control. An improved extended state observer is then introduced to estimate unmodeled dynamics and external time-varying disturbances in real time, providing feedforward compensation to enhance system robustness. To explicitly handle actuator output limitations, an anti-saturation auxiliary system is further developed to dynamically regulate the control input and mitigate the adverse effects of saturation. Comparative simulation studies conducted on the Oberon7 underwater manipulator demonstrate that the proposed control strategy achieves higher trajectory tracking accuracy, improved disturbance rejection capability, and faster recovery after saturation release compared with conventional control methods. These results indicate that the proposed approach offers an effective and reliable solution for high-precision trajectory tracking control of underwater manipulators under input saturation constraints. Full article

The large-scale integration of wind turbine generators (WTGs) and photovoltaic (PV) generation increases operational uncertainty and can exacerbate stability limitations in weak transmission networks, motivating the use of green hydrogen energy storage systems (HESS). This paper presents a probabilistic planning framework for the joint siting and sizing of HESS to support hybrid WTG–PV integration under stochastic wind, solar irradiance, and load conditions. The proposed framework explicitly couples Monte Carlo-based probabilistic power flow with weak-grid security constraints by enforcing FVSI-based voltage-stability limits and an SSI-based system-strength requirement within the optimization loop, rather than treating these indices as post-analysis checks. The planning problem is formulated using a weighted-sum scalarization to minimize life-cycle carbon footprint and active power losses, subject to security constraints based on the Fast Voltage Stability Index (FVSI) and a system-strength constraint expressed through a System Strength Index (SSI). To solve the resulting constrained, nonlinear optimization problem, a sequential hybrid metaheuristic that couples Whale Optimization (exploration) with Osprey Optimization (exploitation) is developed. The framework is implemented in MATLAB using MATPOWER and evaluated on a modified IEEE 39-bus system. Simulation results report an annual carbon footprint of 22.16 Mt CO₂eq/yr, an improvement of 9.2% and 5.3% relative to PSO and GA/PSO baselines, respectively, while increasing the weakest-bus SSI to 4.68 (bus 7). The resulting HESS design comprises a 296.9 MW electrolyzer, a 262.7 MW fuel cell, and 28,012 kg of hydrogen storage. Full article

During laser processing, optimizing the cutting performance by adjusting the angle or off-axis displacement between the auxiliary gas flow and the laser beam is an effective approach to improving processing quality and efficiency. However, traditional electromechanical actuators suffer from inherent limitations in compactness and multi-degree-of-freedom cooperative control, which restrict their applicability in high-speed and high-precision laser cutting systems. To address these limitations, this paper presents a five-degree-of-freedom magnetic levitation actuator for laser cutting lens control and proposes a multi-degree-of-freedom cooperative control strategy based on backstepping control (BC) to cope with the system’s strong coupling, nonlinearity, and model uncertainty. First, a dynamic model of the actuator system is established, and a corresponding BC is designed. Subsequently, a centralized control framework is developed, and comparative simulations and experiments are carried out between the proposed BC and a conventional PID controller. The experimental results demonstrate that the proposed BC method outperforms the PID controller in terms of multi-degree-of-freedom cooperative control capability and dynamic response, thereby significantly enhancing the overall control performance of the system. Full article

Asymptotic synchronization is one of the essential differences between artificial neural networks and biologically inspired neural networks due to mismatches from the dynamical update of weight parameters and heterogeneous activations. In this paper, a new concept of approximate synchronization is proposed and investigated for Hopfield neural networks coupled with nonlinear memristors. It is proved that global solution dynamics are robustly dissipative and a sharp ultimate bound is acquired. Through a priori uniform estimates on the interneuron differencing equations, it is rigorously and analytically shown that approximate synchronization to any prescribed small gap at an exponential convergence rate of the memristive Hopfield neural networks occurs if an explicitly computable threshold condition is satisfied by the interneuron coupling strength parameter. The main result is also extended to Hopfield neural networks with Hebbian learning rules for a broad range of applications in unsupervised learning. The contribution of this approximate synchronization framework and the analytic methodology in this work advance the exploration of asymptotic dynamics for more AI mathematical models. Full article