Search Results (74)

High-precision torque regulation is essential to ensure reaction wheel systems meet the stringent attitude control requirements of modern spacecraft. In three-phase half-bridge brushless DC (BLDC) drives, non-ideal back-electromotive force (back-EMF) waveforms cause pronounced conduction interval torque ripple, leading to inaccurate and unstable output torque. To address this problem, this article proposes a composite torque control strategy integrating an Adaptive Nonsingular Fast Terminal Sliding-Mode Observer (ANFTSMO) with an Adaptive Sliding-Mode Controller (ASMC). The ANFTSMO achieves precise back-EMF estimation and electromagnetic torque reconstruction by eliminating singularities, reducing chattering, and adaptively adjusting observer gains. Meanwhile, the ASMC employs an adaptive switching gain function to achieve asymptotic current convergence with suppressed chattering, thereby ensuring accurate current tracking. System stability is verified via Lyapunov analysis. Simulation and experimental results demonstrate that, compared with conventional constant-current control, the torque smoothness and disturbance rejection of the proposed method are improved, enabling precise and stable reaction wheel torque delivery for high-accuracy spacecraft attitude regulation. Full article

In response to the demands for high reliability, excellent dynamic response, and high-precision control of advanced aircraft actuation systems, this study focuses on the control technology for the master-master operating mode of dual-redundancy electro-hydrostatic actuation (EHA) systems. A multi-domain coupling model integrating motor magnetic circuit saturation, hydraulic viscosity-temperature characteristics, and mechanical clearances was established, based on which a current-loop decoupling technique using vector control was developed. Furthermore, the study combined adaptive sliding mode control (ASMC) and an improved active disturbance rejection control (ADRC) to enhance the robustness of the speed loop and the disturbance rejection capability of the position loop, respectively. To address the key challenges of synchronous error accumulation and uneven load distribution in the master-master mode, a dual-redundancy dynamic model accounting for hydraulic coupling effects was developed, and a two-level cooperative control strategy of "position synchronization-dynamic load balancing" was proposed based on the cross-coupling control (CCC) framework. Experimental results demonstrate that the position loop control error is less than ±0.02 mm, and the load distribution accuracy is improved to over 97%, fully meeting the design requirements of advanced aircraft. These findings provide key technical support for the engineering application of power-by-wire flight control systems in advanced aircraft. Full article

The development of robust and efficient wireless charging systems is essential for the widespread adoption of electrification in the transport sector, e.g., Electric Vehicles (EVs). Capacitive Wireless Power Transfer (CWPT) has emerged as a promising alternative to inductive methods, offering advantages such as lower cost, lighter structure, and reduced electromagnetic interference. However, the performance of practical CWPT systems, particularly systems employing simple L-type compensation networks, is severely affected by coupling plate misalignment, which causes variations in coupling capacitance. These variations give rise to a pseudo-resonance phenomenon, wherein conventional controllers, such as traditional Sliding Mode Control, mistakenly regulate reactive power to zero at an off-resonant frequency, leading to a drastic collapse in active power transfer. To overcome this limitation, this paper introduces a novel Adaptive Sliding Mode Control (ASMC) framework augmented with an online Recursive Least Squares (RLS) observer for real-time estimation of the time-varying coupling capacitance. The proposed dual-loop control structure integrates an inner adaptive loop that accurately tracks capacitance changes and an outer sliding mode loop that dynamically adjusts the inverter switching frequency to sustain true resonant operation. A rigorous Lyapunov-based stability analysis confirms global convergence and robustness of the closed-loop system. Comprehensive MATLAB/Simulink R2025a simulations validate the proposed approach, demonstrating its capability to maintain zero reactive power and stable 35 kW power transfer with over 95% efficiency under dynamic misalignment conditions of up to 30%. In contrast, a conventional SMC approach experiences severe pseudo-resonant collapse, with output power degrading below 1 kW. These results conclusively highlight the effectiveness and necessity of the proposed ASMC-RLS strategy for achieving robust, misalignment-tolerant CWPT in high-power EV charging applications. Full article

