Search Results (1,176)

This paper presents a distributed hierarchical model predictive control (MPC) framework designed to ensure dynamic consensus and stability in nonlinear vehicle platoons, addressing challenges posed by mixed communication topologies and hard constraints. By directed graph modeling of the mixed communication topologies, the dynamic consensus goal for the platoon is defined by the inter-vehicle distances between the host and its neighbors, whereas the stability criterion for an individual vehicle is expressed as a positive definite function of its position and velocity deviations. Then, a contractive constraint is elegantly designed to correlate these two objectives in a hierarchical model predictive control framework, where the lower layer optimizes the stability objective and the upper layer optimizes the dynamic consensus objective. The conditions ensuring stability and string stability for the vehicle platoon are shown to be only dependent on the deviations of the host vehicle, which achieves dynamic consensus and string stability simultaneously for nonlinear vehicle platoons. Several representative scenarios are used to validated the performance of the proposed strategy. Full article

Reliable trajectory estimation and precise motion control are the prerequisites for underwater robotic systems to perform complex autonomous tasks, which are essential for enhancing the operational efficiency of intelligent underwater facilities. However, the inherent asymmetry of underwater hydrodynamics, featureless images caused by complex environments, and the lack of high-frequency state feedback significantly hinder stable trajectory tracking and robust autonomous navigation. To address these challenges, this paper proposes an integrated autonomous navigation and robust control scheme for underwater robotic systems. Specifically, we first propose a visual–inertial trajectory estimation method for underwater robotic systems, which effectively overcomes the challenges of featureless images and provides consistent, real-time pose feedback for motion execution. Furthermore, we develop a hierarchical robust motion control strategy for autonomous underwater robots, which integrates model predictive control with incremental nonlinear dynamic inversion to achieve precise positioning performance and reliable operation under environmental disturbances. Finally, we design and implement a customized, highly integrated underwater robotic platform that integrates the proposed trajectory estimation and robust control modules, with its performance validated through extensive field experiments in underwater scenarios. The experimental results demonstrate that the proposed system can effectively achieve high-precision trajectory tracking and maintain operational stability, providing a comprehensive engineering solution for the autonomous navigation of underwater robots in complex environments. Full article

Microinjection is one of the most established and effective techniques for introducing foreign substances into cells. However, issues such as cumbersome procedures, low success rates, and poor repeatability in manual cell microinjection have seriously restricted its practical applications in biomedical research and engineering. Responding to such problems, this paper designs an automated microinjection system that combines deep learning visual positioning and adaptive neural network sliding-mode motion control. The machine vision solution based on the deep learning YOLOv8 target detection algorithm is utilized by the system to provide positional prerequisites for automated microinjection. Then, stable and fast puncture is completed by controlling the end effector (composed of a piezoelectric actuator and a displacement amplification mechanism). Since the piezoelectric actuator has strong nonlinearity, the motion control of the end effector adopts the control strategy combining sliding mode variable structure and adaptive neural networks to meet the requirements of precise displacement output of microinjection. At the same time, a host computer control system is developed to integrate hardware equipment, visual positioning algorithms and motion control algorithms to achieve corresponding automated microinjection tasks. Finally, the effectiveness of the designed automated microinjection system is successfully verified on zebrafish embryos. Full article

