Search Results (3,988)

Against the backdrop of increasing penetration of renewable energy sources such as wind and solar power, coupled with intermittent regional power restrictions, ensuring the quality of power transmission has become increasingly critical. The volatility and uncertainty of wind and photovoltaic output exacerbate dynamic fluctuations in net load on the grid side, necessitating hydroelectric units to undertake more frequent Automatic Generation Control (AGC) regulation tasks in complementary hydro–wind–solar operations. However, frequent regulation processes significantly intensify the operational stress on actuating mechanisms within the governor system, thereby accelerating wear and degradation of equipment such as hydraulic turbine servomotors. This study employs modeling and simulation to investigate the influence and mechanistic role of key control parameters in the AGC process on the wear of hydraulic turbine servomotors. Utilizing pulse count and pulse width metrics, a reasonable quantification of this impact is established. A multi-objective optimization framework for AGC parameters is constructed, and frontier solutions are selected based on quantified equipment wear values. Simulation results indicate that the optimized parameters achieve a balanced performance in terms of settling time, steady-state performance, and comprehensive dynamic metrics during power closed-loop transition processes. This approach effectively mitigates the actuation intensity of servomotors while satisfying regulation quality requirements, thereby enhancing the overall performance of the power closed-loop adjustment process. Full article

Electro-hydraulic servo systems are widely used in industrial machinery and automation due to their high power density and fast dynamic response; however, their achievable positioning accuracy is often limited by nonlinear friction effects. In many robust control strategies, including sliding mode control and its multi-surface variants, friction is commonly treated as a lumped bounded disturbance. This simplification neglects the dynamic and operating condition-dependent nature of friction, leaving the practical value of explicit friction compensation insufficiently clarified, especially for electro-hydraulic actuators operating near their bandwidth limits. This paper presents an experimental evaluation of LuGre-based dynamic friction compensation integrated into a multi-surface sliding mode control framework for electro-hydraulic actuators. Rather than proposing a new control methodology, the study focuses on clarifying, from a control-oriented mechanical engineering perspective, how friction compensation influences closed-loop tracking performance under different operating regimes. The proposed scheme is implemented on a laboratory-scale electro-hydraulic test bench and evaluated using step and sinusoidal reference motions over a wide range of excitation frequencies, from low-speed operation to the practical bandwidth limit of the actuator. Comparative experiments with a conventional proportional–integral–derivative controller and a multi-surface sliding mode controller without friction compensation are conducted to isolate the effect of explicit friction modeling. The experimental results reveal a strongly frequency-dependent influence of friction on tracking performance. At low excitation frequencies (e.g., 0.1 Hz), friction compensation provides only marginal improvement in root mean square (RMS) tracking errors. In contrast, as the excitation frequency approaches the actuator bandwidth limit (1 Hz), explicit LuGre-based friction compensation reduces the relative RMS tracking error by approximately 57% compared with the baseline MSSM controller and by up to 82% relative to a conventional PID controller. These results demonstrate that the effectiveness of friction compensation is highly dependent on operating conditions, providing experimentally grounded guidance for the design of control strategies for bandwidth-limited electro-hydraulic machines. Full article

Low-carbon scheduling of regional integrated energy systems (RIES) based only on energy-balance models may overlook the physical operating limits of distribution, gas, and heating networks, resulting in a gap between scheduling outcomes and actual operating boundaries. To address this issue, this paper proposes a framework integrating bi-objective scheduling, three-network posterior verification, and closed-loop repair. A mixed-integer linear programming model is first formulated with operating cost and carbon emissions as the two objectives, and an adaptive ε-constraint strategy is used to improve the characterization of the compromise region on the Pareto front. Posterior verification models are then established for the distribution, gas, and heating networks to assess the physical feasibility of representative solutions. When infeasibility is detected, a boundary-shrinking repair mechanism is triggered to iteratively update the scheduling boundaries. Case results show that the adaptive refined strategy improves the resolution of the compromise region by 3.2 times with only a 20.4% increase in computational time. Compared with the cost-optimal solution, the carbon-optimal solution reduces carbon emissions but increases peak purchased electricity from 7.333 MW to 11.1 MW, further tightening the lower-voltage margin of the distribution network. The results show that posterior physical verification and closed-loop repair provide additional support for evaluating and improving the engineering feasibility of RIES scheduling solutions. Full article

