Search Results (2,931)

An active damping control technique based on improved linear active disturbance rejection control (LADRC) is suggested to address the inadequate damping of doubly fed induction generator (DFIG) systems coupled to the grid using series compensation capacitors. Conventional LADRC still has certain limitations under complicated operating conditions, primarily because of its inadequate periodic disturbance estimate capabilities, which limit the system’s dynamic performance and disturbance-rejection capability. An enhanced LADRC scheme is created for the inner current loop of the rotor-side converter (RSC) in the DFIG system in order to lessen these restrictions. To enable a real-time estimate and adjustment of sub-synchronous disturbances, a decoupled linear extended state observer (LESO) is first proposed. In order to effectively attenuate both sub-synchronous oscillation and periodic disturbances, a composite control structure with enhanced suppression capability is constructed by incorporating an improved repetitive control scheme into the linear state error feedback law. The results show that the improved LADRC significantly enhances damping performance and disturbance rejection capability in the subsynchronous frequency range, suppressing active power oscillations within approximately 0.3 s based on a ±10% settling band. Compared with the conventional LADRC, the average THD of the grid current is reduced from 3.43% to 0.56%. Full article

In permanent magnet synchronous generator-based wind energy conversion systems, generator-side measurements may become unreliable due to sensor faults, which can degrade system reliability. To address this issue, a redundant control framework based on load-side encoder feedback is proposed, where the load-side encoder serves as an alternative measurement source under sensor degradation. Compared with conventional generator-side sensing strategies, the proposed approach enhances fault tolerance without requiring additional hardware redundancy. An extended state observer is employed to estimate system states and lumped disturbances, enabling improved robustness. Simulation results show that the proposed method significantly improves speed tracking performance, reducing the root mean square error by approximately 45% compared with conventional PI control, while maintaining stable operation under sensor degradation conditions. The results demonstrate that the proposed strategy enhances system reliability and robustness in fault scenarios. Full article

Decidualization in the human endometrium is not merely a hormone-dependent differentiation process; rather, it represents a multilayered adaptive program characterized by the tight integration of immune regulation, metabolic reprogramming, and cellular stress responses. In this review, endoplasmic reticulum (ER) stress and the associated unfolded protein response (UPR) are proposed as central regulatory mechanisms governing this process. Triggered by increased protein synthesis and secretory demand, UPR activation under physiological conditions preserves proteostasis and supports the secretory capacity of stromal cells. In contrast, chronic or dysregulated activation leads to a maladaptive response characterized by apoptosis, inflammation, and metabolic dysfunction. UPR signaling pathways shape immune tolerance through their effects on macrophage polarization, uterine natural killer (uNK) cell function, and T cell balance. At the metabolic level, adenosine monophosphate-activated protein kinase (AMPK) regulates cellular adaptation through bidirectional interactions with mitochondrial function and redox homeostasis. Within this framework, the ER stress–immune–metabolic axis operates not as a linear pathway but as a dynamic network incorporating multiple feedback loops, thereby constituting a critical threshold mechanism that determines the success of decidualization. Disruption of this axis provides a shared mechanistic basis for pathologies such as recurrent implantation failure, pregnancy loss, and preeclampsia. From a therapeutic perspective, agents including chemical chaperones, UPR modulators, AMPK activators, and anti-inflammatory compounds hold translational potential by targeting these pathological feedback circuits. However, key knowledge gaps remain, particularly regarding the cell type-specific and temporal regulation of ER stress, the molecular boundaries defining the transition from adaptive to pathological states, and interspecies differences. Future studies employing single-cell omics approaches and functional in vivo models will be essential to elucidate the dynamic organization of this axis and to enable the development of targeted and personalized therapeutic strategies. Full article

