Search Results (316)

As critical actuating components in vehicular transmission systems, wet clutches exhibit strongly nonlinear thermal responses in their friction pairs during engagement operations. Although existing temperature prediction models achieve high-accuracy prediction performance, their practical application remains constrained by significant limitations such as high computational costs and time consumption. This study proposes an Optuna-LSTM temperature prediction model for wet clutch friction pairs, developed through the integration of long short-term memory (LSTM) deep learning theory with finite element method generated training datasets under diverse operating conditions. By synergistically combining the automated hyperparameter optimization library (Optuna) framework and early stopping mechanisms, the model enables dynamic temperature prediction of friction pairs. Experimental results indicate that the proposed model achieves prediction metrics of Root Mean Squared Error (RMSE) of 1.42 °C, Mean Absolute Error (MAE) of 1.09 °C, Coefficient of Determination (R²) of 0.9930, and Mean Absolute Percentage Error (MAPE) of 0.72% with a prediction duration of 60 ms. These findings confirm that the Optuna-LSTM model enables both accurate and rapid temperature prediction for friction pairs, providing an efficient solution for thermal management in wet clutch systems. Full article

Liquid crystal elastomers (LCEs) are soft, stimuli-responsive materials capable of converting molecular-scale reorientation of mesogenic groups into macroscopic, reversible deformations. In this study, film-shaped LCEs were fabricated via a thiol–ene click reaction and characterized under varying electric-field strengths and tensile loads. The LCEs exhibited pronounced soft elasticity and large, reversible strains due to reorientation of mesogenic groups, in contrast to non-mesogenic elastomers. Dynamic viscoelastic measurements revealed fully reversible changes in storage modulus upon electric-field application, highlighting the critical role of mesogenic group alignment in electromechanical actuation. Electric-field-induced contraction increased with field strength and decreased with tensile load, reaching a maximum of 15% under minimal load, with additional enhancement resulting from mesogenic group reorientation along the thickness direction. Notably, unlike conventional light- or temperature-driven actuation, which is often limited by slow molecular relaxation or heat diffusion, the electric-field-driven LCE exhibited rapid, fully reversible deformation. These findings demonstrate the potential of electric-field-responsive LCEs for applications requiring fast, controllable, and large-strain actuation, and provide insights into the interplay between mesogenic group orientation, mechanical loading, and external stimuli in designing efficient soft actuators. Full article

A novel hydraulically actuated uniform clamping flange connection mechanism is proposed to address the long-standing challenges in high-pressure natural gas flowmeter calibration, including cumbersome bolt-by-bolt assembly/disassembly, high leakage risk, and severe non-uniform gasket contact pressure associated with conventional multi-bolt flanges. Unlike traditional discrete bolt loading, the proposed mechanism generates a continuous and actively adjustable circumferential clamping force via an integrated hydraulic annular piston, ensuring excellent sealing uniformity and rapid installation within minutes. A high-fidelity transient finite element model of the hydraulic clamping flange assembly is established, incorporating the nonlinear compression/rebound behavior of flexible graphite–stainless steel spiral-wound gaskets and one-way fluid–structure interaction under water hammer loading. Parametric studies reveal that reducing the effective clamping area to below 80% of the original design significantly intensifies stress concentration and compromises sealing integrity, while clamping force below 80% or above 120% of the nominal value leads to leakage or component overstress, respectively. Under steady 10 MPa pressurization, the flange exhibits a maximum stress of 150.57 MPa, a minimum gasket contact stress exceeding 30 MPa, and a rotation angle below 1°, demonstrating robust sealing performance. During a severe water hammer event induced by rapid valve closure, the peak flange stress remains acceptable at 140.41 MPa, while the minimum gasket contact stress stays above the critical sealing threshold (38.051 MPa). However, repeated water hammer cycles increase the risk of long-term gasket fatigue. This study introduces, for the first time, a hydraulic uniform-clamping flange solution that dramatically improves sealing reliability, installation efficiency, and operational safety in high-pressure flowmeter calibration and similar temporary high-integrity piping connections, providing crucial technical guidance for field applications. Full article

