Search Results (133)

Employing ab initio electronic structure methods, in this study, I examine the effect of order on the spin gapless semiconducting behavior of the Mn₂CoAl Heusler compound. The occurrence of atomic disorder in general destroys the spin gapless semiconductivity observed in the inverse XA lattice structure; however, in some cases, novel magnetic configurations emerge. In the case of structures derived from the XA structure, where only Mn-Co or Mn-Al atoms are mixed, Mn₂CoAl alloy presents a half-metallic magnetic character. In the case of full disorder (A2 lattice structure), where atoms occupy all sites with the same probability, the ground state is an antiferromagnetic metallic one. The L2₁ and B2 lattice structures, where Mn atoms occupy both sites of a similar local environment, correspond to a ferromagnetic state of very high spin magnetic moment per formula unit. The present study encompasses a much larger variety of disordered structures in comparison with other studies in the literature. It concludes that the control and minimization of the concentration of impurities at anti-sites is imperative to achieving optimal performance in spintronic devices based on spin gapless semiconducting Mn₂CoAl. Full article

Maximum Power Point Tracking (MPPT) in permanent-magnet synchronous generator (PMSG)-based wind energy conversion systems (WECS) remains challenging owing to strong nonlinearities, parametric uncertainties, and external disturbances. Conventional sliding mode control (SMC) strategies, while robust, suffer from chattering, dependence on full-state measurements, and degraded performance under model mismatch, limiting their practical deployment. To address these issues, this study proposes a neuroadaptive fuzzy fractional-order super-twisting sliding mode control (Fuzzy-FOSTSMC) integrated with a high-gain observer (HGO) and a radial basis function neural network (RBFNN). The HGO estimates unmeasurable higher-order states (e.g., angular acceleration), enabling output-feedback implementation. In contrast, the RBFNN online approximates unknown nonlinear system dynamics $L_f^{2}h\left(x\right)$ and $L_g{L}_{f}h\left(x\right)$, rendering the controller model-free. Chattering is eliminated by replacing the discontinuous signum function with an adaptive fuzzy boundary layer that dynamically modulates the slope near the sliding surface. Stability is theoretically confirmed by Lyapunov analysis. Extensive MATLAB/Simulink simulations verify that the proposed approach yields a tracking precision of 99.96%, a steady-state speed error of 0.018 rad/s, and a 58.2% reduction in the integral absolute error (IAE) compared to the traditional FOSTSMC. It achieves the optimal power coefficient ($C_p=0.4762$) via TSR control at $7.000\pm 0.002$, under ±30% parametric uncertainties, demonstrating excellent robustness and MPPT effectiveness. Full article

Due to the increasing demand for greater safety and ease of scale bioprocessing, fault detection and diagnosis (FDD) is becoming an effective method to avoid breakdowns and disasters. Therefore, this work focuses on developing a dedicated observer-based fault diagnosis for nonlinear systems. To solve this, the FDD scheme is needed to make it perform satisfactorily even in a faulty situation. A case study on bioethanol production is proposed to illustrate and demonstrate the proposed techniques in real time. Single faults and different sensor faults are considered. The effectiveness of the proposed model is proved by comparing its performance obtained by simulation with the experimental data. In order to supervise the change of the possible faulty parameter, robust adaptive full-order observers that focus not only on the state estimation but also on the parameter change are applied to the considered bioreactor. In order to achieve the desired outcome of sensor fault detection, we propose a residual evaluation function, given by the root-mean-square (RMS) value of the residual and a practical threshold for the bioreactor. Experimental results show that sensor faults can be well diagnosed by the proposed observer-based FDD method. The precision, recall rate, and overall accuracy of three diagnostic metrics for abrupt failures were compared. The diagnostic approach was successful, achieving an overall accuracy rate of over $90\%$ for each of the three abrupt failure scenarios in every sensor. Finally, even if the biomass or $C{O}_2$ sensors fail, the FDD system can reconstruct the substrate and ethanol dynamics that are typically quantified offline in bioprocesses in real time. Full article