Human airways maintain homeostasis through intricate cellular interactomes combining secretome-mediated signalling and mechanotransduction feedback loops. This review presents the first unified map of bidirectional mechanobiology–secretome interactions between airway epithelial cells (AECs), smooth muscle cells (ASMCs), and chondrocytes. We unify a novel three-component regulatory architecture: epithelium functioning as environmental activators, smooth muscle as mechanical actuators, and cartilage as calcium-dependent regulators. Critical mechanotransduction pathways, particularly YAP/TAZ signalling and TRPV4 channels, directly couple matrix stiffness to cytokine release, creating a closed-loop feedback system. During development, ASM-driven FGF-10 signalling and peristaltic contractions orchestrate cartilage formation and epithelial differentiation through mechanically guided morphogenesis. In disease states, these homeostatic circuits become pathologically dysregulated; asthma and COPD exhibit feed-forward stiffness traps where increased matrix rigidity triggers YAP/TAZ-mediated hypercontractility, perpetuating further remodelling. Aberrant mechanotransduction drives smooth muscle hyperplasia, cartilage degradation, and epithelial dysfunction through sustained inflammatory cascades. This system-level understanding of airway cellular networks provides mechanistic frameworks for targeted therapeutic interventions and tissue engineering strategies that incorporate essential mechanobiological signalling requirements. Full article

This research aims to investigate the practicality and feasibility of pneumatic muscle actuators (PMAs) applied in the pneumatic servo system. The mechanism consists of closed symmetrical planar robotic arms driven by two pairs of opposing PMAs, whose structure is similar to human arms. Importantly, the two distal links (or wrist parts) are combined into a collective end-effector, whose desired position is controlled only by the two shoulder angle joints. When two pairs of PMAs are attached to the upper arms, they actuate each shoulder and assist in the movement of the arms. However, the nonlinear behavior, high hysteresis, low damping, and time-varying characteristics of PMAs significantly limit their controllability. Therefore, to effectively address these challenges, a Fourier series-based adaptive sliding mode controller with $H_{\mathrm{\infty }}$ (FSB-ASMC + $H_{\mathrm{\infty }}$) is employed to achieve accurate path tracking of the PMAs. This control approach not only compensates for approximation errors, disturbances, and unmodeled dynamics but also ensures the desired $H_{\mathrm{\infty }}$ positioning performance of the overall system. The controller method can not only effectively prevent approximation errors, disturbances, and un-modeled dynamics but can also ensure the required $H_{\mathrm{\infty }}$ positioning performance of the whole system. Thus, the results of the experiment showed that the control strategy for the system collocating the FSB-ASMC + $H_{\mathrm{\infty }}$ can attain excellent control performance. Full article

The human lower extremity plays a vital role in locomotion, posture, and weight-bearing through coordinated motion at the hip, knee, and ankle joints. These joints facilitate essential functions including flexion, extension, and internal and external rotation. To address mobility impairments through personalized therapy, this study presents the design, dynamic modeling, and control of a four-degree-of-freedom (4-DOF) lower limb exoskeleton robot. The system actuates hip flexion–extension and internal–external rotation, knee flexion–extension, and ankle dorsiflexion–plantarflexion. Anatomically aligned joint axes were incorporated to enhance biomechanical compatibility and reduce user discomfort. A detailed CAD model ensures ergonomic fit, modular adjustability, and the integration of actuators and sensors. The exoskeleton robot dynamic model, derived using Lagrangian mechanics, incorporates subject-specific anthropometric parameters to accurately reflect human biomechanics. A conventional sliding mode controller (SMC) was implemented to ensure robust trajectory tracking under model uncertainties. To overcome limitations of conventional SMC, an adaptive sliding mode controller with boundary layer-based chattering suppression was developed. Simulations in MATLAB/Simulink 2025 R2025a demonstrate that the adaptive controller achieves smoother torque profiles, minimizes high-frequency oscillations, and improves tracking accuracy. This work establishes a comprehensive framework for anatomically congruent exoskeleton design and robust control, supporting the future integration of physiological intent detection and clinical validation for neurorehabilitation applications. Full article