Background: Human lead exposure is a multi-pathway phenomenon that integrates internal biological burden with persistent residential environmental reservoirs. Although individual lead metrics have been linked to cardiometabolic dysfunction, current research often fails to capture the ‘exposome’ reality of joint, nonlinear, and interaction-dependent effects on metabolic outcomes like BMI. Objectives: To evaluate associations between biological (blood and urinary) and residential dust (window and floor) lead measures and BMI, and to characterize nonlinear and interaction-dependent mixture effects using Bayesian Kernel Machine Regression (BKMR). Methods: We analyzed data from NHANES 2001–2002, a nationally representative survey of the U.S. noninstitutionalized civilian population. Window and floor dust lead (µg/ft²) were obtained from the NHANES household dust component, and blood lead (µg/dL) and urinary lead (µg/L) were measured using standardized NHANES laboratory protocols. BMI was calculated from measured height and weight. Missing data were addressed using multivariate imputation by chained equations. Descriptive statistics and multivariable linear regression were used to estimate adjusted associations between individual lead metrics and BMI, controlling for age, gender, income, race/ethnicity, and education. BKMR was then applied to evaluate joint mixture effects, estimate univariate and bivariate exposure–response functions, and quantify relative exposure importance using posterior inclusion probabilities (PIPs). Results: In covariate-adjusted linear regression, blood lead (β = −0.485; 95% CI: −0.566, −0.405; p < 0.001) and window dust lead (β = −0.00047; 95% CI: −0.00067, −0.00026; p < 0.001) were inversely associated with BMI, whereas floor dust lead was positively associated (β = 0.258; 95% CI: 0.209, 0.306; p < 0.001). Urinary lead was inversely but not significantly associated with BMI (β = −0.111; 95% CI: −0.235, 0.013; p = 0.079). In BKMR, blood lead was the dominant contributor, with a posterior inclusion probability (PIP; proportion of iterations in which an exposure is selected) of 1.00. Window dust lead showed modest inclusion (PIP = 0.26), whereas urinary and floor dust lead were not selected (PIP = 0.00). Exposure–response functions indicated modest nonlinearity for blood lead and greater divergence for the blood lead–window dust lead pairing at higher exposure levels. The overall mixture effect declined across increasing joint exposure quantiles, crossing the null near the median and becoming increasingly negative at higher mixture levels. Conclusions: In our study, lead metrics showed heterogeneous associations with BMI, and BKMR indicated that internal lead burden (blood lead) primarily drove mixture-related BMI patterns, with evidence that window dust lead may modify mixture effects at higher co-exposure levels. These findings support evaluating multiple lead exposure pathways jointly and using flexible mixture models to capture nonlinear and interaction-dependent relationships with BMI. Full article

The mechanical properties and in situ stress conditions of coal reservoirs critically control the effectiveness of hydraulic fracturing, yet the continuous acquisition of relevant parameters at the well scale is often limited by logging data availability and quality. To address this, an integrated workflow combining machine learning-based parameter inversion with a fracturing suitability evaluation framework was proposed for coalbed methane (CBM) reservoirs. A supervised neural network model was developed to establish nonlinear relationships between conventional logs and key parameters, including Young’s modulus, Poisson’s ratio, and horizontal principal stresses. Based on these inverted parameters, a dimensionless Fracturing Index (FI) was constructed to comprehensively characterize coal fracturability by integrating brittleness, fracture toughness, and stress conditions, with a density-based constraint introduced to ensure mechanical consistency. Point-scale FI values within coal seams were upscaled to the well scale for inter-well comparison and regional evaluation. Results showed that FI varied relatively little within individual wells but markedly between wells, reflecting systematic inter-well variations in mechanical and stress conditions, consistent with spatial patterns revealed by cross-well profiles. Correlation analysis from over ten wells with both FI and treatment data demonstrated positive relationships between FI and breakdown pressure, injected fluid volume, and proppant volume, confirming its engineering relevance. Consequently, a four-level FI-based classification scheme was established to identify favorable zones across the study area. This FI framework provides a practical, interpretable tool for early-stage CBM development, offering quantitative guidance for well prioritization, stimulation design, and regional planning in unfractured areas. Full article

