Search Results (124)

Grid-forming converters are essential for low-inertia power systems. However, their semiconductor current and junction-temperature limits make them vulnerable to protection-triggered blocking and thermal overstress during severe voltage sags. Existing control methods still struggle to suppress fault-instant current surges and the associated transient thermal stress induced by rapid overcurrent, which restricts the continuous operation of grid-forming converters during fault ride-through. This paper proposes a two-stage coordinated electro-thermal control strategy centered on fractional-order reactive power regulation. First, a fractional-order operator is embedded into the reactive-power loop of the virtual synchronous generator to smooth the transient response of the internal electromotive force and directly suppress the amplitude of fault-induced inrush current. By introducing a continuously tunable order, the proposed controller enhances transient damping, mitigates the waterbed effect trade-off associated with integer-order control, and reduces the initial junction-temperature shock at the fault instant. A constraint-based feasible-region identification procedure is then developed, in which a full-range parameter sweep is performed to screen fractional orders under stability, current, and thermal constraints. Second, generalized discontinuous pulse-width modulation and switching-frequency reduction are jointly applied to mitigate post-transient thermal accumulation. Electro-thermal co-simulation results indicate that, under the specified voltage-sag condition, the proposed strategy reduces transient overcurrent and thermal stress and improves the simulated fault-ride-through performance of grid-forming converters without additional hardware thermal margins. Full article

In scrubbing tower (ST) systems, outlet vapor temperature regulation is subject to strict thermal constraints, requiring the bottom temperature to remain below a critical safety threshold. Transient overshoots can rapidly trigger pyrolytic coking, foul mass-transfer packed beds and spray nozzles, and ultimately force complete production shutdowns. Due to feedstock compositional drifts, high thermal inertia, and significant transport delays, high-fidelity predictive identification is essential for proactive early warning and for overcoming the limitations of reactive feedback control. To address these bottlenecks, this paper introduces an offset-free, hard-constrained, data-driven adaptive optimal control paradigm, designated as the improved GRU-coupled conjugate gradient linear quadratic regulator (IGRUCG-LQR). First, by constructing an augmented state space embedded with integral error, the proposed paradigm eliminates permanent tracking offsets induced by long-term nonstationary drifts. Second, automatic differentiation is used to extract the time-varying Jacobian matrix of a gated recurrent unit (GRU) online, thereby tracking the nonlinear evolution of the underlying thermodynamic baseline with high fidelity. To manage the critical trade-off between strict actuator saturation and short real-time sampling intervals, the conjugate gradient (CG) method is fused with a hard-boundary projection operator, enforcing physical constraints with high computational efficiency and without complex matrix inversions. Experimental validation on a real-world industrial dataset demonstrates that the proposed paradigm secures the safety baseline while achieving high-resolution transient tracking. Furthermore, it significantly suppresses high-frequency valve chattering to mitigate mechanical fatigue, establishing a solid theoretical and engineering foundation for the prolonged stable operation of safety-critical processes. The proposed framework achieves an Integral Absolute Error (IAE) of 86.66 and an Integral Time Absolute Error (ITAE) of 4064.49. Full article

Accurate identification of axial forces in beam structures with unknown boundary conditions is important for structural assessment and safety monitoring. Most existing methods are based on Euler–Bernoulli beam theory and neglect the effect of rotational inertia. This simplification may reduce the accuracy of axial force identification for short beam members. To address this limitation, this study develops an axial force identification method that accounts for rotational inertia effects. First, a free-vibration governing equation for axially loaded beam members is derived based on the Reissner energy approach. Compared with the Euler–Bernoulli beam, the derived equation further accounts for the effect of rotational inertia. Then, based on the proposed dynamic formulation, an axial force identification method applicable to beam members with unknown boundary conditions is established by utilizing measured natural frequencies and mode shapes. Finally, the effectiveness and accuracy of the proposed method are systematically validated through both numerical simulations and experimental investigations on beam members. Numerical results indicate that incorporating rotational inertia improves axial force identification accuracy compared with conventional approaches, particularly for short beam members and higher-order modes. Experimental results further confirm its effectiveness, with a maximum identification error reduction of 7.69%. Full article