This paper proposes a cooperative localization method based on time-division processing of interferometric measurements, in which the receiver updates the signals from multiple UAVs in separate time slots, thereby reducing spectrum usage and baseband hardware overhead. A Kalman-enhanced tracking loop is designed to achieve high-precision carrier-phase and Doppler estimation under low-SNR conditions. For angle estimation, a time-division update strategy is employed such that the receiver performs full carrier tracking for only one UAV in each time slot, while the carrier phases of the remaining UAVs are extrapolated from the Doppler states estimated in the previous epoch. This avoids the hardware complexity associated with maintaining multiple parallel tracking loops. By fusing the estimated azimuth, elevation, and pseudorange measurements with the master UAV’s high-precision GNSS observations, a factor-graph-based sliding-window cooperative localization algorithm is constructed. Simulation results show that the proposed method improves the RMSE of carrier-phase and Doppler estimation by nearly an order of magnitude compared with the traditional FLL-assisted PLL. The system maintains angle estimation accuracy better than 0.01° within a four-node configuration and achieves centimeter-level ranging accuracy when SNR ≥ 0 dB. In a cooperative flight scenario with one master and three follower UAVs, the method consistently delivers sub-decimeter 3D localization accuracy. Full article

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband–hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within ±0.01°, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance. Full article

As conventional computing architectures face fundamental physical limitations and the von Neumann bottleneck constrains computational efficiency, neuromorphic systems have emerged as a promising paradigm for next-generation information processing. Memristive neurons, particularly third-order circuits operating near the edge of chaos, exhibit rich neuromorphic dynamics that closely mimic biological neural activities but present significant prediction challenges due to their complex nonlinear behavior. Current approaches typically require complete system state measurements, which is often impractical in real-world neuromorphic hardware implementations where only partial state information is accessible. This paper addresses this critical limitation by proposing an innovative hybrid machine learning framework that integrates a Modified Next-Generation Reservoir Computing (MNGRC) with XGBoost regression. The core novelty lies in its dual-path prediction architecture designed specifically for partial state observability scenarios. The primary path employs NGRC to capture and forecast the system’s temporal dynamics using available state variables and input stimuli, while the secondary path leverages XGBoost as an efficient state estimator to infer unobserved state variables from minimal measurements. This strategic combination enables accurate prediction of diverse neuromorphic patterns with significantly reduced sensor requirements. Experimentally, the framework demonstrates its capability to identify and predict the complex spectrum of neuromorphic behaviors exhibited by the third-order memristive neuron. This includes accurately capturing all 18 distinct neuronal patterns, which are theoretically grounded in Hopf bifurcation analysis near the edge of chaos. Additionally, the framework successfully addresses the inverse problem of input stimulus reconstruction. By achieving accurate prediction of complex dynamics from limited states, our approach represents a key breakthrough, where full state access is often impossible, thereby addressing a critical challenge in edge AI and brain-inspired computing. Full article

To address the need for wide-area electromagnetic spectrum sensing of space targets from sparse Low-Earth Orbit constellation observations, this paper proposes SRFlow, a flow-matching generative model. We first construct a high-fidelity dataset covering diverse scenarios via STK-MATLAB co-simulation. By integrating multi-source priors and an iterative measurement injection strategy, SRFlow achieves high-quality reconstruction of full spectrum maps from sparse measurements. Experiments demonstrate that SRFlow significantly outperforms state-of-the-art baselines, including the physics-informed diffusion model RMDM, in both reconstruction accuracy (NMSE/SSIM) and computational efficiency (parameters/inference time), under both known and unknown target-position conditions. Moreover, it trains nearly an order of magnitude faster than diffusion models. This work contributes the first dedicated dataset for space-based spectrum sensing, introduces the accurate and efficient SRFlow model, and establishes a rigorous benchmark for future research. Full article