This paper focuses on the dynamics modeling and control methods for an underactuated Dual-Unmanned Aerial Helicopter Slung Load System (DUH-SLS), which consists of two Unmanned Aerial Helicopters (UAHs) connected to the suspended load via two sling cables. The DUH-SLS is a multi-body coupled system with internal ideal constraint forces and has seven motion degrees of freedom (DOFs) in the longitudinal plane. In this paper, a set of independent and complete generalized coordinates is selected to describe the system’s motion. The dynamics model of DUH-SLS is established using Lagrange analytical mechanics. This approach, which avoids system internal forces, greatly improves modeling efficiency. Finally, the correctness of this dynamics model is validated using a virtual prototype of the DUH-SLS developed in the multi-body dynamics simulation software ADAMS. The DUH-SLS is a complex nonlinear controlled object, and the iterative Linear Quadratic Regulator (iLQR) method is introduced to design an integrated optimal controller to achieve trajectory tracking and swing suppression for the DUH-SLS. This method transforms the quadratic optimal control problem of nonlinear systems into a series of linear quadratic optimal control (LQR) problems through iterative optimization in function space, thus obtaining an optimal solution. The iLQR optimal controller requires offline iterative computation, but the optimal control obtained has a state feedback closed-loop form, which ensures robustness during online control. Numerical simulation results demonstrate that the proposed iLQR optimal controller exhibits excellent control performance in complex multi-task scenarios. Particularly in trajectory tracking tasks, the maximum average position tracking error of the iLQR controller is only 0.14 m, compared to 3.57 m and 3.11 m for the LQR and LMC (Lyapunov Method Controller) controllers, respectively. Furthermore, the controller demonstrates strong robustness against internal parameter perturbations and external complex wind disturbances, fully validating the effectiveness and superiority of the proposed approach. Full article

This paper presents a finite-time uniformly ultimately bounded (FTUUB) cooperative control strategy based on a nonlinear disturbance observer (NDOB) for high-precision collaborative control of multi-hydraulic robotic arm systems operating under unknown disturbances and model uncertainties in confined scenarios such as coal silo cleaning. The proposed approach simplifies control design by lumping various uncertainties into a total disturbance, which is estimated and compensated in real time by the NDOB. Building upon this, a finite-time convergent sliding mode controller is developed, wherein the disturbance compensation is inherently embedded, ensuring that both position and velocity tracking errors converge to a small neighborhood of zero within a finite time. A master–slave distributed control architecture is adopted, with the agent communication topology characterized by graph theory. To mitigate the chattering inherent in traditional sliding mode control, a smooth hyperbolic tangent function is employed to construct the sliding surface. Rigorous Lyapunov stability analysis demonstrates that the closed-loop system achieves uniform ultimate boundedness within a finite time. Comprehensive simulation experiments, including a digital twin-based visualization in a virtual coal silo environment, validate the superior performance of the proposed method in terms of tracking accuracy, convergence speed, disturbance rejection, and control smoothness. Full article

Hydraulic manipulators face critical challenges due to valve dead-band nonlinearity and state constraints, which can lead to safety hazards and hardware damage. This study proposes a state-constrained controller with valve dead-band compensation to ensure prescribed positioning accuracy and operational safety. Barrier Lyapunov functions ensure that state constraints are maintained and that boundary violations are avoided. Concurrently, a smooth dead-band inverse model is developed to offset asymmetric valve dead-band effects without inducing chatter. Adaptive laws estimate uncertain parameters and dead-band impact in real time, and a disturbance observer attenuates unmatched uncertainties. Dynamic surface control is employed to diminish the explosion of complexity in backstepping design. Comparative simulations under fixed-angle and arbitrary-angle tracking demonstrate that the proposed controller achieves superior tracking accuracy with steady-state errors below 0.04° compared to 0.06° for non-compensated controllers, while significantly reducing pressure fluctuations and control chattering as adaptive parameters converge. The results indicate that the strategy effectively compensates for valve dead zones while strictly maintaining state constraints, thereby achieving the required control precision for hydraulic servo systems. Full article