Despite recent advances in metamaterials, experimental studies addressing the dynamic behavior of waveguide-type fractals manufactured by means of additive manufacturing remain scarce, limiting understanding of their performance in real-world vibration control. This study investigates the dynamic behavior of fractal waveguide beams based on Sierpinski geometry through combined experimental and analytical approaches. Beams with iterations i = 0–3 were fabricated via stereolithography and tested under a doubly clamped configuration subjected to harmonic excitation. The dynamic response was captured using an accelerometer and analyzed in both time and frequency domains using Fast Fourier Transform. A single-degree-of-freedom mass–spring model was employed to estimate dynamic stiffness and validate experimental results. The findings reveal that fractal geometry significantly influences vibrational behavior, producing a nonlinear and non-monotonic evolution of stiffness and energy dissipation. The highest-order fractal beam exhibited the greatest vibration attenuation and resonance frequency (27.2 Hz), despite having the lowest effective mass, demonstrating an optimized stiffness-to-mass ratio. Spectral area analyses confirmed that energy dissipation increases with fractal complexity, enabling identification of transitions between stiffness- and inertia-dominated regimes. By identifying these regimes, this work provides a framework for engineering lightweight, adaptive structures for advanced vibration attenuation and tunable mechanical vibration control applications. Full article

Gantry-type dual-drive feed systems are widely used in high-precision CNC machine tools, and their synchronization performance directly affects machining accuracy and operational stability. To reduce synchronization errors caused by load-position variation, nonuniform stiffness distribution, and inertia mismatch, this study proposes a structural parameter optimization method for a gantry-type dual-drive feed system. The novelty of this work lies in integrating position-dependent dynamic modeling, critical-position identification, sensitive structural-parameter selection, and response-surface-based optimization into a unified framework for synchronization-error reduction. First, a position-dependent dynamic model is established using modal reduction, spline interpolation, and substructure synthesis. The dynamic model is then coupled with a servo control model to construct an electromechanical coupling model, which is validated experimentally on a gantry-type dual-drive feed system. Next, the synchronization-error distribution over the entire workspace is evaluated, and the critical position with the poorest synchronization performance is identified. Based on sensitivity analysis, the key structural parameters affecting synchronization error are selected as design variables. A response surface surrogate model is then constructed, and particle swarm optimization is used to obtain the optimal structural-parameter combination. The results show that the synchronization error at the critical position is reduced by 20.5%, while the average synchronization error at the validation positions is reduced by 17.3%. These results demonstrate that the proposed method can effectively improve the synchronization accuracy of gantry-type dual-drive feed systems and provide practical guidance for the structural design of high-precision dual-drive machine tools. Full article

Accurate vehicle state estimation is a critical prerequisite for electric vehicle motion control, yet its performance is highly sensitive to deviations in inertial parameters. Variations in vehicle mass and moment of inertia caused by changing loads can lead to model mismatch, thereby degrading the accuracy and robustness of state estimation. To this end, this paper proposes a hierarchical collaborative estimation framework that integrates the Maximum Correntropy Adaptive Unscented Kalman Filter (MCAUKF) with a Physics-Informed Neural Network (PINN) for inertial parameter identification and key state estimation in electric vehicles. The upper layer employs MCAUKF for robust online identification of unknown inertial parameters, such as vehicle mass and moment of inertia. The lower layer develops a PINN-based state estimator that incorporates physical constraints by embedding the coupled dynamic residuals of longitudinal, lateral, and roll motions into the supervised learning process, thereby enabling high-precision real-time estimation of key dynamic states, including yaw angle, longitudinal velocity, and roll angle. Simulation results demonstrate that the proposed method can effectively achieve coordinated estimation of inertial parameters and key states under varying load conditions and complex maneuvering scenarios, significantly improving overall estimation accuracy and robustness. Full article