In next-generation wireless communication systems, spectrum efficiency can be realized through the integration of hybrid beamforming (HBF) and non-orthogonal multiple access (NOMA). To maximize the synergy between these two technologies, it is essential to accurately cluster users within beams. Most existing studies on clustering overlook practical constraints and assume perfect channel state information (CSI). However, obtaining full CSI is impractical in realistic environments due to high feedback overhead and potential CSI errors. To address these challenges, this paper adopts an opportunistic beamforming (OBF) framework based on a partial CSI environment. The OBF facilitates channel estimation and HBF precoder design using only signal-to-interference-plus-noise ratio (SINR) feedback. Subsequently, clustering and power allocation (PA) are performed utilizing the feedback SINR from OBF without requiring additional feedback information. While conventional NOMA focuses on maximizing either throughput or fairness, this paper proposes a scheme that selects users with high SINR to maximize system throughput while minimizing the throughput disparity among users to enhance fairness. Furthermore, a power allocation method that satisfies the minimum successive interference cancellation (SIC) power requirement is employed to ensure stable decoding. Simulation results demonstrate that the proposed clustering scheme enhances the sum-rate compared to conventional SINR-based clustering methods while maintaining fairness. Consequently, this study suggests a promising approach to improving NOMA performance in practical partial CSI environments. Full article

The thyristor level is the basic unit of ultra-high-voltage and extra-high-voltage direct current (DC) converter valves, and its main-circuit parameters are important indicators for characterizing the health status of converter valves. To meet the demand for efficient detection of converter valve thyristor levels, this paper proposes a parameter inversion method for converter valve thyristor levels by combining the time-frequency-domain features of valve voltage and current, temporal characteristics of feedback signals from the thyristor-level monitoring unit, and a Grey Wolf Optimizer–Backpropagation Neural Network (GWO-BPNN). First, a six-pulse converter valve circuit simulation model is established. Based on this model, the original dataset is generated using the Latin hypercube sampling (LHS) method. Wavelet packet decomposition is then used to extract time-frequency-domain features, and dimensionality reduction is carried out by comparing the coefficient of variation and explained variance ratio so as to obtain input data suitable for neural network training. A BP neural network is then trained, and the network parameters are optimized using the Grey Wolf Optimizer to improve the accuracy and convergence speed of parameter inversion. Simulation comparison results show that the GWO-BP method is more efficient than the state equation method and is suitable for efficient inversion of damping parameters in multi-level thyristor systems. After GWO optimization, the maximum inversion errors of both parameters are reduced to below 5%. Compared with BP, GA-BP, and PSO-BP, the proposed GWO-BP model provides the best overall balance between resistance-inversion accuracy and training efficiency. By further incorporating feedback feature signals, the inversion error can be reduced to 1%. The proposed method provides a new technical route for efficient detection of thyristor converter valves and has broad application prospects. Full article

A real-time optimized model predictive control–equivalent consumption minimization strategy (MPC-ECMS) is proposed for the energy management of shipboard gas turbine–photovoltaic hybrid energy storage (GT-PV-HESS) power systems. Different from conventional MPC-ECMS methods that only adopt single-level SOC-based feedback regulation, the strategy aims to overcome the limitations of conventional methods, including the poor adaptability of rule-based strategies and the lack of foresight in traditional ECMS, which cannot achieve simultaneous improvements in fuel economy, generation efficiency, and battery lifespan while maintaining system stability under dynamic operating conditions. The proposed strategy integrates the forward-looking optimization ability of MPC and the real-time decision-making advantage of ECMS. MPC is used to predict short-term load and photovoltaic power and identify operating modes, and a two-level equivalent factor adjustment mechanism is designed based on predicted conditions and battery state of charge (SOC). The optimized factor is applied in ECMS to achieve optimal power allocation between the gas turbine and battery under system constraints, while the supercapacitor implements power secondary correction to suppress bus voltage fluctuations caused by gas turbine operation. The architectural novelty lies in the two-level coordination mechanism and the marine-oriented hybrid energy storage cooperation. Simulation studies are conducted on the MATLAB/Simulink R2021b platform, and the results validate that it yields superior performance to the rule-based control and traditional ECMS under typical ship operating conditions. It increases gas turbine efficiency to 15.62% (0.47% and 6.24% higher than the two conventional methods). Over the 120 s simulation period, the proposed strategy reduces total fuel consumption to 1.049 kg, which is lower than 1.054 kg for the rule-based strategy and 1.192 kg for conventional ECMS. The battery SOC fluctuation is restricted to only 3.89%. The maximum DC bus voltage fluctuation rate is controlled within 3.28%, which meets the stability requirements of shipboard DC microgrids. The proposed strategy achieves a comprehensive and superior balance among fuel economy, power generation efficiency, and battery life while ensuring stable system operation under all working conditions. This two-level MPC-ECMS framework provides a high-performance and practically feasible energy management solution for shipboard hybrid power systems. Full article

