Search Results (490)

This paper presents an online efficiency optimization strategy for a six-phase induction generator (6PIG) operating in both healthy and faulty modes for micro-hydropower applications. The proposed method is based on an extended Loss Model Control (LMC) approach, in which the direct axis stator current Id is dynamically optimized in real time to minimize the total electrical losses. Unlike conventional LMC strategies, this method explicitly incorporates switching losses into the loss model, along with stator and rotor copper losses and iron losses. The optimization problem is solved using a numerical minimization routine, allowing the control system to adapt continuously to variations in torque requests. The proposed approach is validated under both healthy and faulty configurations of the 6PIG. It is implemented and tested through simulation in MATLAB/Simulink^® and experimentally validated on a 24 kW squirrel cage six-phase induction generator (SC6PIG). The results are compared in terms of power losses, energy saving, and efficiency. Full article

To address the impact of insulation medium degradation in the arc quenching chambers of ultra-high-voltage SF₆ circuit breakers on phase-controlled switching accuracy caused by multiple operations throughout the service life, this paper proposes an adaptive switching algorithm. First, a modified formula for the breakdown voltage of mixed gases is derived based on the synergistic effect. Considering the influence of contact gap on electric field distortion, an adaptive switching strategy is designed to quantify the dynamic relationship among operation times, insulation strength degradation, and electric field distortion. Then, multi-round switching-on and switching-off tests are carried out under the condition of fixed single-arc ablation amount, and the laws of voltage–current, gas decomposition products, and pre-breakdown time are obtained. The test data are processed by the least squares method, adaptive switching algorithm, and machine learning method. The results show that the coincidence degree of the pre-breakdown time obtained by the adaptive switching algorithm and the test value reaches 90%. Compared with the least squares fitting, this algorithm achieves a reasonable balance between goodness of fit and complexity, with prediction deviations tending to be randomly distributed, no obvious systematic offset, and low dispersion degree. It can also explain the physical mechanism of the decay of insulation degradation rate with the number of operations. Compared with the machine learning method, this algorithm has stronger generalization ability, effectively overcoming the defects of difficult interpretation of physical causes and the poor engineering adaptability of the black box model. Full article

Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article

This paper addresses the issues of sensor failures and actuator faults in mining tracked mobile vehicles (TMVs) operating in harsh environments by proposing a global fixed-time fault-tolerant control strategy based on a hierarchical unknown input observer structure. First, a kinematic and dynamic model of the TMV is established considering side slip and track slip, and its linear parameter-varying (LPV) model is constructed through parameter-dependent linearization. Then, a distributed structure consisting of four collaborating low-dimensional observers is designed, including a state observer, a disturbance observer, a position sensor fault observer, and a wheel speed sensor fault observer, and the fixed-time convergence of the closed-loop system is proven. Additionally, by equivalently treating actuator faults as power losses, an observer capable of identifying and compensating for motor efficiency losses is designed. Finally, an adaptive fault-tolerant control law is proposed by combining nominal control, disturbance compensation, and sliding mode switching terms, achieving global fixed-time stability and fault tolerance. Experimental results demonstrate that the proposed control system maintains excellent trajectory tracking performance even in the presence of sensor faults and actuator power losses, with tracking errors less than 0.1 m. Full article

Hub motor-driven vehicles (HMDVs) suffer from poor handling and stability due to an increased unsprung mass and unbalanced radial electromagnetic forces. Although traditional ground-hook control reduces the dynamic tire load, it severely worsens the body acceleration. This paper presents a generalized ground-hook control strategy based on impedance transfer functions to address the parameter redundancy in structural methods. A quarter-vehicle model with a switched reluctance motor wheel hub drive was used to study different orders of generalized ground-hook impedance transfer function control strategies for dynamic inertial suspension. An enhanced fish swarm parameter optimization method identified the optimal solutions for different structural orders. Analyses showed that the third-order control strategy optimized the body acceleration by 2%, reduced the dynamic tire load by 8%, and decreased the suspension working space by 22%. This strategy also substantially lowered the power spectral density for the body acceleration and dynamic tire load in the low-frequency band of 1.2 Hz. Additionally, it balanced computational complexity and performance, having slightly higher complexity than lower-order methods but much less than higher-order structures, meeting real-time constraints. To address time-domain deviations from generalized ground-hook control in semi-active systems, a dynamic compensation strategy was proposed: eight topological structures were created by modifying the spring–damper structure. A deviation correction mechanism was devised based on the frequency-domain coupling characteristics between the wheel speed and suspension relative velocity. For ride comfort and road-friendliness, a dual-frequency control criterion was introduced: in the low-frequency range, energy transfer suppression and phase synchronization locking were realized by constraining the ground-hook damping coefficient or inertance coefficient, while in the high-frequency range, the inertia-dominant characteristic was enhanced, and dynamic phase adaptation was permitted to mitigate road excitations. The results show that only the T0 and T5 structures met dynamic constraints across the frequency spectrum. Time-domain simulations showed that the deviation between the T5 structure and the third-order generalized ground-hook impedance model was relatively small, outperforming traditional and T0 structures, validating the model’s superior adaptability in high-order semi-active suspension. Full article