This paper addresses issues such as nonlinearity, model uncertainty, and multiple policy constraints within the dynamic evolution of new quality productive forces systems. It proposes a research framework integrating data-driven modelling with adaptive tracking control. By merging control theory with economic dynamics, a closed-loop analytical system of ‘theory-data-control’ is constructed, providing a methodologically rigorous yet operationally feasible pathway for the precise regulation of complex economic systems. First, utilising provincial panel data, a discrete-time system model integrating linear inertia, policy effects, and nonlinear compensation is established. System parameter identification is achieved through a dual machine learning approach employing partial linear regression. Subsequently, a tracking controller integrating data-driven initial identification with online parameter adaptation is designed, incorporating a projection mechanism to strictly ensure policy variables remain within feasible adjustment ranges. Based on Lyapunov stability theory, we demonstrate that the tracking error of the closed-loop system exhibits ultimate convergence with boundedness. Simulation experiments confirm that the proposed method significantly enhances the system’s tracking performance towards the target trajectory, reducing the mean absolute error by approximately 30.8% while producing smoother control signals. Comparative studies indicate that the parameter adaptation mechanism and nonlinear compensation module play crucial roles in improving control effectiveness. This research not only expands the theoretical toolkit for analysing the dynamics of new quality productive forces but also provides an interdisciplinary methodological reference for the closed-loop management of complex socioeconomic systems under data-driven conditions. Full article

This article presents the potential for maliciously influencing a control system by interfering with the program code of an industrial controller, using a temperature control system for a heating process based on resistive electric heating as an example. The presented attack scenarios are crucial for the energy efficiency of electric heating systems, which is related to the issue of cybersecurity in the area of energy security. The aim of this research was to demonstrate that a cyberattack involving the malicious manipulation of the setpoint can be carried out in a manner invisible to the heating process operator and be difficult to detect using classical time-domain control quality indicators (time-response specifications). The first involves incorporating proportional elements with mutually inverted gains into the input and output of a closed-loop system. The second method is based on adding an additional transfer function G_m(s) in parallel to the control system. The difference between the correct and manipulated setpoints is introduced into the input, and the output signal is added to the actual (hidden) value of the controlled variable. In the first method, at the moment of starting the control system, there is a difference between the apparent (falsified) value and the ambient temperature. In the second method, the inclusion of an additional G_m(s) ensures that the apparent (falsified) value of the controlled variable matches the temperature at the moment of starting the system. PID control enables achieving satisfactory control quality in heating processes, which are characterized by high inertia and time delays. Compared to classical PID regulation, advanced control methods can, under certain conditions, provide better performance in terms of quality indicators. However, due to their high computational complexity and sensitivity to model uncertainty—particularly in methods relying on accurate system identification—PID controllers continue to be widely used in industrial practice. For this reason, the present study focuses on a control system based on a PID controller as a practical solution. Based on the results, it was found that the most effective manipulation occurred within the range from 0.9 to 1.1 of the actual setpoint value for both the first and second method, using a model with $T_m$ between 5 s and 30 s. In these cases, the quality indicators referenced to the nominal values, determined for the falsified control system responses to a step change in the setpoint, were as follows: overshoot—0.97 and 1.30 (method 1), and 0.90 and 1.10 (method 2 for 5 s), 0.75 and 1.30 (method 2 for 30 s); settling time—1.06 (method 1), and 0.98 and 1.17 (method 2 for 5 s), 0.85 and 1.14 (method 2 for 30 s). The settling times determined for the system’s response to a disturbance were: 1.00 and 1.15 (method 1), and 1.13 and 1.16 (method 2 for 5 s), 1.12 and 1.02 (method 2 for 30 s). Based on the conducted analysis, it was demonstrated that the relatively simple setpoint manipulation methods presented can effectively mask the impact of malicious interference on the temperature value in the control system of a heating process. Full article

Accurate thermal conductivity measurement requires temperature control systems to establish stable operating conditions within a limited time. In practical thermal conductivity performance testing systems, large thermal inertia, complex heat transfer paths, and input time delays arising from thermal propagation and sensor placement often degrade dynamic response and control accuracy. To overcome these limitations, a composite PI–RLS–MPC control strategy is proposed for thermal systems with inertia and time delay. A proportional–integral (PI) controller serves as the baseline stabilizing controller, while model predictive control (MPC) is utilized to optimize the control input by explicitly considering system delay and input constraints. To enhance robustness against model uncertainty and parameter variations, recursive least squares (RLS) is adopted for online parameter identification and adaptive PI tuning, and a steady-state parameter freezing mechanism is introduced to suppress unnecessary parameter updates after convergence. Simulation studies are performed on an identified thermal process model with a 20 s input time delay. The results indicate that the proposed strategy reduces overshoot, shortens settling time, and improves disturbance rejection compared with conventional controllers. Overall, the proposed PI–RLS–MPC approach provides a practical solution for improving temperature control performance in thermal conductivity testing systems. Full article