Wheeled inverted-pendulum robots with movable upper structures and variable payloads exhibit configuration-dependent equilibrium drift and payload-dependent dynamic variation, which complicate balancing control. This paper proposes a gain-scheduled controller–observer framework for payload-adaptive balancing of such a robot. First, the multi-body system is reduced to a control-oriented equivalent inverted-pendulum model through center-of-mass lumping, from which a parameter-varying linearized model is established. On this basis, an H∞ state-feedback controller with input constraints is synthesized in a linear matrix inequality (LMI) framework, and an augmented-state observer is designed to estimate the residual equilibrium offset induced by payload variation. To improve robustness over the operating range, the frozen-point design is extended to a sampled-model multi-model synthesis framework, and gain scheduling is implemented with respect to the measurable arm angle. Nonlinear Simscape simulations show that the proposed method can recover balance at representative fixed operating points, compensate effectively for load-induced equilibrium drifts, and preserve stable balancing performance under slow arm-angle variation. Quantitative comparisons with an LQR baseline further support the effectiveness of the proposed framework for payload-adaptive balancing control. Full article

The large-scale integration of variable renewable energy sources introduces critical challenges of intermittency and uncertainty, yet consumption forecasting, generation forecasting, and anomaly detection are typically addressed in isolation, neglecting the bidirectional feedback between consumption patterns, generation mix, and public decision-making. This PRISMA 2020-compliant systematic review compared statistical, machine learning, and deep learning models for energy forecasting and machine learning and deep learning models for anomaly detection. Searches in Google Scholar and Scopus used seven targeted strings, restricted to peer-reviewed empirical studies (2022–2026; 2023–2026 for anomaly detection), indexed in Q1–Q3 JCR journals, excluding theoretical and non-benchmarked works. A six-item risk of bias questionnaire—with a threshold of four points—guided inclusion, yielding 60 articles. Addressing the first research question (RQ1) on comparative model performance, hybrid deep learning architectures optimized with bio-inspired metaheuristics achieved the highest forecasting accuracy ($R^2$ up to 0.9984), with metaheuristic optimization acting as a cost-reducing factor; statistical models remained competitive for long-horizon forecasting, while large-language-model-based approaches addressed data scarcity through few-shot learning. Addressing the second research question (RQ2) on smart grid optimization, predictive techniques reduce forecasting errors enabling real-time load adjustment and Demand Response, though a systematic asymmetry constrains their potential: consumption studies integrate socio-economic variables, whereas generation studies rely on meteorological inputs. Addressing the third research question (RQ3) on infrastructure security, supervised and unsupervised approaches detect anomalous operational states and support fault diagnosis, yet remain constrained by scarce labeled fault data and limited cross-regional validation; generative models such as GANs and diffusion models partially address this limitation by enabling Sim2Real strategies and realistic digital twin construction. Evidence is strongest for hybrid forecasting; certainty is lower for anomaly detection given reliance on experimental surrogates. No single paradigm achieves universal superiority. The primary finding is the consistent absence of integrated frameworks jointly modeling consumption, generation, anomaly detection, and public decision-making across the reviewed literature. This result reflects a structural limitation of the current state of the art, rather than a forward-looking research agenda. This study was funded by the ENIA International Chair on Trustworthy Artificial Intelligence European Recovery Plan; the protocol was not pre-registered. Full article

