Search Results (473)

The mathematical modeling and experimental validation of a non-conventional vertical-axis wind turbine (VAWT) with a variable-pitch angle are presented, employing the Double-Multiple Streamtube (DMST) method to simulate aerodynamic performance. The aerodynamic coefficients required by the model are obtained through a data-driven approach using a multi-input, two-output multilayer perceptron artificial neural network (MLP–ANN). The model is validated through numerical simulations under two distinct wind input profiles. An experimental evaluation with a prototype replicates the step input. It shows strong agreement with the simulations, particularly in the angular velocity response, which fluctuates between 35 and 55 RPM, with an average value in the range of 40–45 RPM. This hybrid methodology enhances the modeling fidelity of VAWTs and provides a scalable framework for real-time aerodynamic analysis and control. Full article

This study presents the design, control, and flight experiments of a motor-directly-driven flapping-wing micro air vehicle with extension springs (MDD-FWMAVES). The flapping wing actuation utilizes the resonance of a linear extension spring and a flapping wing. The analysis results of the proposed MDD-FWMAVES revealed a resonant frequency of 19.59 Hz for the flapping-wing mechanism, and actual flapping experiments confirmed this to be 20 Hz. Using a six-axis load cell, we demonstrated the ability to generate roll, pitch, and yaw moments for attitude control based on wing flapping variations. All roll, pitch, and yaw moments were linearly proportional to the wing flapping variations. MEMS gyroscopes and accelerometers were used to measure roll, pitch, and yaw angular velocities and the gravity. A complementary filter was applied to these measurements to obtain the roll and pitch angles required for attitude control. A microprocessor, two motor drive circuits, one MEMS gyroscope/accelerometer, and one EEPROM for flight data storage were implemented on a single, ultra-compact electronic control board and mounted on the MDD-FWMAVES. Simple roll and pitch PD controllers were implemented on this electronic control board, and the controlled flight feasibility of the MDD-FWMAVES was explored. Flight tests demonstrated stable hovering for approximately 6 s. While yaw control was not achieved, the onboard feedback control system demonstrated stable roll and pitch control. Therefore, the MDD-FWMAVES holds the potential to be developed into a high-performance flapping-wing micro air vehicle if its flight system and controller are improved. Full article

Knee osteoarthritis (OA) is a prevalent condition in older adults that often results in impaired gait and balance, increased risk of falls, and reduced quality of life. Conventional clinical assessments may not adequately capture these deficiencies. This study investigated the gait and balance of elderly individuals with knee OA using wearable inertial measurement units (IMUs). Forty-four participants with Kellgren–Lawrence grade 2–3 knee OA (71.23 ± 5.75 years) and forty-five age-matched controls (70.87 ± 4.30 years) completed dynamic balance (balance board), static balance (single-leg stance), ‘timed up and go’ (TUG), and normal walking tasks. Between 2 and 8 IMUs, depending on the task, were placed on the head, chest, waist, knees, ankles, soles, and balance board to record kinematic data. Balance was quantified using absolute angular velocity and linear acceleration, with group differences analyzed by MANOVA and Bonferroni-adjusted univariate tests. The participants with knee OA exhibited greater gait asymmetry, although the difference was not significant. However, they consistently demonstrated higher absolute angular velocities than controls across most body segments during static and dynamic tasks, indicating reduced postural stability. No group differences were observed in TUG performance. These findings suggest that IMU-based measures, particularly angular velocity, are sensitive to balance impairment detection in knee OA. Incorporating IMU technology into clinical assessments may facilitate early identification of instability and guide targeted interventions to reduce fall risk. Full article

In this study, a nonlinear dynamic model is developed to examine the stability and vibration behavior of a self-balancing electric Segway operating over irregular stochastic terrains. The Segway is treated as a three-degrees-of-freedom cart–inverted pendulum system, incorporating elastic and damping effects at the wheel–ground interface. Road irregularities are generated in accordance with international standard using high-order filtered noise, allowing for representation of surface classes from smooth to highly degraded. The governing equations, formulated via Lagrange’s method, are transformed into a Lorenz-like state-space form for nonlinear analysis. Numerical simulations employ the fourth-order Runge–Kutta scheme to compute translational and angular responses under varying speeds and terrain conditions. Frequency-domain analysis using Fast Fourier Transform (FFT) identifies resonant excitation bands linked to road spectral content, while Kernel Density Estimation (KDE) maps the probability distribution of displacement states to distinguish stable from variable regimes. The Lyapunov stability assessment and bifurcation analysis reveal critical velocity thresholds and parameter regions marking transitions from stable operation to chaotic motion. The study quantifies the influence of the gravity–damping ratio, mass–damping coupling, control torque ratio, and vertical excitation on dynamic stability. The results provide a methodology for designing stability control systems that ensure safe and comfortable Segway operation across diverse terrains. Full article