Asthma is a chronic respiratory disease affecting over 262 million people worldwide, with obesity-associated asthma emerging as a distinct endotype of increasing prevalence characterized by metabolic inflammation and airway remodeling. Unlike allergic asthma, this phenotype is driven by chronic low-grade inflammation, originating from hypertrophic and hypoxic adipose tissue. This dysregulated state leads to the activation of pro-inflammatory pathways and the secretion of cytokines, contributing to airway dysfunction and remodeling. Recent evidence highlights non-coding RNAs (ncRNAs) as key regulators of these processes. MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) influence inflammation and remodeling by modulating immune cell polarization, cytokine secretion, extracellular matrix composition, and airway smooth muscle cell (ASMC) proliferation. Notably, H19, MEG3, GAS5, miR-26a-1-3p, and miR-376a-3p have been implicated in both asthma and obesity, suggesting their role in linking metabolic dysfunction with airway pathology. Moreover, ncRNAs regulate Treg/Th17 balance, fibroblast activation, and autophagy-related pathways, further influencing airway remodeling. Our in silico analysis highlighted the IGF1R signaling pathway as a key enriched mechanism, linking selected ncRNAs with metabolic dysregulation and inflammation in obesity-related asthma. This paper reviews how ncRNAs regulate inflammation and airway remodeling in obesity-associated asthma, emphasizing their potential molecular links between metabolic dysfunction and airway pathology. Full article

This paper develops a dynamic information-theoretic network framework to quantify systemic risk in China’s maritime–commodity nexus with a focus on the Yangtze River Basin using eight monthly indicators, CCFI, CBCFI, BDI, YRCFI, GAUP, MPCT, CPUS, and ASMC. We resample, impute, standardize, and difference series to achieve stationary time series. Nonlinear interdependencies are estimated via KSG mutual information (MI) within sliding windows; networks are filtered using the Planar Maximally Filtered Graph (PMFG) with bootstrap edge validation (95th percentile) and benchmarked against the MST. Average MI indicates moderate yet heterogeneous dependence (about 0.13–0.17), revealing a container/port core (CCFI–YRCFI–MPCT), a bulk/energy spine (BDI–CPUS), and commodity bridges via GAUP. Dynamic PMFG metrics show a generally resilient but episodically vulnerable structure: density and compactness decline in turbulence. Stress tests demonstrate high redundancy to diffuse link failures (connectivity largely intact until ∼70–80% edge removal) but pronounced sensitivity of diffusion capacity to targeted multi-node outages. Early-warning indicators based on entropy rate and percolation threshold Z-scores flag recurring windows of elevated fragility; change point detection evaluation of both metrics isolates clustered regime shifts (2015–2016, 2018–2019, 2021–2022, and late 2023–2024). A Systemic Importance Index (SII) combining average centrality and removal impact ranks MPCT and CCFI as most critical, followed by BDI, with GAUP/CPUS mid-peripheral and ASMC peripheral. The findings imply that safeguarding port throughput and stabilizing container freight conditions deliver the greatest resilience gains, while monitoring bulk/energy linkages is essential when macro shocks synchronize across markets. Full article