Background and Objectives: Fertility preservation in young patients with hematological malignancies is often constrained by the need for urgent life-saving chemotherapy, leaving limited evidence to guide counseling once treatment has already begun. Reliable predictors of ovarian response after chemotherapy are therefore clinically important. Materials and Methods: This retrospective single-center study included 37 hematological patients aged ≤35 years who underwent urgent controlled ovarian stimulation after initial chemotherapy. Only the first cycle per patient was analyzed. Patients were grouped by metaphase II oocyte yield as high-yield group (≥8 metaphase II oocytes) or low-yield group (<8). Post-chemotherapy ovarian reserve markers and chemotherapy-related variables were assessed. Parsimoniously adjusted logistic regression and ROC analyses were performed, and LOESS regression was used to explore relationships with mature oocyte number. Results: The median number of chemotherapy cycles before stimulation was three (IQR: 2–4), and the median interval from last chemotherapy to retrieval was 33 days (IQR: 27–39). The high-yield group had higher post-chemotherapy anti-Müllerian hormone (AMH) and antral follicle count (AFC) than the low-yield group (both p < 0.05). In adjusted analyses, AMH (OR 2.58, 95% CI 1.17–5.70) and AFC (OR 1.24, 95% CI 1.04–1.48) were associated with achieving ≥8 mature oocytes. No association was detected between oocyte yield and chemotherapy cycle number, chemotherapy-free interval, alkylating agent exposure, or stimulation-related factors. LOESS showed positive, non-linear associations for AMH and AFC with mature oocyte number. In this exploratory analysis, ROC curves suggested moderate discrimination for predicting high oocyte yield, with areas under the curve of 0.78 for AMH, 0.73 for AFC, and 0.80 for the combined model. Conclusions: Post-chemotherapy AMH and AFC were associated with ovarian response in urgent fertility preservation after initial chemotherapy for young hematological malignancies. Larger studies are needed to validate these exploratory findings. Full article

This article addresses the challenges of regulation and trajectory tracking in a nonlinear, multivariable drone quadrotor system using a discrete-time Generalized Proportional Integral (GPI) controller, which is the discrete-time version of its continuous-time counterpart. The discrete-time formulation offers several advantages, including simplified trajectory planning by eliminating time derivatives, reduced computational demands, and lower complexity in nominal feed-forward input functions. The proposed GPI controller ensures asymptotic exponential stability for both attitude and position, enabling effective trajectory tracking. Its effectiveness has been validated through numerical simulations, which demonstrate excellent stabilization and tracking performance even in the presence of atmospheric disturbances and measurement noise. Full article

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

High-performance operation of permanent magnet synchronous motors (PMSMs) strongly depends on the reliable availability of rotor position and speed information. Although this information is commonly obtained using physical position sensors, such sensors increase system cost and structural complexity and may reduce long-term reliability, particularly in demanding operating environments. In this study, a model-based, discrete-time, nonlinear gradient observer is adapted for the sensorless estimation of rotor speed and position in PMSMs. The developed Runge–Kutta model-based gradient observer (RKGO) utilizes stator voltage inputs and measured stator currents within a mathematical motor model to estimate the system states. In contrast to conventional sensorless estimation approaches, the adopted observer framework exploits discretization-based gradient dynamics to enhance numerical robustness and convergence behavior under nonlinear operating conditions. The observer design specifically targets stable and accurate state estimation in discrete-time implementations, with a particular focus on low-speed operating conditions. The performance of the adapted method is experimentally evaluated under low-speed operating conditions, including transient and steady-state operation. Real-time implementation is carried out on a dSPACE DS1104 control platform, including loaded acceleration scenarios to assess practical robustness. In addition, a comparative analysis with the Extended Kalman Filter (EKF) and the Runge–Kutta Extended Kalman Filter (RKEKF) is conducted at 60 rad/s under identical experimental conditions. Experimental results show that the RKGO method achieves accurate steady-state speed and position estimation with acceptable transient performance. The findings demonstrate that RKGO can be considered a viable alternative for low-speed sensorless PMSM drive applications. Full article