In automated orchard operations, the straight-line locomotion stability of ground-based weeding robots is critical for ensuring path coverage efficiency and operational reliability. To address the response lag and high-frequency oscillations often observed in conventional PID and fixed-lookahead Pure Pursuit controllers, this study proposes an adaptive lookahead Pure Pursuit method incorporating angular velocity feedback. By dynamically adjusting the lookahead distance according to real-time attitude changes, the method enhances coordination between path curvature and robot stability. To enable systematic evaluation, three time-series-based metrics are introduced: mean absolute yaw error (MAYE), peak-to-peak fluctuation amplitude, and the standard deviation of angular velocity, with overshoot occurrences included as an additional indicator. Field experiments demonstrate that the proposed method outperforms baseline algorithms, achieving lower yaw errors (0.61–0.66°), reduced maximum deviation (≤3.7°), and smaller steady-state variance (<0.44°²), thereby suppressing high-frequency jitter and improving turning convergence. Under typical working conditions, the method achieved a mean yaw deviation of 0.6602°, a fluctuation of 5.59°, an angular velocity standard deviation of 10.79°/s, and 155 overshoot instances. The yaw angle remained concentrated around the target orientation, while angular velocity responses stayed stable without loss-of-control events, indicating a favorable balance between responsiveness and smoothness. Overall, the study validates the robustness and adaptability of the proposed strategy in complex orchard scenarios and establishes a reusable evaluation framework, offering theoretical insights and practical guidance for intelligent agricultural machinery optimization. Full article

Background: This study investigated sex differences in foot arch structure and function, and their impact on postural control and energy flow during dynamic tasks. Findings aim to inform sex-specific training, movement assessment, and injury prevention strategies. Methods: A total of 108 participants (53 males and 55 females) underwent foot arch morphological assessments and performed a sit-to-stand (STS). Motion data were collected using an infrared motion capture system, three-dimensional force plates, and wireless surface electromyography. A rigid body model was constructed in Visual3D, and joint forces, segmental angular and linear velocities, center of pressure (COP), and center of mass (COM) were calculated using MATLAB. Segmental net energy was integrated to determine energy flow across different phases of the STS. Results: Arch stiffness was significantly higher in males. In terms of postural control, males exhibited significantly lower mediolateral COP frequency and anteroposterior COM peak velocity during the pre-seat-off phase, and lower COM displacement, peak velocity, and sample entropy during the post-seat-off phase compared to females. Conversely, males showed higher anteroposterior COM velocity before seat-off, and greater anteroposterior and vertical momentum after seat-off (p < 0.05). Regarding energy flow, males exhibited higher thigh muscle power, segmental net power during both phases, and greater shank joint power before seat-off. In contrast, females showed higher thigh joint power before seat-off and greater shank joint power after seat-off (p < 0.05). Conclusions: Significant sex differences in foot arch function influence postural control and energy transfer during STS. Compared to males, females rely on more frequent postural adjustments to compensate for lower arch stiffness, which may increase mechanical loading on the knee and ankle and elevate injury risk. Full article

The VR-based circular tracking movement evaluation system (CES) was developed to quantitatively assess visuomotor control. The virtual stick, a component of the CES, provides visual cues in the virtual environment and haptic feedback when holding the controller. This study examined the effects of stick presence and presentation order on control accuracy for distance, angle, and angular velocity. Twenty-seven participants (12 females; mean age 23.3 ± 2.3 years) performed tasks in the frontal plane followed by the sagittal plane. In each plane, the stick was visible for the first 1–3 revolutions and invisible for the subsequent 4–6 revolutions in the invisible condition, with the reverse order in the visible condition. In the invisible condition, control accuracy with the stick was 1.10 times higher for distance only in the sagittal plane, and 1.13 and 1.09 times higher for angle and angular velocity in the frontal plane, and 1.11 and 1.08 times higher in the sagittal plane. No significant differences were observed in the visible condition. The improved control accuracy when the stick was visible is likely due to enhanced precision in constructing the reference frame, internal models, body coordinates, and effective multisensory integration of visual and haptic information. Such visual information may enable fine control in virtual environment-based applications, including games and surgical simulations. Full article