Accurate prediction of significant wave height (SWH) is central to coastal ocean dynamics, wave–climate assessment, and operational marine forecasting, yet many high-performing machine-learning (ML) models remain opaque and weakly connected to underlying wave physics. We propose an explainable, feature engineering-guided ML framework for coastal SWH prediction that combines extremal wave statistics, temporal descriptors, and SHAP-based interpretation. Using 30 min buoy observations from a high-energy, wave-dominated coastal site off Australia’s Gold Coast, we benchmarked seven regression models (Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression, K-Nearest Neighbors, and Neural Networks) across four feature sets: (i) Base (Hmax, Tz, Tp, SST, peak direction), (ii) Base + Temporal (lags, rolling statistics, cyclical hour/month encodings), (iii) Base + a physics-informed Wave Height Ratio, WHR = Hmax/Hs, and (iv) Full (Base + Temporal + WHR). Model skill is evaluated for full-year, 1-month, and 10-day prediction windows. Performance was assessed using R², RMSE, MAE, and bias metrics, with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) employed for multi-criteria ranking. Inclusion of WHR systematically improves performance, raising test R² from a baseline range of ~0.85–0.95 to values exceeding 0.97 and reducing RMSE by up to 86%, with a Random Forest|Base + WHR configuration achieving the top TOPSIS score (1.000). SHAP analysis identifies WHR and lagged SWH as dominant predictors, linking model behavior to extremal sea states and short-term memory in the wave field. The proposed framework demonstrates how embedding simple, physically motivated features and explainable AI tools can transform black-box coastal wave predictors into transparent models suitable for geophysical fluid dynamics, coastal hazard assessment, and wave-energy applications. Full article

This paper presents an optimal-design methodology for small-scale Integrated Energy Systems (IESs) that couple electricity and heat in distributed networks. A hybrid reliability assessment integrates probabilistic state enumeration with scenario-based simulation. Mathematically, the design is cast as a stochastic, reliability-driven ranking: time-sequential Monte Carlo (MC) produces estimators of Loss of Load Probability (LOLP), Expected Energy Not Supplied (EENS), and Self-Sufficiency Rate (SSR), which are normalized and combined into a Composite Reliability Index (CRI) that orders candidate siting/sizing options. The case study is the D-campus microgrid with Photovoltaic (PV), Combined Heat and Power (CHP), Fuel Cell (FC), Battery Energy Storage Systems (BESSs), and Heat Energy Storage Systems (HESSs; also termed TESs), across multiple siting and sizing scenarios. Results show consistent reductions in LOLP and EENS and increases in SSR as distributed energy resource capacity increases and resources are placed near critical nodes, with the strongest gains observed in the best-performing configurations. The CRI also reveals trade-offs across intermediate scenarios. The operational concept of the campus Energy Management System (EMS), including full operating modes and scheduling logic, is developed to maintain a design focus on reliability-driven decision making. Probability-based formulations, reliability metrics, and the sequential MC setup underpin the proposed ranking framework. The proposed method supports Distributed Energy Resource (DER) sizing and siting decisions for reliable, autonomy-oriented IESs. Full article

High-performance tracking control for the hydraulic manipulator should address the challenges of the uncertainties and unknowns associated with the electro-hydraulic servo system (EHSS). This paper presents an extended state observer-based chattering-free terminal sliding-mode (ESO-CFTSM) control scheme for hydraulic manipulators. A third-order integral chain model is developed to characterize the system dynamics, where uncertainties and unknowns are considered as disturbances and estimated by the ESO. Meanwhile, a full-order TSM manifold is designed to stabilize the closed-loop system in finite-time. For this proposed scheme, the feedforward compensation of disturbances is introduced in the equivalent control law. Furthermore, the composite reaching law and a low-pass filter are used to realize the chattering-free control. The singularity is avoided because there are no derivatives of terms with fractional powers in the control law. The stability of the overall system is proved by Lyapunov technique. The simulations using the physical model of a hydraulic manipulator with coupled dynamics show the effectiveness of the proposed scheme for trajectory tracking problems. Simulation results indicate that the proposed ESO-CFTSM can achieve superior performance without being affected by lumped disturbances. Full article

