Search Results (1,272)

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

Additive manufacturing offers rapid and customizable production, yet conventional plastic-based methods remain energy-intensive and environmentally harmful, often resulting in higher impacts per part than traditional manufacturing. The goal of this study was to evaluate whether upcycled biomaterials, specifically oyster shells, pistachio shells, and clay, could be used as lower-impact alternatives to PLA in 3D printing. The scope included detailed measurement of print parameters for each material and a full life cycle assessment (LCA) of the printed elements, covering printer manufacturing, raw material extraction, transport, operation, and end of life. The results show that ambient-temperature extrusion of these upcycled biomaterials can reduce energy consumption by up to 89% and overall environmental impact by up to 94% (as measured by ReCiPe Endpoint H points) compared to PLA printing. These reductions were observed for the Netherlands and EU contexts, where electricity mixes are relatively clean and recycling rates are high; even greater improvements were observed for the US. Although the printed biomaterial objects exhibit lower mechanical strength, limited waterproofness, and reduced print resolution, they are already suitable for low-load applications such as prototypes and architectural models. Overall, the findings demonstrate that upcycled biomaterial extrusion has strong sustainability potential, outperforming both conventional plastics and bioplastics such as PLA in terms of material impacts and energy use. Continued development of material formulations as well as pre- and post-processing techniques could further expand functionality and support the broader adoption of low-impact 3D printing across a wide range of applications. Full article

Cable-driven robotic systems are widely used in applications requiring lightweight structures, large workspaces, and accurate force regulation. In such systems, the mechanical behavior of cable-driven actuators is strongly influenced by the elastic properties of the cable, transmission elements, and supporting structure, leading to an effective stiffness that varies with pretension, applied load, cable length, and operating conditions. These stiffness variations have a direct impact on force control performance but are often implicitly treated or assumed constant in control-oriented studies. This paper investigates the effects of operating-point-dependent (incremental) cable stiffness on actuator-level force control performance in cable-driven robotic systems. The analysis is conducted at the level of an individual cable-driven actuator to isolate local mechanical effects from global robot dynamics. Mechanical stiffness is characterized within a limited elastic domain through local linearization around stable operating points, avoiding the assumption of global linear behavior over the entire force range. Variations in effective stiffness induced by changes in pretension, load, and motion regime are analyzed through numerical simulations and experimental tests performed on a dedicated test bench. The results demonstrate that stiffness variations significantly affect force tracking accuracy, dynamic response, and disturbance sensitivity, even when controller structure and tuning parameters remain unchanged. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

Ground-source heat pump (GSHP) systems are widely regarded as an energy-efficient solution for building heating and cooling. However, their actual performance in large commercial buildings is often limited by rigid control strategies, insufficient equipment coordination, and suboptimal load matching. In the Liuzhou Fengqing Port commercial complex, the seasonal coefficient of performance (SCOP) of the GSHP system remains at a relatively low level of 3.0–3.5 under conventional operation. To address these challenges, this study proposes a gray-box-model-based cooperative optimization and group control strategy for GSHP systems. A hybrid gray-box modeling approach (YFU model), integrating physical-mechanism modeling with data-driven parameter identification, is developed to characterize the energy consumption behavior of GSHP units and variable-frequency pumps. On this basis, a multi-equipment cooperative optimization framework is established to coordinate GSHP unit on/off scheduling, load allocation, and pump staging. In addition, continuous operational variables (e.g., chilled-water supply temperature and circulation flow rate) are globally optimized within a hierarchical control structure. The proposed strategy is validated through both simulation analysis and on-site field implementation, demonstrating significant improvements in system energy efficiency, with annual electricity savings of no less than 3.6 × 10⁵ kWh and an increase in SCOP from approximately 3.2 to above 4.0. The results indicate that the proposed framework offers strong interpretability, robustness, and engineering applicability. It also provides a reusable technical paradigm for intelligent energy-saving retrofits of GSHP systems in large commercial buildings. Full article

Modern power transmission systems are required to meet increasingly stringent demands, including a wide range of transmission ratios, compact dimensions, high precision, energy efficiency, reliability, and thermal stability under dynamic operating conditions. Among the solutions that satisfy these requirements, cycloidal reducers are particularly prominent, with their application continuously expanding in industrial robotics, computer numerical control (CNC) machines, and military and transportation systems, as well as in the satellite industry. However, as with all mechanical power transmissions, friction in the contact zones of load-carrying elements in cycloidal reducers leads to power losses and an increase in operating temperature, which in turn results in a range of adverse effects. These undesirable phenomena strongly depend on lubrication conditions, namely on the type and properties of the applied lubricant. Although manufacturers’ catalogs provide general recommendations for lubricant selection, they do not address the fundamental tribological mechanisms in the most heavily loaded contact pairs. At the same time, the available scientific literature reveals a significant lack of systematic and experimentally validated studies examining the influence of lubricant type on the energetic and thermal performance of cycloidal reducers. To address this identified research gap, this study presents an analytical and experimental investigation of the effects of different lubricant types—primarily greases and mineral oils—on the thermal stability and efficiency of cycloidal reducers. The results demonstrate that grease lubrication provides lower total power losses and a more stable thermal operating regime compared to oil lubrication, while oil film thickness analyses indicate that the most unfavorable lubrication conditions occur in the contact between the eccentric bearing rollers and the outer raceway. These findings provide valuable guidelines for engineers involved in cycloidal reducer design and lubricant selection under specific operating conditions, as well as deeper insight into the lubricant behavior mechanisms within critical contact zones. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