The rapid evolution of virtual reality systems and the broader metaverse landscape has prompted growing research interest in biometric authentication methods for user verification. These solutions offer an additional layer of access control that surpasses traditional password-based approaches by leveraging unique physiological or behavioral traits. Current literature emphasizes analyzing controller position and orientation data, which presents challenges when using convolutional neural networks (CNNs) with non-continuous Euler angles. The novelty of the presented approach is that it addresses this limitation. We propose a modality transformation approach that generates acceleration and angular velocity signals from trajectory and orientation data. Specifically, our work employs algebraic techniques—including quaternion algebra—to model these dynamic signals. Both the original and transformed data were then used to train various CNN architectures, including Vanilla CNNs, attention-enhanced CNNs, and Multi-Input CNNs. The proposed modification yielded significant performance improvements across all datasets. Specifically, F1-score accuracy increased from 0.80 to 0.82 for the Comos subset, from 0.77 to 0.82 for the Quest subset, and notably from 0.83 to 0.92 for the Vive subset. Full article

Modeling mobile robots is crucial to odometry estimation, control design, and navigation. Classical state-space models (SSMs) have traditionally been used for system identification, while recent advances in deep learning, such as Long Short-Term Memory (LSTM) networks, capture complex nonlinear dependencies. However, few direct comparisons exist between these paradigms. This paper compares two multivariate modeling approaches for a differential drive robot: a classical SSM and an LSTM-based recurrent neural network. Both models predict the robot’s linear (v) and angular ($\omega$) velocities using experimental data from a five-minute navigation sequence. Performance is evaluated in terms of prediction accuracy, odometry estimation, and computational efficiency, with ground-truth odometry obtained via a SLAM-based method in ROS2. Each model was tuned for fair comparison: order selection for the SSM and hyperparameter search for the LSTM. Results show that the best SSM is a second-order model, while the LSTM used seven layers, 30 neurons, and 20-sample sliding windows. The LSTM achieved a FIT of 93.10% for v and 90.95% for $\omega$, with an odometry RMSE of 1.09 m and 0.23 rad, whereas the SSM outperformed it with FIT values of 94.70% and 91.71% and lower RMSE (0.85 m, 0.17 rad). The SSM was also more resource-efficient (0.00257 ms and 1.03 bytes per step) compared to the LSTM (0.0342 ms and 20.49 bytes). The results suggest that SSMs remain a strong option for accurate odometry with low computational demand while encouraging the exploration of hybrid models to improve robustness in complex environments. At the same time, LSTM models demonstrated flexibility through hyperparameter tuning, highlighting their potential for further accuracy improvements with refined configurations. Full article

This study addresses robust trajectory tracking for quadrotor unmanned aerial vehicles (QUAVs) under partial state measurements and bounded external disturbances. To this end, a control framework is introduced that integrates backstepping sliding mode control (BSMC) with a super-twisting observer (STO). In this scheme, only position and attitude are directly measured while the STO reconstructs the linear and angular velocities in real time. The estimated states are then fed into the control law, enabling accurate trajectory tracking and robust performance without full-state feedback or explicit disturbance compensation. The approach is validated through three simulation scenarios: nominal full-state feedback, observer-based control without disturbances, and observer-based control under bounded time-varying perturbations. Quantitative metrics confirm consistent tracking accuracy and closed-loop stability across all scenarios. These results demonstrate the effectiveness of the integrated BSMC–STO framework for QUAV operations in sensor-limited and disturbance-prone environments. Full article