When the global energy transition is analyzed through economic lenses, the constraints imposed by the laws of thermodynamics are often overlooked. This study addresses the Latecomer’s Dilemma—the predicament of semi-peripheral nations compelled to decarbonize without the capital stock accumulated following the example of the countries of the Global North during their more than two hundred years of industrial development associated with the saturation of the atmosphere with carbon dioxide. A novel phase space model of the Anthropocene is constructed, synthesizing the political concept of ecological debt with the biophysical reality of entropy debt. The application of the laws of systems ecology and non-equilibrium thermodynamics enables the mapping of national development trajectories against the saturated “atmospheric bathtub”. The analysis identifies a critical Injustice Gap—a region of phase space physically foreclosed by historical emissions. Moreover, it has been demonstrated that a circular economy powered by low-density renewables functions as an entropy trap, converting material debt into radiative debt without achieving a closed loop. Consequently, the Polish correction vector is proposed as a stabilization mechanism. This study’s findings indicate that addressing the emerging phenomenon of adaptation apartheid necessitates the implementation of a high-density energy flux, namely Generation IV nuclear reactors, which would be funded by a retroactive ETS3 mechanism. This approach fulfills the thermodynamic condition for material closure, thereby substantiating the notion that energy justice constitutes a physical necessity for planetary stability. This study quantifies the historical radiative debt of a single early-industrialized hub (Manchester) at approximately 142.8 billion EUR. The novelty lies in the synthesis of biophysical laws and the Latecomer’s Dilemma through the proposed ETS3 mechanism. Full article

Real-time simulation plays an important role in the development and verification of modular multi-level converter (MMC) systems, especially for the rapid and low-risk evaluation of control and protection functions in medium- and high-voltage applications. However, MMC validation often requires simulation models with different fidelity levels for different testing purposes, while detailed device-level representation further imposes stringent constraints on computational efficiency. To address these issues, this paper develops a multi-level real-time modeling framework for MMCs, in which switch models of different accuracy can be incorporated within a unified architecture and flexibly selected according to the target test scenario. On this basis, a device-level real-time simulation method is further established to capture the nonlinear switching transients of MMCs under the proposed framework. By combining network decoupling with FPGA-oriented implementation, the framework can achieve a minimum simulation step of 50 ns under fully parallel hardware allocation. Considering FPGA resource optimization, the prototype implemented in this work is validated with a 100 ns time-step. A three-phase MMC with four submodules per arm is used as the validation case and implemented on an FPGA platform. Both waveform comparisons and quantitative error analysis demonstrate close agreement between the proposed real-time model and offline reference models. In addition, closed-loop real-time experiments are conducted to further confirm the effectiveness of the developed MMC model in realistic real-time simulation-based testing applications. Full article

Vegetative restoration in degraded landscapes requires scalable deployment strategies capable of adapting to heterogeneous terrain conditions. Conventional aerial seeding methods typically operate in open-loop mode, distributing seeds uniformly without considering terrain suitability. This study presents a modular, vision-guided unmanned aerial vehicle (UAV) system for real-time adaptive seed deployment based on the closed-loop integration of onboard perception and actuation under embedded computational constraints. The proposed system combines RGB-based terrain classification, embedded processing, and altitude-adaptive seed dispensing within a unified perception–decision–actuation framework, enabling selective and context-aware seed deployment during flight. Terrain suitability is evaluated onboard using three convolutional neural network (CNN) models and a color-based baseline to distinguish sowable and non-sowable areas. A confidence-based decision strategy with temporal filtering improves reliability, while an altitude-adaptive control mechanism regulates seed distribution across varying flight heights. Field experiments conducted in semi-arid environments demonstrate classification accuracy above 85% with inference latency below 100 ms on a Jetson Nano platform. Additional offline evaluation under varying altitude, speed, illumination, and terrain conditions confirms the robustness of the perception module. The results demonstrate the feasibility of integrating real-time perception with adaptive actuation, enabling UAVs to transition from passive sensing platforms to active agents for environmental intervention. The proposed system provides a practical and scalable approach for precision vegetative restoration in heterogeneous environments. Full article