Postural control relies on the integration of visual, vestibular, and somatosensory inputs under biomechanical constraints. Conventional Romberg testing provides limited quantitative insight, particularly regarding directional control and sensory dependence. Wearable inertial measurement units (IMUs) enable portable, multidimensional assessment of postural sway. Thirty healthy adults (15 females, 15 males) completed a modified Romberg protocol with systematic manipulation of stance (normal, tandem), visual condition (eyes open, eyes closed), and arm position (arms at sides, arms forward), including both left and right leading foot during tandem stance. Whole-body kinematics were recorded using a full-body IMU system comprising 17 wireless sensors. Center-of-mass (CoM) trajectories were derived from a 23-segment biomechanical model, and linear, spatial, and nonlinear sway metrics were computed. Statistical analyses were conducted using repeated-measures ANOVA, with significance set at p < 0.05. Visual deprivation significantly increased sway path length, mean sway velocity, and sway area across all stance conditions (p < 0.001). Tandem stance elicited greater mediolateral sway than normal stance (p < 0.001). Romberg ratios exceeded unity for all metrics and were significantly higher in tandem stance (p < 0.01). Arm position effects were negligible in normal stance but showed significant Vision × Arm interactions during tandem stance (p < 0.05). Leading foot position had no significant main effects. Combining a modified Romberg protocol with full-body IMU-based CoM analysis enables sensitive characterization of sensory dependence and directional postural control. Tandem stance with visual deprivation increases mediolateral postural demands under reduced base-of-support conditions, providing a more challenging context for evaluating directional postural control. Full article

Parabolic trough collectors (PTCs) are highly sensitive to receiver positioning accuracy; however, most existing studies report optical efficiency degradation without formally defining alignment tolerance limits. This study proposes a tolerance-based thermo-optical risk framework to quantify allowable receiver misalignment envelopes for reliable PTC operation. A Monte Carlo Ray Tracing (MCRT) methodology is employed to evaluate the impact of angular receiver misalignment on optical efficiency and circumferential heat flux redistribution. Beyond conventional efficiency metrics, normalized flux-based thermal non-uniformity indicators are introduced to assess thermo-mechanical risk without requiring full thermo-fluid modeling. The results reveal a nonlinear decoupling between optical acceptability and thermal safety. While optical efficiency remains above 0.80 up to approximately ±6°, pronounced flux localization and rapid growth of thermal stress indicators occur beyond ±4°, marking the onset of thermally critical behavior. The identified ±4° threshold corresponds to approximately twice the collector half-acceptance angle (θ_(crit)/δ ≈ 2), demonstrating geometry-dependent scaling characteristics. The proposed framework formalizes the optical–thermal decoupling phenomenon and transforms conventional efficiency-based evaluation into a reliability-informed alignment tolerance assessment tool applicable to manufacturing precision, installation control, and operational quality management in CSP systems. Full article

Wind erosion is a multidimensional, dynamic process driven by natural and anthropogenic factors, but existing statistical methods struggle to capture its complex nonlinear relationships, resulting in incomplete quantification of drivers and their spatial variability. To address this, we integrate the Revised Wind Erosion Equation (RWEQ)model with explainable artificial intelligence to disentangle the spatiotemporal positive and negative effects of dominant drivers and their synergistic interactions in Inner Mongolia. Results show that, from 2000–2022, wind erosion has been decreasing on average by 1.1 t·ha⁻¹·yr⁻¹, mainly in the western deserts and locally in Hulunbuir sandy land. Severe erosion is mostly due to nature (78.7%) rather than anthropogenic (21.3%). Normalized difference vegetation index (NDVI), clay content (CL), windy days (WD), precipitation (PRE), temperature (TEM), and sand content (SA) were found to be the most important drivers of wind erosion. Critical threshold conditions for severe wind erosion are NDVI < 0.14, CL < 12%, GD > 26, PRE < 73.15 mm, and SA > 66%. When there is a certain combination of variables, wind erosion risk is greatly increased, which mainly happens in the western part of Alxa, Bayannur, and the area near the desert edge. Wind erosion control should shift toward region-specific precision management, including engineering protection, optimized grazing management, and vegetation restoration. Full article