To achieve precise trajectory tracking and lateral-roll stability during the coordinated control of high-speed autonomous vehicles under lane-changing conditions, this paper proposes an integrated control strategy based on state estimation with a high-order sliding mode and a double-power sliding mode. Firstly, establish a three-degrees-of-freedom vehicle dynamics model and trajectory-tracking error model that includes yaw lateral-roll coupling, and use an extended Kalman filter to estimate real-time unmeasurable states such as the center of mass roll angle, roll angle, and angular velocity. Then, for the trajectory-tracking subsystem, a high-order sliding-mode controller is designed. By introducing a virtual control variable and an arbitrary-order robust differentiator, the switching signal is implicitly integrated into the derivative of the control variable, significantly reducing chattering and ensuring finite-time convergence. Furthermore, in the lateral stability loop, a double-power convergence law sliding-mode controller is constructed to dynamically allocate yaw moment and roll moment with estimated state as feedback, achieving the decoupling optimization of stability and tracking performance. The joint simulation results show that the proposed strategy significantly outperforms traditional sliding-mode schemes in terms of lateral deviation, heading deviation, and key state oscillations under typical high-speed lane-changing conditions. This can provide theoretical basis and engineering reference for integrated control of autonomous vehicles under high dynamic limit conditions. Full article

To address heading instability in electric vertical take-off and landing (eVTOL) aircraft at low speeds and large pitch angles, a rotational speed feedback compensation control scheme based on Active Disturbance Rejection Control (ADRC) is proposed for variable-pitch propellers. This scheme integrates propeller speed into the heading control inner loop and employs a state observer to process the measured speed. Simulation results demonstrate that under dynamic propeller speed variations of 0.5%, 1%, and 2%, the proposed compensation scheme reduces yaw angle oscillation amplitudes by 22.2%, 30.6%, and 37.8%, and yaw angular velocity fluctuations by 32.5%, 43.4%, and 33.3%, respectively, compared to a basic speed feedback scheme, showcasing significantly superior robustness. Experimental bench tests further validate that the proposed strategy enhances overall propeller force efficiency from 2.479 kg/kW to 3.05 kg/kW at 120 km/h cruise, resulting in a power saving of 0.48 kW and extending the cruising range by 8.5 km. The stability and energy efficiency of the proposed method are rigorously validated through both simulation and experimental testing. 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

Archery performance is substantially influenced by postural stability. Although archery is commonly practiced outdoors, most studies have focused on short-distance indoor environments. Accordingly, this study examined the correlation between postural stability and shooting accuracy in competitive recurve and compound archers on a standard outdoor field (70 m for recurve and 50 m for compound). This study included 37 archers. Each archer’s performance was recorded during a simulated competition. Measurements included muscle strength, body stability, and center of pressure. Postural stability data were analyzed at 0.5 s before and 0.1 s after arrow release. The results indicated that compared with compound archers, recurve archers had stronger upper-limb muscles and exhibited lower pre-release total center of pressure (51.9 mm; p = 0.022) and medial/lateral sway (1.1 mm; p = 0.043). The compound archers exhibited lower post-release anterior/posterior sway (3.2 mm; p = 0.001) and lower angular velocities in most body segments, except for the lower back. The recurve archers relied more on post-release stability, whereas the compound archers relied more on pre-release control. Linear regression analysis identified different predictors of scoring accuracy for each bow type. Our findings highlight the need for discipline-specific training strategies, such as enhancing bow-side stability for recurve archers and drawing-side control for compound archers. Full article

Autonomous docking is a critical capability for enabling fully automated operations in industrial and logistics environments using Autonomous Mobile Robots (AMRs). Traditional rule-based docking approaches often struggle with generalization and robustness in complex, dynamic scenarios. This paper presents a deep reinforcement learning-based autonomous docking framework that integrates Proximal Policy Optimization (PPO) with multi-sensor fusion. It includes YOLO-based vision detection, depth estimation, and LiDAR-based orientation correction. A concise 4D state vector, comprising relative position and angle indicators, is used to guide a continuous control policy. The outputs are linear and angular velocity commands for smooth and accurate docking. The training is conducted in a Gym-compatible Gazebo simulation, acting as a digital twin of the real-world system, and incorporates realistic variations in lighting, obstacle placement, and marker visibility. A designed reward function encourages alignment accuracy, progress, and safety. The final policy is deployed on a real robot via a sim-to-real transfer pipeline, supported by a ROS-based transfer node. Experimental results demonstrate that the proposed method achieves robust and precise docking behavior under diverse real-world conditions, validating the effectiveness of PPO-based learning and sensor fusion for practical autonomous docking applications. Full article