The object of this research is the process of tracking air targets at the initial stage of radar system operation. The problem lies in the lack of a comprehensive approach to tracking air targets in difficult conditions that is able to dynamically adapt filtering parameters, predict signal reliability, and change the processing mode depending on the level of interference. In conditions of signal loss, noise, and unstable measurement reliability, traditional methods do not provide stable and accurate tracking, especially at the initial stages of radar operation. To address this issue, an intelligent method is proposed that integrates a probabilistic graphical evaluation and review technique (GERT) model, a recursive Kalman filter, and a measurement reliability prediction module based on a long short-term memory (LSTM) neural network. The proposed approach allows for the real-time adaptation of filtering parameters, fusion of local and global trajectory estimates, and dynamic switching between tracking modes depending on the environmental conditions. The dynamic weighting algorithm between model estimates ensures a balance between accuracy and robustness. Simulation experiments confirmed the effectiveness of the method: the root mean square error (RMSE) of coordinate estimation was reduced by 25%; the probability of tracking loss decreased by half (from 0.2 to 0.1); and the accuracy of loss prediction exceeded 85%. The novelty of the approach lies in integrating stochastic modeling, machine learning, and classical filtering into a unified adaptive loop. The proposed system can be adapted to various types of radar and easily scaled to multi-sensor architectures. This makes it suitable for practical implementation in both defense and civilian air object detection systems operating under complex conditions. Full article

Fault recovery in distribution networks is a complex, high-dimensional decision-making task characterized by partial observability, dynamic topology, and strong interdependencies among components. To address these challenges, this paper proposes a graph-based multi-agent deep reinforcement learning (DRL) framework for intelligent fault restoration in power distribution networks. The restoration problem is modeled as a partially observable Markov decision process (POMDP), where each agent employs graph neural networks to extract topological features and enhance environmental perception. To address the high-dimensionality of the action space, an action decomposition strategy is introduced, treating each switch operation as an independent binary classification task, which improves convergence and decision efficiency. Furthermore, a collaborative reward mechanism is designed to promote coordination among agents and optimize global restoration performance. Experiments on the PG&E 69-bus system demonstrate that the proposed method significantly outperforms existing DRL baselines. Specifically, it achieves up to 2.6% higher load recovery, up to 0.0 p.u. lower recovery cost, and full restoration in the midday scenario, with statistically significant improvements ($p<0.05$ or $p<0.01$). These results highlight the effectiveness of graph-based learning and cooperative rewards in improving the resilience, efficiency, and adaptability of distribution network operations under varying conditions. Full article

This research studies the impact of macroeconomic announcement surprises on daily U.S. Treasury excess returns during the heart of Alan Greenspan’s tenure as Federal Reserve Chair, addressing the possible limitations of standard static regression (SSR) models, which may suffer from omitted variable bias, parameter instability, and poor mis-specification diagnostics. To complement the SSR framework, an automated general-to-specific (Gets) modeling approach, enhanced with modern indicator saturation methods for robustness, is applied to improve empirical model discovery and mitigate potential biases. By progressively reducing an initially broad set of candidate variables, the Gets methodology steers the model toward congruence, dispenses unstable parameters, and seeks to limit information loss while seeking model congruence and precision. The findings, herein, suggest that U.S. Treasury market responses to macroeconomic news shocks exhibited stability for a core set of announcements that reliably influenced excess returns. In contrast to computationally costless standard static models, the automated Gets-based approach enhances parameter precision and provides a more adaptive structure for identifying relevant predictors. These results demonstrate the potential value of incorporating interpretable automated model selection techniques alongside traditional SSR and Markov switching approaches to improve empirical insights into macroeconomic announcement effects on financial markets. Full article