With the rapid development of the low-altitude economy, UAV logistics delivery systems have garnered widespread attention due to their flexibility and efficiency. The cooperative delivery mode involving a UAV with a suspended payload and a ground vehicle represents a typical networked distribution scenario, whose performance is constrained by the tight coupling of sensing, communication, and control. In practical applications, sensor measurement noise and sudden disturbances propagate through the closed-loop system, severely degrading velocity synchronization and swing angle stability. To address this challenge, this paper focuses on a quadrotor UAV slung-load system and proposes a three-layer nested networked closed-loop control architecture for simultaneous velocity tracking of a moving ground target and swing angle stabilization. First, by establishing the system’s dynamic model, the mapping relationship between cable tension and the payload swing angle (based on sensor feedback) is revealed. Then, by setting the payload velocity as the outermost control objective and constructing a coupled error to drive a virtual swing angle actuator, the direct impact of noise in the raw sensor data is effectively mitigated. Subsequently, the desired acceleration of the UAV is derived through inverse computation, achieving synchronous optimization of velocity tracking and swing angle suppression. Theoretical analysis using Lyapunov methods demonstrates the stability of the closed-loop system in the presence of bounded delays. Simulation results show that the proposed method effectively suppresses payload swing, controls velocity synchronization error, and exhibits strong robustness against sensor noise and sudden disturbance. This study provides a control solution that improves the precision and robustness of sensor-enabled networked control systems in complex dynamic scenarios Full article

This paper presents a discrete-time, centralized fractional-order linear quadratic regulator FOLQR for automatic generation control (AGC) of three-area interconnected nonreheat thermal systems. The AGC state explicitly includes the area control error (ACE) and tie-line power; a quadratic performance index penalizes ACE, its integral (IACE), and control effort. The continuous-time plant (governor–turbine dynamics and tie-line flows) is discretized at a fixed sampling interval, and a single centralized gain is obtained from the discrete algebraic Riccati equation; the fractional-order extension shapes memory in the feedback to temper rapid transients. Benchmark studies under 0.01 and 0.05 p.u. step-load disturbances show that FOLQR stabilizes the interconnection and consistently lowers peak excursions relative to a conventional discrete LQR (COQAGC) baseline—reducing frequency peaks by about 9–12% and tie-line peaks by 24–60% in the small-step case—while producing smoother actuator commands. Although FOLQR exhibits longer settling times, this trade-off is acceptable FOr multi-area AGC where limiting overshoot and tie-line excursions is operationally more critical than strict settling-time targets. The proposed controller retains a simple centralized, discrete-time structure with a modest computational burden, making it suitable FOr real-time AGC deployment in large interconnected grids and demonstrating for the first time, to our knowledge, a fractional-order LQR applied to a three-area thermal benchmark. Full article

This paper addresses the trajectory tracking problem for a six-degree-of-freedom (6-DOF) underwater manipulator subject to complex disturbances and input saturation. It proposes a fixed-time preset performance sliding mode control method considering input saturation (FT-PP-SMC-IS), aiming to achieve rapid and stable tracking performance under these constraints. Firstly, to improve modeling accuracy, the Newton–Euler method and Morison’s equation are integrated to establish a more precise dynamic model of the underwater manipulator. Secondly, to balance dynamic and steady-state performance, a preset performance function is designed to constrain the tracking error boundaries. Based on dual-limit homogeneous theory, a fixed-time sliding mode surface is constructed, significantly enhancing the convergence speed and fixed-time stability. Furthermore, to suppress the effects of input saturation, a fixed-time auxiliary system is designed to compensate in real-time for deviations caused by actuator saturation. By separately constructing the sliding mode reaching law and equivalent control law, global fixed-time convergence of the system states is ensured. Based on Lyapunov stability theory, the fixed-time stability of the closed-loop system is rigorously proven. Finally, comparative simulation experiments verify the effectiveness and superiority of the proposed method. Full article

The primary objective of this study is to enable an unmanned surface vehicle (USV) to autonomously approach the extremum of an unknown scalar field using only real-time field measurements. To this end, a source-seeking method based on timescale separation is developed within a hierarchical control framework that divides the closed-loop system into a slow and a fast subsystem. The slow subsystem governs the gradual evolution of the USV pose and generates reference heading and surge commands from local scalar field information, providing a directional cue toward the field extremum. The fast subsystem applies actuator-level control inputs that ensure these references are tracked with sufficient accuracy through rapid corrective actions. A Lyapunov-based analysis is carried out to study the stability properties of the coupled slow–fast dynamics and to establish conditions under which convergence can be guaranteed in the presence of model nonlinearities and external disturbances. Numerical simulations are conducted to illustrate the resulting system behavior and to verify that the proposed framework maintains stable seeking performance under typical operating conditions. Full article