In this paper, a flower-shaped spoof surface plasmon polaritons (SSPPs) unit with strong slow-wave effect is proposed to construct bandpass filters (BPFs). Benefiting from extended current path induced by addition of rotated stubs around rectangular unit, the proposed SSPPs unit exhibits reduced asymptotic frequency. Following this, a single-band filter boasting multiple transmission zeros (TZs) in its upper stopband is developed by embedding the unit into half-mode substrate integrated waveguide (HMSIW). To improve suppression of the lower stopband, a pair of open circuited stubs are loaded to produce TZs and enhance its frequency selectivity. Consequently, the single-band BPF realizes an impressive roll-off rate of 0.116 dB/MHz. Subsequently, geometric dimensions of the open-circuited stubs are modified to dispose the TZs into passband and acquire dual-band operation. In addition, defected ground structures (DGSs) are loaded to broaden the bandwidth of notch between two passbands. Finally, a dual-band filter with a wide suppression band of 0.50 GHz is developed. With roll-off rates of 0.096 and 0.119 dB/MHz, the filter demonstrates good selectivity as well. Full article

Earthwork operations constitute a substantial share of infrastructure project costs and are critical to overall project efficiency. However, the construction industry still relies on conventional approaches and there is a lack of integrated fleet management systems for collaboratively working equipment. While telematics is widely used in other industries, its applications to monitor the complex interactions between excavators, dump trucks, and dozers in real time remain limited. This study proposes an intelligent fleet monitoring system that utilizes only satellite navigation data (GNSS) to analyze the real-time productivity of multiple earthwork machines without relying on additional sensors, such as IMU or accelerometers, thereby eliminating the need for separate measurement procedures. A lightweight site configuration step is required to define the work area/loading/dumping geofences on an existing site map. This research provides novel developed algorithms that facilitate a real-time productivity assessment for several earthwork equipment and provide planning-level recommendations for equipment deployment combinations. Dedicated motion classification algorithms were developed for excavators, dump trucks, and dozers to distinguish activity states, to compute working and idle times, and to quantify operational efficiency. The system integrates a web-based e-Fleet Management platform and a mobile e-Map application for visualization and equipment optimization. Field validation was conducted on two active earthwork projects to evaluate accuracy and feasibility. The results demonstrate that the developed algorithms achieved classification and productivity estimation errors within 2.5%, while enabling optimized equipment combinations and improved cycle time efficiency. The proposed system offers a practical, sensor-independent approach for enhancing productivity monitoring, real-time decision-making, and cost efficiency in large-scale earthwork operations. Full article

This paper presents experimental testing results which describe the response of modern residential loads and electric vehicle (EV) chargers to various voltage magnitude, frequency, and phase angle disturbances. The purpose of these tests is to replicate real life network conditions and assist Network Service Providers and the Australian Energy Market Operator in identifying and predicting potential power variation and system stability issues caused by load behaviour during power system transient phenomena. By examining the behaviour of typical loads connected to distribution networks, a deeper understanding of their response can be achieved, enabling the refinement of composite load models that are compatible with the Western Electricity Coordinating Council dynamic composite load model (CMPLDW) structure presently used for dynamic studies. The performance of a wide range of common appliances found in residential settings, such as refrigerators, microwave ovens, air conditioners, direct-on-line motor-based appliances, and EV chargers, has been evaluated. The results obtained from these tests offer valuable insights into the behaviour of different load types and illustrate differing performances from established model parameters, identifying the need to refine existing CMPLDW models. The results also support the reclassification of several appliances within the composite load model, motivate the introduction of a dedicated EV charger component, and empower network operators to improve the modelling of modern power network responses. Full article

Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and is highly sensitive to traffic fluctuations that could lead the system outside of its stable operational regime. Although theoretical results specifying the optimal transmission probability that maximizes the successful preamble transmission probability are well known, the lack of knowledge about the current offered traffic load at the BS makes the problem of maintaining the optimal throughput a challenging task. In this paper, we propose and analyze a new reactive access-barring scheme for NB+IoT systems based on machine learning (ML) techniques. Specifically, we first demonstrate that knowing the number of user equipments (UE) experiencing a collision at the BS is sufficient to make conclusions about the current offered traffic load. Then, we show that through utilizing ML-based techniques, one can safely differentiate between events in the Physical Random Access Channel (PRACH) at the base station (BS) side based on only the signal-to-noise ratio (SNR). Finally, we mathematically characterize the delay experienced under the proposed reactive access-barring technique. In our numerical results, we show that by utilizing modern neural network approaches, such as the XGBoost classifier, one can precisely differentiate between events on the PRACH channel with accuracy reaching 0.98 and then associate it with the number of user equipment (UE) competing at the random access phase. Our simulation results show that the proposed approach can keep the successful preamble transmission probability constant at approximately 0.3 in overloaded conditions, when for conventional NB-IoT access, this value is less than 0.05. The proposed scheme achieves near-optimal throughput in multi-channel ALOHA by employing dynamic traffic awareness to adjust the non-unit transmission probability. This proactive congestion control ensures a controlled and bounded delay, preventing latency from exceeding the system’s maximum load capacity. Full article

Tunnel boring machine (TBM) hydraulic cylinders operate under pronounced start–stop shocks and load uncertainties, making it difficult to simultaneously achieve smooth start-up and high-precision tracking. This paper proposes a two-phase switching adaptive sliding mode control (ASMC) strategy for TBM hydraulic actuation. Phase I targets a soft start by introducing smooth gating and a ramped start-up mechanism into the sliding surface and equivalent control, thereby suppressing pressure spikes and displacement overshoot induced by oil compressibility and load transients. Phase II targets precise tracking, combining adaptive laws with a forgetting factor design to maintain robustness while reducing chattering and steady-state error. We construct a state-space model that incorporates oil compressibility, internal/external leakage, and pump/valve dynamics, and provide a Lyapunov-based stability analysis proving bounded stability and error convergence under external disturbances. Comparative simulations under representative TBM conditions show that, relative to conventional PID Controller and single ASMC Controller, the proposed method markedly reduces start-up pressure/velocity peaks, overshoot, and settling time, while preserving tracking accuracy and robustness over wide load variations. The results indicate that the strategy can achieve the unity of smooth start and high-precision trajectory of TBM hydraulic cylinder without additional sensing configuration, offering a practical path for high-performance control of TBM hydraulic actuators in complex operating environments. Full article

This study focuses on investigating the adaptive sliding mode control (SMC) problem for connected vehicles with input delays and unknown time-varying control coefficients. As a result of wear and tear of mechanical components, throttle response lags, and the internal data processing time of the controller, input delays widely exist in vehicle actuators. Since input delays may lead to instability of the vehicle platoon, an improved integral compensation mechanism (ICM) with the adjustment factor for input delays is developed to improve the platoon’s robustness. As the actuator efficiency, drive mechanism, and load of the vehicle may change during operation, the control coefficients of vehicle dynamics are usually unknown and time-varying. A novel adaptive updating mechanism utilizing a radial basis function neural network (RBFNN) is designed to deal with the unknown time-varying control coefficients, thereby improving the vehicle platoon’s tracking performance. By integrating the improved ICM and the RBFNN-based adaptive updating mechanism (RBFNN−AUM), an innovative distributed adaptive control scheme using sliding mode techniques is proposed to guarantee that the convergence of state errors to a predefined region and accomplish the vehicle platoon’s control objectives. Comparative numerical results confirm the effectiveness and superiority of the developed control strategy over existing method. Full article

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R² of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R² ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R² ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation. Full article

The operation of the superheated steam temperature system significantly impacts the safety and economy of thermal power units. To ensure its stable operation under large-scale variable load conditions, a modified active disturbance rejection control strategy based on parameter adaptation is proposed. Firstly, a typical superheated steam temperature system model is introduced, and the cascade control structure is applied to the model. Then, on this basis, a modified active disturbance rejection control strategy based on parameter adaptation is proposed, and the parameter tuning method of the modified active disturbance rejection control is introduced. Finally, the control performance of the proposed control strategy under a wide range of variable loads is verified through comparative simulations under nominal working conditions and uncertain working conditions. To further illustrate the effectiveness of the proposed strategy, the method is applied to a certain 660 MW unit in the field. After implementing the method, the fluctuation range of superheated steam temperature on the A and B sides decreased to only 34.0% and 53.0% of the original, respectively, and the fluctuation variance on the A and B sides decreased to only 28.5% and 43.3% of the original, respectively. The above field application results fully demonstrate that the control strategy proposed does not merely remain at the theoretical simulation level, but is a key technical means that can be effectively implemented and effectively solve the problem of superheated steam temperature control in thermal power units. Full article