With the increasing penetration of renewable energy, power systems require fast and reliable frequency regulation resources. Vehicle-to-grid (V2G) aggregation can provide fast response capability. However, it relies heavily on communication networks and is vulnerable to communication degradation and false data injection attacks (FDIAs). To address this challenge, this paper proposes a detection-free resilient control method for V2G-based frequency regulation. Rather than relying on explicit attack detection or compensation, the proposed method achieves decision-level adaptation from closed-loop system feedback through dynamic selection and switching of aggregator subsets. In this way, unreliable or compromised aggregators are implicitly avoided, improving system robustness under uncertain communication and cyber conditions. To further enhance robustness, a diffusion-based adversarial reinforcement learning framework is developed. A conditional diffusion model is used to generate diverse capacity scenarios with spatial heterogeneity. Adversarial training formulates the interaction between the attacker and the defender as a zero-sum game. This enables the learning of robust selection–switching policies under worst-case disturbances. Simulation results on the IEEE 39-bus system show that the proposed method improves frequency regulation performance under communication degradation and FDIA. The RMS frequency deviation is reduced from 0.13426 Hz to 0.09174 Hz compared with the no-defense case. Full article

Additive manufacturing (AM) has emerged as a transformative technology across multiple industries, from aerospace to biomedical applications. The integration of artificial intelligence (AI) and machine learning (ML) into AM processes represents a paradigm shift toward intelligent, autonomous manufacturing systems. This comprehensive review synthesizes recent advances in AI/ML applications across the entire AM life cycle—from design optimization and process planning through in situ monitoring, closed-loop control, and post-process qualification. The analysis is organized by ISO/ASTM AM process families, including powder bed fusion (PBF), directed energy deposition (DED), material extrusion (MEX), vat photopolymerization (VP), binder jetting (BJ), material jetting (MJT), and sheet lamination (SL). For each process family, the review examines the specific AI/ML techniques employed, the data modalities utilized (thermal imaging, acoustic signals, in situ cameras, CT/NDE data), and the current state of deployment from research prototypes to industrial implementation. The analysis reveals that while significant progress has been made in single-stage ML applications such as defect detection and parameter optimization, truly integrated life cycle-wide AI-driven AM workflows remain largely aspirational. Key challenges are identified including data scarcity, model generalization across machines and materials, real-time control constraints, and certification requirements. Finally, future research directions are outlined toward autonomous AM systems enabled by physics-informed ML, digital twins, and hierarchical AI architectures. Full article

This paper presents a theoretical engineering feasibility analysis of the RK-X photocatalytic process for In Situ Resource Utilisation (ISRU) on Mars. Experimental validation under simulated Martian conditions is the essential next step before any mission deployment claim can be made. The RK-X process converts the two most abundant Martian resources, atmospheric carbon dioxide (CO₂) and subsurface water ice (H₂O), into formic acid (HCOOH) and oxygen (O₂) through a fulvic acid-based photocatalytic cycle validated at the industrial scale in Hungary. A reference module processing 10 tonnes of CO₂ per Earth year yields 10.459 tonnes of formic acid and 3.636 tonnes of oxygen, sufficient to sustain a six-person crew for approximately two Earth years with a 198% safety margin over nominal respiratory demand. The economic analysis indicates that importing equivalent oxygen from Earth costs $1.82–$3.64 million per year; equivalent energy storage (Li-ion) costs $30.5–$61 million for one-time use. Formic acid stores 15.25 MWh of energy in ambient-stable liquid form at a round-trip efficiency of 68.64% without cryogenic infrastructure. A photovoltaic array of 55.37 m² provides the primary energy source; a kilowatt-class nuclear fission reactor constitutes the strategic opportunity for continuous, dust-storm-immune operation with free thermal co-generation. Three critical research gaps have been identified requiring laboratory validation before Mars deployment: (i) catalyst performance at the Martian CO₂ partial pressure (p(CO₂) < 10 mbar, T = 15 °C); (ii) water ice and dry ice extraction at an operational scale; and (iii) integrated closed-loop system demonstration. Built on Earth-proven chemistry with identified, addressable development pathways, the RK-X process theoretically resolves the problems of oxygen supply, seasonal energy storage, water management, and cryogenic infrastructure within a single closed-loop chemical cycle. Full article

In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid. Full article

Solar tracking systems (STSs) are widely adopted in photovoltaic (PV) installations to increase energy yield by maintaining favorable module orientation relative to the sun’s trajectory. This paper presents a systematic review of STSs from an electrical engineering perspective, focusing on electrical performance, control strategies, and system integration aspects relevant to grid-connected PV applications. Fixed-tilt, single-axis, and dual-axis configurations are comparatively assessed in terms of output power, annual energy yield, influence on I–V and P–V characteristics, and auxiliary power consumption. The analysis emphasizes net energy gain rather than gross energy improvement. Control strategies are classified as open-loop, closed-loop, hybrid, and intelligent approaches. Their impact on tracking accuracy, actuator duty cycles, electrical stability, and coordination with maximum power point tracking (MPPT) algorithms is critically examined. A bibliographic and scientometric analysis is conducted to identify research trends, dominant themes, and existing gaps. The results indicate that single-axis tracking often provides the most favorable balance between energy gain and auxiliary consumption in utility-scale systems, while dual-axis configurations achieve higher absolute yield at increased complexity. The review highlights the need for standardized net-energy evaluation and grid-aware tracking strategies. Full article