In this paper, an adaptive output feedback control scheme is proposed for a class of parametric strict feedback systems with asymmetric full-state constraints and unknown output gain. Firstly, an adaptive state observer is constructed to estimate the unmeasured system states. To compensate for the effect of the unknown output gain on the tracking performance, a new error signal incorporating an adaptive compensation coefficient is introduced into the backstepping design. Then, by combining the universal transformed function with a coordinate transformation, all system states are kept within time-varying asymmetric bounds, and the feasibility issues of conventional constrained control methods are avoided. Based on Lyapunov stability analysis, all signals in the closed-loop system are proven to be globally uniformly ultimately bounded. Finally, simulation results based on motor models demonstrate the effectiveness of the proposed scheme. Full article

Heat transport properties of serpentine minerals are important to the thermal state of subduction zones, but available data contain systematic errors from contact losses, radiative gains, deformation with pressure (P), and/or modelling short-comings. Here, laser flash analysis (LFA) provides thermal diffusivity (D) within ±3% as a function of temperature (T) of perpendicularly oriented, nearly pure Mg₃Si₂O₅(OH)₄ polymorphs, Al-rich lizardite with minor brucite, three serpentinites, plus chrysotile and lizardite near Ni₃Si₂O₅(OH)₄. Visible spectra show that Fe is mostly ferric and Cr³⁺ occasionally occupies tetrahedral sites. The proposed coupled substitution of Al³⁺ + OH⁻ replacing Si⁴⁺ + O²⁻ accounts for extra OH⁻ peaks in infrared spectra. Rietveld refinements and infrared spectra reveal that serpentine dehydration in LFA runs begins near 800 K. Thermal conductivity (K) vs. T is calculated within ~±5% from D, available heat capacity data, and ambient density. For antigorite, D and K are strongly anisotropic whereas chrysotile has extreme differences, but lizardite is nearly isotropic. A thermodynamic identity provides ∂(lnK)/∂P = 11 ± 1% Gpa⁻¹ for soft serpentine, double that of hard olivine. Lizardite becomes more thermally conductive than olivine near the 1 bar decomposition temperature, which increases with P. Through feedback, and because released H₂O vapor carries heat upwards, P,T conditions in serpentinized slabs follow the decomposition phase boundary during subduction. Full article

Neuromorphic technologies are attracting increasing interest in neuroengineering, as they provide an event-driven, spike-based computational framework that is well suited to temporally structured, sparse, and resource-constrained biological systems. Compared with conventional computing pipelines, neuromorphic approaches enable tighter integration of sensing, encoding, inference, feedback, and actuation under low-power and low-latency conditions. These features make them particularly relevant for wearable, implantable, and other edge-native neuroengineering applications. This review examines neuromorphic neuroengineering from four closely related perspectives: neuromorphic neurostimulation and adaptive actuation; tactile and sensory biointerfaces; spiking neural network (SNN)-based biosignal processing and state decoding; and wearable or implantable neuromorphic platforms. Across these domains, we highlight how neuromorphic systems may facilitate edge-native, closed-loop architectures that operate closer to the body and respond selectively to meaningful state changes. Neurorehabilitation is further discussed as an important translational context, as it involves long-term use, multimodal sensing, adaptive intervention, and substantial real-world deployment constraints. At present, however, the evidence base remains fragmented and is still largely dominated by device demonstrations and proof-of-concept studies rather than robust translational validation. Overall, neuromorphic approaches offer a promising systems-level pathway toward neuroengineering platforms that are not only computationally efficient but also adaptive, deployable, and responsive in real-world settings. Full article

We study a perturbed coupled system of generalized hybrid pantograph equations involving the deformable derivative of Zulfeqarr–Ujlayan–Ahuja. A central point of the revision is made explicit: for classically differentiable functions this derivative is local and satisfies $D^{\tau }u=(1-\tau )u+\tau u^{\prime }$. Therefore, in the present differentiable setting the memory or aftereffect is produced by the proportional pantograph delays, while the deformable order $\tau$ supplies an order-dependent local relaxation/drift term. After rewriting the system as an equivalent integral equation on $\mathcal{X}=C(I,{\mathbb{R}}^2)$, we establish invariant-ball conditions, existence and uniqueness within invariant balls, generalized Ulam–Hyers stability, and Lipschitz continuous dependence on the perturbation amplitude $\epsilon$. The assumptions and constants are stated so that the restrictive roles of the Lipschitz bounds, the interval length, and $\left|\epsilon \right|$ are transparent. We then provide numerical parameter sensitivity diagrams for illustrative pantograph systems and include step-size refinement checks and performance indices. The numerical and plasma-inspired sections are deliberately framed as exploratory delay-feedback examples rather than as first-principles plasma models or rigorous bifurcation theory. Full article