To address the suboptimal leveling performance and insufficient slope stability of existing agricultural machinery chassis in hilly and mountainous regions, this study proposes an innovative omnidirectional leveling system based on a “double-layer frame” crawler-type agricultural chassis. The system employs servo-electric cylinders as its actuation components. A control model for the servo-electric cylinders has been established, accompanied by the design of an adaptive sliding mode controller (ASMC). A co-simulation platform was developed utilizing Matlab/Simulink and Adams to evaluate system performance. Comparative simulations were conducted between the ASMC and a conventional PID controller, followed by comprehensive machine testing. Experimental results demonstrate that the proposed double-layer frame crawler chassis achieves longitudinal leveling adjustments of up to 25° and lateral adjustments of 20°. Through structural optimization and the application of ASMC (in contrast to PID), both longitudinal and lateral leveling response times were reduced by 1.12 s and 0.95 s, respectively. Furthermore, leveling velocities increased by a factor of 1.5 in the longitudinal direction and by a factor of 1.3 in the lateral direction, while longitudinal and lateral angular accelerations decreased by 15.8% and 17.1%, respectively. Field tests confirm the system’s capability for adaptive leveling on inclined terrain, thereby validating the enhanced performance of the proposed omnidirectional leveling system. Full article

A key trade-off persists in the control of permanent magnet synchronous motors (PMSMs): achieving fast finite-time convergence often exacerbates control chattering, while conventional chattering-suppression methods can compromise the system’s dynamic response. The existing literature often addresses these challenges in isolation. The core original contribution of this research lies in proposing a novel robust fuzzy adaptive control scheme that effectively resolves this trade-off through a synergistic design. The contributions are as follows: (1) A novel reaching law is formulated to significantly accelerate error convergence, achieving finite-time stability and improving upon conventional reaching law designs. (2) A super-twisting sliding mode observer is integrated into the control loop, providing accurate real-time estimation of load torque disturbances, which is used for feedforward compensation to drastically improve the system’s disturbance rejection capability. (3) A fuzzy adaptive mechanism is developed to dynamically tune key gains in the sliding mode law. This approach effectively suppresses chattering without sacrificing response speed, enhancing system robustness. (4) The stability and convergence of the proposed controller are rigorously analyzed. Simulations, comparing the proposed method with conventional adaptive sliding mode control (ASMC), demonstrate its marked superiority in control accuracy, transient behavior, and disturbance rejection. This work provides an integrated solution that balances rapidity and smoothness for high-performance motor control, offering significant theoretical and engineering value. Full article

This study presents an adaptive integrated chassis control strategy for enhancing vehicle stability under different road conditions, specifically through the real-time estimation of tire cornering stiffness. A hierarchical control architecture is developed, combining active front steering (AFS) and direct yaw moment control (DYC). A recursive regularized weighted least squares algorithm is designed to estimate tire cornering stiffness from measurable vehicle states, eliminating the need for additional tire sensors. Leveraging this estimation, an adaptive sliding mode controller (ASMC) is proposed in the upper layer, where a novel self-tuning mechanism adjusts control parameters based on tire saturation levels and cornering stiffness variation trends. The lower-layer controller employs a weighted least squares allocation method to distribute control efforts while respecting physical and friction constraints. Co-simulations using MATLAB 2018a/Simulink and CarSim validate the effectiveness of the proposed framework under both high- and low-friction scenarios. Compared with conventional ASMC and DYC strategies, the proposed controller exhibits improved robustness, reduced sideslip, and enhanced trajectory tracking performance. The results demonstrate the significance of the real-time integration of tire dynamics into chassis control in improving vehicle handling and stability. Full article

It has long been recognised that airway smooth muscle cells (ASMCs) possess an abundance of M2 muscarinic receptors (M2Rs). However, the contribution of postjunctional M2Rs to contractions of airway smooth muscle (ASM) induced by the release of acetylcholine (ACh) from parasympathetic nerves was thought to be minimal. Instead, it was believed that these responses were exclusively mediated by activation of M3Rs. However, evidence is emerging that postjunctional M2Rs may have a greater role than previously realised. In this review, we discuss ACh signalling in airways, highlighting the well-established autoinhibitory role of prejunctional M2Rs and the putative roles of postjunctional M2Rs to cholinergic contractions of ASM. The cellular mechanisms that underpin M2R-dependent contractions of ASM are reviewed, with a particular emphasis on the role of ion channels in these responses. The regulation of M2R signalling pathways by β-adrenoceptor activation is also considered, along with the potential involvement of postjunctional M2Rs in airway diseases such as asthma and chronic obstructive pulmonary disease (COPD). Full article