This paper presents the design and industrial implementation of an adaptive discrete control system for a rotary dryer operating in potash fertilizer production. The drying process is characterized by high inertia, multivariable interactions, transport delay, and non-stationary behavior resulting from variations in raw material properties and external disturbances, which significantly reduce the effectiveness of conventional fixed-parameter controllers. A discrete-time mathematical model of the rotary drying process was developed using industrial experimental data collected from a full-scale production plant. The process was modeled as a coupled 2 × 2 multivariable system with pronounced time-delay effects in the main control channels. System identification was carried out using statistical and frequency-domain methods to capture the dominant dynamic characteristics required for controller synthesis. Based on the identified model, an adaptive discrete controller with online parameter adjustment was developed to regulate outlet moisture content and exhaust gas temperature. Simulation and industrial results confirmed stable operation under varying conditions, improved regulation accuracy, enhanced process stability, and an average production efficiency increase of approximately 1.8%, accompanied by reduced fuel consumption. Full article

Sustainability monitoring relies on environmental indicator systems that integrate heterogeneous multivariate measurements across space and time; however, collinearity, non-Gaussian variability, and influential observations frequently destabilize classical multiblock methods and may bias indicator-based assessment and decision support. This study proposes a robust extension of the STATICO (STATIS–CO-inertia) framework to model common structures among paired environmental indicator blocks under realistic data contamination. The approach preserves the original triadic algebraic formulation while incorporating robust covariance estimation and adaptive weighting to reduce the influence of outliers and structurally unstable blocks. Robustification is implemented at the interstructure stage through a reformulated Escoufier’s RV coefficient and in the construction of the compromise space via robust distances. The RV coefficient, a multivariate generalization of the squared Pearson correlation computed between cross-product matrices, is used to quantify structural similarity between paired data blocks and to evaluate the stability of the compromise structure. Performance is evaluated using simulated datasets calibrated to represent Ecuadorian coastal monitoring conditions. The results show that Robust STATICO increases compromise dominance and stability, redistributes inter-block similarities more coherently, and improves discriminative representation in the factorial space, yielding more interpretable and environmentally plausible structures. Overall, the proposed method provides a reliable analytical tool for sustainability-oriented environmental monitoring by supporting stable identification of persistent multivariate patterns and robust comparison of indicator structures in complex systems. Full article

Accurate identification of the rotational moment of inertia of a top drive system is essential for dynamic modeling, control design, and performance optimization in drilling operations. However, the strong coupling between the drive motor, transmission components, and drill string makes direct inertia measurement challenging under field conditions. To address this issue, this study proposes a moment of inertia identification method based on dynamic response analysis of the top drive system. A simplified torsional dynamic model is established by representing the top drive and drill string assembly as an equivalent lumped inertia system. By applying controlled torque excitation under no-load conditions, the system’s angular velocity response is measured and analyzed in both time and frequency domains. The relationship between applied torque and angular acceleration is utilized to identify the equivalent rotational inertia through parameter estimation. Experimental results indicate that low-frequency excitation provides more favorable conditions for reliable and accurate inertia identification, yielding improved stability and reduced estimation error compared with higher-frequency inputs. In addition, frequency response characteristics are investigated to validate the consistency and robustness of the identified inertia across different excitation frequencies. Experimental results obtained from a top drive test rig demonstrate that the proposed method can reliably estimate the equivalent moment of inertia with good repeatability under controlled experimental conditions. The identified inertia shows good agreement with theoretical calculations and exhibits stable behavior over a wide frequency range. The proposed approach avoids the need for additional sensors or structural modifications and is well suited for practical engineering applications. This study provides an effective and experimentally validated method for inertia identification of top drive systems, offering valuable support for dynamic modeling, control parameter tuning, and vibration analysis in drilling engineering. Full article