The permanent magnet synchronous motor (PMSM) is a critical device that converts kinetic energy into mechanical energy. However, it faces issues such as nonlinearity, time-varying uncertainties, and external disturbances, which may degrade the system control performance. To address these challenges, this paper proposes a prescribed performance model-free adaptive fast integral terminal sliding mode control (PP-MFA-FITSMC) method. This approach replaces conventional techniques such as parameter identification, function approximation, and model reduction, offering advantages such as quantitative constraints on the PMSM tracking error, reduced chattering, strong disturbance rejection, and ease of engineering implementation. The method establishes a compact dynamic linearized data model for the PMSM system. Then, it uses a discrete small-gain extended state observer to estimate the composite disturbances in the PMSM online, effectively compensating for their adverse effects. Meanwhile, an improved prescribed performance function and error transformation function are designed, and a fast integral terminal sliding surface is constructed along with a discrete approach law that adaptively adjusts the switching gain. This ensures finite-time convergence of the control system, forming a model-free, low-complexity, high-performance control approach. Finally, response surface methodology is applied to conduct a sensitivity analysis of the controller’s critical parameters. Finally, controller parameter sensitivity experiments and comparative experiments were conducted. In the parameter sensitivity experiments, the response surface methodology was employed to design the tests, revealing the impact of individual parameters and parameter interactions on system performance. In the comparative experiments, under various operating conditions, the proposed strategy consistently constrained the tracking error within ±0.0028 rad, demonstrating superior robustness compared to other control methods. Full article

We propose a novel framework for adaptive stochastic dynamics analysis of tourist behavior by integrating context-aware Markov models with finite mixture models (FMMs). Conventional Markov models often fail to capture abrupt changes induced by external shocks, such as event announcements or weather disruptions, leading to inaccurate predictions. The proposed method addresses this limitation by introducing virtual sensors that dynamically detect contextual anomalies and trigger regime switches in real-time. These sensors process streaming data to identify shocks, which are then used to reweight the probabilities of pre-learned behavioral regimes represented by FMMs. The system employs expectation maximization to train distinct Markov sub-models for each regime, enabling seamless transitions between them when contextual thresholds are exceeded. Furthermore, the framework leverages edge computing and probabilistic programming for efficient, low-latency implementation. The key contribution lies in the explicit modeling of contextual shocks and the dynamic adaptation of stochastic processes, which significantly improves robustness in volatile tourism scenarios. Experimental results demonstrate that the proposed approach outperforms traditional Markov models in accuracy and adaptability, particularly under rapidly changing conditions. Quantitative results show a 13.6% improvement in transition accuracy (0.742 vs. 0.653) compared to conventional context-aware Markov models, with an 89.2% true positive rate in shock detection and a median response latency of 47 min for regime switching. This work advances the state-of-the-art in tourist behavior analysis by providing a scalable, real-time solution for capturing complex, context-dependent dynamics. The integration of virtual sensors and FMMs offers a generalizable paradigm for stochastic modeling in other domains where external shocks play a critical role. Full article

In this study, to address the problems of the redundant safety and mass production of electro-mechanical braking (EMB) structures that are widely used in distributed drive electric vehicles (DDEV), we designed a compact dual-disc electromagnetic–EMB composite brake. The composite brake embeds an electromagnetic brake into the original friction disc, which realizes an organic combination of the friction and electromagnetic brakes. Electromagnetic braking has the advantages of no friction, a rapid response, and a high-speed braking effect, which can effectively improve the reliability and mechanical redundancy of composite braking systems. The braking system comprises regenerative, electromagnetic, and friction braking, which are typical hybrid systems. We designed a mode-switching control strategy for a composite braking system based on the hybrid control theory. MATLAB/Simulink were used to model each system and set different simulation conditions. The simulation results showed that, under different working conditions, the hybrid automata control strategy had a fast response speed, small overshoot error, and adapted to different road conditions. The feasibility of the redundant design of the electromagnetic–friction–regenerative composite braking structure and the rationality of the hybrid automata control strategy design were verified. Full article