Against the backdrop of global energy pattern restructuring, the advancement of dual-carbon goals and large-scale development of unconventional oil and gas, complex oil and gas fields are confronted with practical challenges including harsh geological conditions and diversified development objectives. Traditional development planning methods for oil and gas fields suffer from single evaluation dimensions, strong subjectivity in decision making and insufficient dynamic adaptability, which make them unable to meet the full-process development requirements. To realize scientific, quantitative and systematic development planning of complex oil and gas fields, a development planning evaluation method suitable for complex oil and gas fields is established by integrating multidisciplinary theories. First, a multilevel evaluation model for oil and gas field development planning is constructed according to the characteristics and difficulties of development planning evaluation for complex oil and gas fields. The model consists of five core modules: external analysis, internal analysis, corporate development strategy selection, business planning and risk assessment. Secondly, a development planning evaluation method is established through a closed-loop process including special quantitative IFE/EFE analysis, IE matrix strategic positioning, SWOT alternative strategy pool and QSPM priority ranking. Then, the strategic priority ranking is dynamically adjusted by considering the impact of stepped oil prices. Finally, combined with the analytic hierarchy process (AHP), a comprehensive risk index evaluation model is established to realize quantitative assessment and traceability of risk levels. A case application in Block M demonstrates that its strategic positioning belongs to the growth type. Under low–medium–high tiered oil prices, the strategic combinations with the highest strategic priority are W+O strategy, S+O strategy and S+O, respectively. The development risk level is moderate risk. This study fills the gap in the whole-process evaluation system of complex oil and gas fields, and realizes the transformation of development planning from qualitative analysis to quantitative decision making. It provides theoretical methods and practical references for ensuring high-quality development of complex oil and gas fields and energy security. Full article

The trajectory tracking problem of underactuated unmanned surface vehicles (USVs) with unknown physical parameters arising from hydrodynamic effects is addressed using a robust control strategy based on a sliding-mode dynamic neural network identifier. To handle the unknown physical parameters, a dynamic neural network identifier with a novel structure is developed, enabling the construction of an equivalent mathematical model of the USV dynamics. To compensate for the underactuated nature of the system, a coordinate transformation is introduced. Using this transformation, together with the proposed identifier, a nonsingular sliding-mode controller is designed. Lyapunov-based analysis establishes finite-time convergence of the neural weight estimation errors to zero and convergence of the identification errors to a bounded neighborhood of zero. Furthermore, once the identification errors enter this bounded region, they asymptotically converge to zero. In addition, the closed-loop stability analysis guarantees finite-time convergence of the tracking errors. The effectiveness of the proposed identifier–controller framework is validated through simulation studies that incorporate explicit actuator saturation constraints and external disturbances to emulate realistic operating conditions. These results demonstrate the practical applicability of the proposed control strategy, as the commanded inputs remain within the physical limits of the propulsion system. Comparative results with a state-of-the-art model-based super-twisting controller show that the proposed approach achieves comparable tracking performance while eliminating the need for prior knowledge of the system’s dynamic parameters. 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

This paper investigates the distributed formation control problem for nonlinear multiagent systems subject to full-state constraints and proposes a predefined-time neural adaptive control scheme based on a nonlinear mapping technique. To handle the time-varying asymmetric constraints on system states, a smooth and invertible nonlinear mapping function is introduced to transform the original constrained states into unconstrained variables, thereby eliminating the dependence on initial conditions typically required by traditional barrier Lyapunov functions. Within this transformed framework, a predefined-time distributed formation control law is developed, which guarantees that all followers converge to the desired formation configuration and track the leader’s trajectory within a user-specified time upper bound, independent of the initial states. Radial basis function neural networks are employed to approximate the unknown nonlinear dynamics of each agent, and adaptive laws are designed to update the network weights online. Theoretical analysis shows that all closed-loop signals remain bounded, the original system states strictly stay within their prescribed constraint boundaries at all times, and the formation tracking errors converge to a small neighborhood of the origin within the predefined time. Numerical simulations validate the effectiveness of the proposed method, demonstrating faster convergence, higher steady-state accuracy, and improved robustness to initial conditions compared to existing control approaches. Full article