This paper presents a proof-of-concept for a natural-language-based closed-loop controller that regulates the temperature of a simple single-input single-output (SISO) thermal process. The key idea is to express a relay-with-hysteresis policy in plain English and let a local large language model (LLM) interpret sensor readings and output a binary actuation command at each sampling step. Beyond interface convenience, we demonstrate that natural language can serve as a valid medium for modeling physical reality and executing deterministic reasoning in control loops. We implement a compact plant model and compare two controllers: a conventional coded relay and an LLM-driven controller prompted with the same logic and constrained to a single-token output. The workflow integrates schema validation, retries, and a safe fallback, while a stepwise evaluator checks agreement with the baseline. In a long-horizon (1000-step) simulation, the language controller reproduces the hysteresis behavior with matching switching patterns. Furthermore, sensitivity and ablation studies demonstrate the system’s robustness to measurement noise and the LLM’s ability to correctly execute the hysteresis policy, thereby preserving the theoretical robustness inherent to this control law. This work demonstrates that, for slow thermal dynamics, natural-language policies can achieve comparable performance to classical relay systems while providing a transparent, human-readable interface and facilitating rapid iteration. Full article

Flexible forming technology breaks through the traditional reliance on rigid molds in the hot-pressing process and demonstrates great potential for fabricating large, lightweight composite components with curved geometries. However, the precise actuation and error control of discrete units in flexible molds remain key technical challenges in the flexible forming of composites. This study proposes a high-precision and efficient method for the shape adjustment and error compensation of flexible multi-point molds. The proposed approach integrates the tangential offset unit configuration (TOUC) algorithm with an industrial robot to establish a robot-assisted precision driving system (RAPDS) for flexible molds. Furthermore, the main error-influencing factors of RAPDS are identified through correlation analysis and response surface modeling (RSM). Based on these findings, a backpropagation neural network (BPNN) is employed to predict adjustment errors, and heuristic algorithms guided by the structural characteristics of the BPNN are embedded into the framework to construct a bi-level optimization strategy that enhances model performance. The experimental results show that, compared with traditional methods, the robot-assisted flexible mold driving system improves the accuracy of shape adjustment by 31.0% and increases the production efficiency of composite components by 66.7%. Overall, this study develops a rapid, efficient, and highly precise flexible multi-point forming method for composite components, demonstrating strong potential for industrial applications. Full article

The transition toward energy-efficient and smart buildings requires Digital Twins (DTs) that can couple real-time data with physics-based Building Energy Models (BEMs) for predictive and adaptive operation. Yet, despite rapid digitalisation, there remains a lack of practical guidance and real-world implementations demonstrating how calibrated BEMs can be effectively integrated into Building Management Systems (BMSs). This study addresses that gap by presenting a complete and reproducible end-to-end framework for embedding physics-based BEMs into operational DTs using two setups: (i) encapsulation as Functional Mock-up Units (FMUs) and (ii) containerisation via Docker. Both approaches were deployed and tested in a real educational building in Cáceres (Spain), equipped with a LoRaWAN-based sensing and actuation infrastructure. A systematic comparison highlights their respective trade-offs: FMUs offer faster execution but limited weather inputs and higher implementation effort, whereas Docker-based workflows provide full portability, scalability, and native interoperability with Internet of Things (IoT) and BMS architectures. To enable real-time operation, a surrogate modelling framework was embedded within the Docker architecture to replicate the optimisation logic of the calibrated BEM and generate predictive blind control schedules in milliseconds—bypassing simulation overhead and enabling continuous actuation. The combined Docker + surrogate setup achieved 10–15% heating energy savings during winter operation without any HVAC retrofit. Beyond the case study, this work provides a step-by-step, in-depth guideline for practitioners to integrate calibrated BEMs into real-time control loops using existing toolchains. The proposed approach demonstrates how hybrid physics- and data-driven DTs can transform building management into a scalable, energy-efficient, and operationally deployable reality. Full article

Artificial muscles translate the biological principles of motion into soft, adaptive, and multifunctional actuation. This review accordingly highlights research into natural actuation strategies, such as skeletal muscles, muscular hydrostats, spider silk, and plant turgor systems, to reveal the principles underlying energy conversion and deformation control. Building on these insights, polymer-based artificial muscles based on these principles, including pneumatic muscles, dielectric elastomers, and ionic electroactive systems, are described and their capabilities for efficient contraction, bending, and twisting with tunable stiffness and responsiveness are summarized. Furthermore, the abilities of carbon nanotube composites and twisted yarns to amplify nanoscale dimensional changes through hierarchical helical architectures and achieve power and work densities comparable to those of natural muscle are discussed. Finally, the integration of these actuators into soft robotic systems is explored through biomimetic locomotion and manipulation systems ranging from jellyfish-inspired swimmers to octopus-like grippers, gecko-adhesive manipulators, and beetle-inspired flapping wings. Despite rapid progress in the development of artificial muscles, challenges remain in achieving long-term durability, energy efficiency, integrated sensing, and closed-loop control. Therefore, future research should focus on developing intelligent muscular systems that combine actuation, perception, and self-healing to advance progress toward realizing autonomous, lifelike machines that embody the organizational principles of living systems. Full article