The increased penetration of power electronics interfaced resources in modern power systems is unlocking new opportunities and challenges. New concepts like multiport converters can further enhance the efficiency and power density of power electronics-based solutions. The triple active bridge is an isolated multiport converter with soft switching and high voltage gain that can integrate different sources, storage, and loads, or act as a building block for modular systems. However, the triple active bridge suffers from power flow cross-coupling, which affects its dynamic performance if it is not removed or mitigated. Unlike the extensive literature on two-port power converters, studies on modeling and control comparison for multiport converters are still lacking. Therefore, this paper presents and compares different modeling and decoupling control approaches applied to the triple active bridge converter, highlighting their benefits and limitations. The converter operation and modulation are introduced, and modeling and control strategies based on the single phase shift power flow control are detailed. The switching model, generalized full-order average model, and the reduced-order model derivations are presented thoroughly, and a comparison reveals that first harmonic approximations can be detrimental when modeling the triple active bridge. Furthermore, the model accuracy is highly sensitive to the operating point, showing that the generalized average model better represents some dynamics than the lossless reduced-order model. Furthermore, three decoupling control strategies are derived aiming to mitigate cross-coupling effects to ensure decoupled power flow and improve system stability. To assess their performance, the TAB converter is subjected to power and voltage disturbances and parameter uncertainty. A comprehensive comparison reveals that linear PI controllers with an inverse decoupling matrix can effectively control the TAB but exhibit large settling time and voltage deviations due to persistent cross-coupling. Furthermore, the decoupling matrix is highly sensitive to inaccuracies in the converter’s model parameters. In contrast, linear active disturbance rejection control and sliding mode control based on a linear extended state observer achieve rapid stabilization, demonstrating strong decoupling capability under disturbances. Furthermore, both control strategies demonstrate robust performance under parameter uncertainty. Full article

To eliminate the dependence of the induction motor (IM) flux-oriented control system on position sensors, IM sensorless control based on a full-order state observer is studied in this paper. First, according to the IM rotor flux linkage models of current and voltage, the speed of the full-order state observer for IM and the solution for the feedback matrix are designed. Then, to simplify the expression of the feedback matrix and improve the stability of the observer for high-speed operation, a novel solving method of the feedback matrix by left-shifting the poles of the observer is proposed, and the terms containing rotor speed are simplified. On this basis, a speed estimation method based on the current error and Lyapunov theory is proposed. For low-speed operation, the feedback matrix parameter design method is proposed based on the stability conditions of d–q axis current error model. Finally, the feasibility and effectiveness of the proposed full-order state observer are verified by simulation and experiment. Since the improved full-order state observer can provide accurate speed feedback and rotor flux position for flux-oriented vector control systems, the IM drive system exhibits good steady-state and dynamic performance. Full article

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

In this work, we provide a promising way to alleviate the Hubble tension within the framework of Myrzakulov $f(R,T)$ gravity. The latter incorporates both curvature and torsion under a non-special connection. We consider the $f(R,T)=R+\alpha R^2$ class, which leads to modified Friedmann equations and an effective dark energy sector. Within this class, we make specific connection choices in order to obtain a Hubble function that coincides with that of $\Lambda$CDM at early times while yielding higher $H_0$ values at late times. The reason behind this behavior is that the dark energy equation of state exhibits phantom behavior, which is known to be a sufficient mechanism for alleviating the $H_0$ tension. A full observational comparison with various datasets, including the Cosmic Microwave Background (CMB), is required to test the viability of this scenario. Strictly speaking, the present work does not provide a complete solution to the Hubble tension but rather proposes a promising way to alleviate it. Full article

Digital control in UPS systems is currently the only reasonable way of controlling a voltage source inverter (VSI). The control frequency range is restricted to up to about 1 kHz owing to the output low-pass LC filter, which should also maintain the output voltage during one switching period for the step unload. The measurement channels in the low-pass frequency range can be modeled as delays equal to some switching periods. A reasonably high (about 50 kHz) switching frequency minimizes the delays of the measurement channels. Two control systems will be compared—the pure discrete control, in this case a one-sample-ahead preview deadbeat control (OSAP), and a discretized passivity-based control (PBC). The OSAP control is easy to realize, is very fast, and enables one to obtain a steady state in a restricted number of steps after disturbance. However, the single-input single-output deadbeat control version is useless because it depends very strongly on the parameters of the inverter. The multi-input single-output OSAP (MISO-OSAP) control is directly based on discrete state equations (we treat the output voltage, output current, and inductor current as the measured state variables) and works perfectly for the nonlinear rectifier RC load (PF = 0.7) in a system without delay. The version of this with a linear prediction of state variables by means of a full-order state Luenberger observer (MISO-OSAP-LO) will be used in systems with different delays and compared with the discretized MISO passivity-based control without prediction for relatively high switching frequency (about 50 kHz). The aim and the novelty of the paper are in enabling a choice between one of these control systems for high switching frequency VSI with delays in the measurement channels. Full article