This paper presents an adaptive sliding mode controller (ASMC) with the implication of a forgetting factor for a two-degree-of-freedom (2-DOF) arm robot manipulator trajectory tracking. The proposed approach builds upon conventional sliding mode control (SMC), which is well known for its robustness and low tracking error. The controller dynamically adjusts this parameter by introducing an adaptive mechanism to enhance trajectory tracking, guarantee high robustness, and reduce chattering effects. In order to mitigate gain drift, a forgetting factor is incorporated into the adaptation law, ensuring stable and reliable control performance. Stability is validated using Lyapunov theory, and the effectiveness of the proposed ASMC is evaluated through numerical simulations. The simulations are conducted in MATLAB R2023b using a dynamic model of the 2-DOF robotic manipulator. Comparative results with conventional SMC confirm that the adaptive approach significantly improves tracking accuracy, noise robustness, and chattering suppression. Full article

This study presents a framework for designing symmetry-aware cooperative controllers to synchronize two SCARA LS3-B401S robots, ensuring precision, adaptability, and fault tolerance in flexible manufacturing environments. Four control strategies—Proportional–Integral–Derivative (PID), Adaptive Sliding Mode Control (ASMC), Adaptation-Enabled Neural Network (ANN), and Inverse-Dynamics with Disturbance Observer (ID-DO)—were evaluated through high-fidelity MATLAB/Simulink simulations (fixed 1 ms step size, ode4 solver), using dynamic SolidWorks 2022 models validated under realistic perturbations, including ±0.0005 rad sensor noise and ±5% mass variation. Among the strategies, the ANN controller—implemented as an 8-10-4 multi-layer perceptron—achieved the highest performance, consistently reducing trajectory errors by over 99%, maintaining symmetry deviations below 0.001 rad, and recovering from ±0.08 rad disturbances in 0.12 s. Its stabilization time averaged 0.247 s across joints, and energy consumption dropped to 0.01 J/s, representing a 98% improvement over PID. Despite a higher computational load (12.5 MFLOPS, 2.80 ms per iteration), GPU acceleration brought execution times below 1.4 ms, ensuring compliance with industrial 5 ms control cycles. These results establish a scalable foundation for next-generation multi-robot systems, with planned physical validation on SCARA LS3-B401S robots equipped with high-resolution encoders and advanced processors. By leveraging symmetry-driven coordination ($S=I$), the proposed framework supports resilient, sustainable, and high-precision manufacturing, aligned with the goals of Industry 5.0. Full article

This paper presents an advanced Adaptive Sliding Mode Control (ASMC) strategy, specifically developed for a hydrogen production system based on a Proton Exchange Membrane electrolyzer (PEM electrolyzer). This work utilized a static model of the PEM electrolyzer, characterized by its V-I electrical characteristic, which was approximated by a linear equation. The ASMC was designed to estimate the coefficients of this equation, which are essential for designing an efficient controller. The primary objective of the proposed control strategy is to ensure the overall stability of the integrated system comprising both an interleaved buck converter (IBC) and PEM electrolyzer. The control framework aims to maintain the electrolyzer voltage at its reference value despite the unknown coefficients while ensuring equal current distribution among the three parallel legs of the IBC. The effectiveness of the proposed approach was demonstrated through numerical simulations in MATLAB-SIMULINK and was validated by the experimental results. The results showed that the proposed ASMC achieved a voltage tracking error of less than 2% and a current distribution imbalance of only 1.5%. Furthermore, the controller exhibited strong robustness to parameter variations, effectively handling fluctuations in the electrolyzer’s ohmic resistance (R_ohm) (from ±28.75% to ±40.35%) and in the reversible voltage (E_rev) (from ±28.67% to ±40.19%), highlighting its precision and reliability in real-world applications. Full article