Realizing the full potential of optical actuation for high-speed phase-change radio-frequency (RF) switches requires a shift to single-pulse operation. This work presents a systematic investigation of reversible phase transitions in GeTe thin films induced by single 10 ns laser pulses, utilizing spatially resolved characterization techniques, including atomic force microscopy (AFM) and micro-spectroscopy. Precise laser fluence windows for crystallization (12.7–16 mJ/cm²) and amorphization (25.44–41.28 mJ/cm²) are established. A critical finding is that the amorphization process is governed by rapid thermal accumulation, which creates a direct trade-off between achieving the phase transition and avoiding detrimental surface morphology. Specifically, we observe that excessive energy leads to the formation of laser-induced ridges and ablation craters, which are identified as primary causes of device performance degradation. This study elucidates the underlying mechanism of single-pulse-induced phase transitions and provides a practical processing window and design guidelines for developing high-performance, optically actuated GeTe-based RF switches. Full article

Background: Shape memory alloy spring actuators offer significant potential for advanced actuation systems in exoskeletons, medical devices, and robotics, but adoption has been limited by slow activation speeds and insufficient design guidelines for achieving rapid response times while maintaining structural integrity. Objective: This study aimed to establish comprehensive design parameters for nickel–titanium spring actuators capable of achieving sub-second activation times through systematic experimental characterization and performance optimization. Methods: Nine different nickel–titanium spring configurations with wire diameters ranging from 0.5 to 0.8 mm and spring indices from 6 to 8 were systematically evaluated using differential scanning calorimetry for thermal characterization, mechanical testing for material properties, high-current electrical activation studies spanning 5–11 A, infrared thermal distribution analysis, and laser displacement sensing for dynamic response measurement. Results: Dynamic testing achieved activation times below 1 s for currents exceeding 5 A, with maximum displacement recoveries reaching 600–800% strain recovery, while springs with intermediate spring index values of 6.5–7.5 provided optimal balance between force output and displacement range, and optimal activation involved moderate current levels of 5–7 A for thin wires and 8–11 A for thick wires. Conclusions: Systematic geometric optimization combined with controlled high-current density activation protocols enables rapid actuation response while maintaining structural integrity, providing essential design parameters for engineering applications requiring fast, reliable actuation cycles. Full article

Wearable robotic devices for rehabilitation and assistive applications face a critical challenge: discomfort induced by prolonged pressure at the human–robot interface. Conventional attachment systems with static straps or rigid cuffs frequently exceed pain tolerance thresholds, limiting clinical acceptance and patient adherence. This study presents a novel dynamic pressure modulation system using thermally activated Twisted and Coiled Artificial Muscles (TCAMs). The system integrates a lightweight lattice structure (0.1 kg) with biocompatible silicone coating incorporating two TCAMs fabricated from silver-coated nylon 6,6 fibers (Shieldex 235/36 × 4 HCB). Electrothermal activation via 2 A constant current induces axial contraction, dynamically regulating circumferential pressure from 0.05 kgf/cm² to 0.50 kgf/cm² within physiological comfort ranges. Experimental validation on a wrist-worn prototype demonstrates precise pressure control, rapid response (5–10 s), and thermal safety through 8 mm Ecoflex insulation. The system enables on-demand interface stiffening during robotic actuation and controlled pressure release during rest periods, significantly enhancing comfort and device tolerability. This approach represents a promising solution for clinically viable wearable robotic devices supporting upper limb rehabilitation and activities of daily living. Full article

This paper investigates a distributed robust tracking control method with prescribed convergence time for heterogeneous air–ground multi-agent systems under the combined effects of deception attacks and actuator faults. Considering the corruption of state information caused by attacks, a time-varying constraint function is first designed, and a command filtering mechanism is introduced. Through coordinate transformation, the disturbed state is indirectly estimated and safely fed back. To cope with actuator malfunctions leading to uncertain control effectiveness, a rationally designed adaptive law is developed for real-time identification and compensation of such uncertainties. Furthermore, within the backstepping control framework, the concept of time-varying constraints is integrated to propose an adaptive prescribed-time controller, transforming the tracking control problem into an error constraint form, thereby ensuring the system error converges within a specified range within a given time. To reduce communication load, the controller is implemented with an event-triggered mechanism, where control signals are updated only at trigger times, effectively avoiding Zeno behavior. Finally, the boundedness and stability of the closed-loop system are proven using Lyapunov methods. Simulation results demonstrate that this control strategy maintains stable and rapid heterogeneous formation tracking performance even in the presence of deception attacks and actuator faults. Full article