Background: Poor clinical control in Chronic Obstructive Pulmonary Disease (COPD) is prevalent, yet the interplay of disease severity, modifiable factors, and clinician perception remains poorly understood. This study aimed to determine the frequency of poor control, identify its independent determinants, and characterize the heterogeneity of the poorly controlled population receiving maintenance inhaled therapy with various devices in primary care. Methods: In a multicenter, cross-sectional analysis of 988 patients from the Study SIMPLIFY, clinical control of COPD was classified using the objective RADAR score. We used multivariable logistic regression and Machine Learning (Random Forest with SHAP analysis) to identify determinants of poor control (RADAR ≥ 4) and k-medoids cluster analysis to characterize the poorly controlled subgroup (n = 452). Results: Nearly half the cohort (45.7%, n = 452) had poor clinical control. Agreement between physician-assessed control (five categories) and RADAR classification was 49.3%, with overestimation in 34.0% and underestimation in 16.7% of cases (Cohen’s κ = −0.081; weighted κ = −0.037). The strongest independent determinants were the exacerbator phenotypes (eosinophilic aOR 6.85; non-eosinophilic aOR 4.91). Key modifiable factors included active smoking (aOR 1.92), lower TAI-12 adherence score (per point; aOR 0.96), high dosing frequency (≥4 inhalations/day; aOR 1.54) and high inhaler burden (≥3 devices; aOR 1.84). Machine learning analysis identified clinical phenotype and adherence behavior as the top two scale-independent predictors of poor control. Cluster analysis of the poorly controlled group revealed five reproducible and clinically meaningful phenotypes (C0–C4), primarily separated by treatment complexity, comorbidities, and adherence. Conclusions: Poor clinical control is common and critically under-recognized in primary care patients with COPD on maintenance inhaled therapy. This is driven by a profound clinician perception gap and a failure to address key modifiable determinants, such as high dosing frequency, regimen complexity, and poor adherence, which likely drives therapeutic inertia. Our findings underscore the need to integrate objective tools to unmask poor control and highlight the importance of treatment simplification. The identification of distinct clinical phenotypes provides a roadmap toward a more personalized, evidence-based standard of care. Full article

This work presents a systematic methodology for the preliminary design and optimization of electromechanical actuators, aimed at minimizing overall mass and rotational inertia while satisfying torque and speed requirements. The proposed approach integrates dimensionless scaling relationships, derived and corrected from catalog data, with a genetic algorithm that performs multi-parameter optimization across different actuator architectures. The algorithm enables the exploration of non-linear and multi-modal design spaces, allowing the identification of balanced solutions between mechanical efficiency and dynamic performance, employing custom functions for individual generation, constraint handling, and compatibility verification to ensure feasible and consistent architecture designs throughout the optimization process. A case study on the steering system of an aircraft nose landing gear illustrates the method’s ability to define optimal design parameters in real mechanical systems. Linear and non-linear dynamic analyses confirmed the compliance of the optimized design with control and stability requirements. The study demonstrates how the developed custom constrained genetic optimization approach can effectively support the early design phase, reducing the computational effort required in further stages and improving the overall consistency of electromechanical actuator development. Full article

This study presents the experimental and model-based characterization of polydimethylsiloxane (PDMS) as a damping medium in a torsional vibration viscous damper. Particular emphasis is placed on the influence of the PDMS viscosity on the dynamic response of the damper under variable hydrodynamic loading generated by torsional vibrations of the system and the mass of the inertia ring. Investigations were conducted over a wide range of kinematic viscosities, enabling the identification of damper operating regimes and the assessment of lubricating film stability. The developed mathematical model, based on hydrodynamic lubrication theory, describes the relationships between the PDMS viscosity, the relative angular velocity, and the eccentricity of the inertia ring. Experimental results confirm the model’s ability to predict transitions between stable, unstable, and boundary operating modes of the damper. The proposed approach enables the functional, system-level characterization of PDMS under hydrodynamic loading conditions within a torsional vibration damper. In this framework, the rheological properties of PDMS are directly linked to the dynamic response and operational stability of the mechanical system. Full article