Under complex disturbances and backwater time-delay conditions, traditional open-channel gate water level control suffers from insufficient accuracy and slow response, readily causing water level overruns, control instability, and engineering safety risks. To overcome the limitations of conventional controllers in responding to rainfall disturbances, this study proposes a Model Predictive Control (MPC) algorithm based on the Integrator Delay (ID) model. The approach first integrates an LSTM-KAN (Kolmogorov–Arnold Network) model for accurate rainfall prediction, providing reliable inputs for disturbance feedforward. Subsequently, leveraging the SWMM simulation model and the PySWMM library, ID model parameters (backwater area and lag time) are identified in real time through impulse response testing. A state-space representation is then formulated and incorporated into the MPC rolling optimization framework, enabling precise water level forecasting over the prediction horizon. Simulation results demonstrate that the average computation time for 24-hour tests is only 240 seconds, with markedly reduced water level deviations. Experimental validation confirms superior performance under steady flow conditions (flow fluctuations < 0.007 m³/s; settling time ≈ 210 seconds) and constant water level control, achieving water level deviations < 0.05 m in known disturbance scenarios. Compared with the conventional Linear Quadratic Regulator (LQR), the proposed MPC algorithm reduces gate response time by 6.38–19.80% under the tested rainfall conditions. The proposed method establishes a complete closed-loop framework integrating rainfall prediction, water level forecasting, and combined feedforward-feedback control, offering an efficient and practical solution for open-channel gate water level management in smart water conservancy systems. It holds considerable theoretical significance and application value. Full article

In recent years, microreactors have attracted increasing attention as next-generation chemical reactors, enabling rapid and highly efficient reactions, while requiring precise control against temperature variations. In this paper, a research platform for a microreactor process close to practical implementation is constructed using a distributed control system (DCS) and wireless communication. By establishing such a research platform, not only the effectiveness of control methods but also discussions on system configuration, including operation and maintenance, can be verified and optimized at an early stage. Moreover, operator-based multi-dimensional nonlinear control strategies have been applied in existing studies of channel temperature control. In contrast, this paper extends such strategies by integrating an operator-based feedback scheme with state estimation via particle filters, which simultaneously accounts for unknown communication delay compensation and the nonlinear characteristics of microreactors. Finally, the feasibility and effectiveness of the proposed research platform are verified through real-world experiments. Full article

This study examines the role of investor sentiment in asset pricing within a frontier market, focusing on Vietnam. Using a comprehensive dataset covering the period 2015–2025 with 4018 observations, sentiment indices are constructed from both market-based and survey-based indicators. The study employs a quantile causality approach and a Quantile Vector Autoregression (QVAR) model to capture nonlinear, asymmetric, and state-dependent relationships among investor sentiment, stock returns, and market volatility. The empirical results provide several important findings. First, investor sentiment significantly influences stock returns, with stronger effects observed at extreme quantiles corresponding to bearish and bullish market conditions. Second, the impact is heterogeneous across firm sizes, with small-cap stocks exhibiting greater sensitivity to sentiment fluctuations. Third, the impact of investor sentiment on volatility is proxy-dependent and state-dependent. The market-based sentiment measure is generally associated with lower volatility at middle and upper quantiles, whereas the survey-based sentiment proxy shows stronger effects at lower quantiles, particularly during distress periods. Finally, robust bidirectional causality is identified between sentiment and market variables, suggesting the presence of feedback mechanisms between investor behavior and market performance. These findings highlight the importance of behavioral factors in shaping market dynamics in frontier markets characterized by high retail participation and limits to arbitrage. The study contributes to the literature by providing new quantile-based evidence on the nonlinear and asymmetric effects of investor sentiment in Vietnam. Full article