With the advancement in power electronics technology, variable-frequency drives have been widely adopted for motor operation due to their inherent benefits: control performance, extending equipment life, and energy savings. The most used technique is Sine Pulse Width Modulation, as it solely requires the modification of the reference signal (sine wave). However, Space Vector Pulse Width Modulation offers lower total harmonic distortion. Therefore, this study presents a technique for the control of induction motors operating in open-loop mode, utilizing a two-level voltage source inverter with a frequency range of 31 to 300 Hz. The proposed control system is modified to encompass between 930 and 1848 switching periods, varying the number of switching periods along with the frequency variation. This approach allows the use of a single LCL filter across the whole frequency spectrum. It is adapted for implementation in an 8-bit microcontroller, which has its inherent limitations, yet it achieves performance levels similar to those found in high-level processors like FPGAs and DSPs. The signals generated by the microcontroller are captured by a DAQ card and input into a MATLAB/Simulink model to observe and analyze the performance of the proposed control system. Full article

The issue of multi-switch generalized projective anti-synchronization of fractional-order chaotic systems is investigated in this work. The model is constructed using Caputo–Fabrizio derivatives, which have been rarely addressed in previous research. In order to expand the symmetric and asymmetric synchronization modes of chaotic systems, we consider modeling chaotic systems under such fractional calculus definitions. Firstly, a new fractional-order differential inequality is proven, which facilitates the rapid confirmation of a suitable Lyapunov function. Secondly, an effective multi-switching controller is designed to confirm the convergence of the error system within a short moment to achieve synchronization asymptotically. Simultaneously, a multi-switching parameter adaptive principle is developed to appraise the uncertain parameters in the system. Finally, two simulation examples are presented to affirm the correctness and superiority of the introduced approach. It can be said that the symmetric properties of Caputo–Fabrizio fractional derivative are making outstanding contributions to the research on chaos synchronization. Full article

Background/Objectives: Pneumococcal conjugate vaccines (PCVs) were first introduced in the pediatric UK National Immunization Programme (NIP) in 2006 and subsequently led to a significant decline in invasive pneumococcal disease (IPD). In 2020, the UK NIP reduced the pediatric PCV dosing schedule from two infant doses and one toddler dose (2 + 1) to one infant dose and one toddler dose (1 + 1). This analysis evaluated the public health impact of pediatric vaccination with PCV15 versus PCV13 under a 1 + 1 schedule. Methods: A population-level compartmental model was previously adapted to the UK setting. The impact on the IPD incidence of vaccination with PCV15 versus PCV13 under a 1 + 1 schedule was evaluated over a 20-year time horizon. The uncertainty regarding the vaccine efficacy (VE) of PCV13 and PCV15 under a 1 + 1 schedule was investigated through a probabilistic sensitivity analysis, i.e., the PCV VE under a 1 + 1 schedule was assumed to be 0–24% lower than the PCV VE under a 2 + 1 schedule. Results: Relative to the initial IPD incidence, vaccination with PCV13 and PCV15 under a 1 + 1 schedule resulted in the IPD incidence in children <2 years old increasing by 11.1% (95% region: 8.4–14.5%) and 3.5% (0.2–7.7%), respectively, over the time horizon. At the end of the time horizon, in the overall population, PCV15 would lead to a 6.0% lower IPD incidence than PCV13 (10.70 IPD cases per 100,000 versus 11.38 per 100,000, respectively). Conclusions: Switching from PCV13 to PCV15 for routine pediatric vaccinations under the 1 + 1 dosing schedule in the UK led to a lower IPD incidence in both the pediatric and overall populations. Full article

Efficient scheduling in industrial prefabrication environments—such as Prefabricated Bathroom Unit (PBU) production—faces increasing challenges due to resource limitations, overlapping projects, and complex task dependencies. To address these challenges, this paper presents a Learning-Enhanced Differential Evolution (LEDE) framework for solving the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP). The MRCMPSP models the operational difficulty of coordinating interdependent activities across multiple PBU projects under limited resource availability. To address the computational intractability of this NP-hard problem, we first formulate a mixed-integer linear programming (MILP) model, and then develop an adaptive DE-based metaheuristic. The proposed LEDE method co-evolves activity sequencing and mode assignment using floating-point encodings, incorporating strategy switching, parameter adaptation, elitism, stagnation handling, and rank-based crossover control. Evaluated on real-world production data from the PBU industry, the algorithm produces high-quality solutions with strong scalability. These results demonstrate its practical potential as a decision-support tool for dynamic, resource-constrained industrial scheduling. Full article