Search Results (3,722)

In robot grinding tasks, dual manipulators possess improved flexibility, which can cooperate to complete different tasks with higher efficiency and satisfactory effect. In collaborative robot grinding tasks, the critical issues lie in the motion trajectory planning of the two manipulators and trajectory tracking with satisfactory accuracy under the condition that the two manipulator ends apply force on each other. In order to accomplish the goals in a more concise and feasible way, a complete scheme for dual-arm robot grinding tasks is essential. To address this issue, taking the motion trajectory planning and impedance control into consideration, a novel scheme for dual manipulators to complete collaborative grinding tasks is presented in this paper. To this end, a dual-arm grinding system is first constructed, and the kinematic constraints in the cooperative motion are analyzed, based on which the motion trajectories of the dual manipulators are planned according to the grinding task objectives. Then, an impedance controller is designed to achieve accurate tracking of the motion trajectory in the grinding process. Finally, dual-arm collaborative simulations and grinding experiments are carried out, and the results show that the proposed method can achieve good motion results and better flexibility compared to the single-arm motion, which demonstrates the effectiveness of the proposed method. Full article

In order to contribute to the operation of unmanned aerial vehicles (UAVs) according to visual flight rules (VFR), this article proposes a monocular approach for cloud detection using an electro-optical sensor. Cloud avoidance is motivated by several factors, including improving visibility for collision prevention and reducing the risks of icing and turbulence. The described workflow is based on parallelized detection, tracking and triangulation of features with prior segmentation of clouds in the image. As output, the system generates a cloud occupancy grid of the aircraft’s vicinity, which can be used for cloud avoidance calculations afterwards. The proposed methodology was tested in simulation and flight experiments. With the aim of developing cloud segmentation methods, datasets were created, one of which was made publicly available and features 5488 labeled, augmented cloud images from a real flight experiment. The trained segmentation models based on the YOLOv8 framework are able to separate clouds from the background even under challenging environmental conditions. For a performance analysis of the subsequent cloud position estimation stage, calculated and actual cloud positions are compared and feature evaluation metrics are applied. The investigations demonstrate the functionality of the approach, even if challenges become apparent under real flight conditions. Full article

There is a growing focus on the design of nanoemulsions because of their valuable properties as an enhanced vehicle for interaction with cells and resistant bacteria. Their potential applications in the health and food industry are numerous. Although they are considered unstable because of flocculation and coalescence, they are still efficient resources for antibacterial inhibition due to their droplet size. Studies on the interactions between essential oils and an aqueous medium are increasing, in order to efficiently formulate them at the nanometric scale using surfactants, thereby providing them with long-lived droplet size stability. This study used the ultrasonication method for fabrication and Eumulgin as a surfactant to achieve nanometric droplet sizes using two noble essential oils, palmarosa and tea tree. A follow-up for one year tracked a stable droplet size and sustained polydispersity in those emulsions as the most valuable outcome. Moreover, the insights of a thermoresponsive study have been included, also showing a strong stability. The antibacterial properties of the essential oils considered became enhanced, at a comparable scale of an antibiotic, on Salmonella spp. and Bacillus subtilis depending on the nanoscale droplet size. The outcomes suggest the importance of deepening parametric studies of these nanoformulations in terms of concentrations and temperature changes, characterizing their remarkable properties and durability. Full article

Niche can reflect the changes in the quality of the ecological environment and the balance of ecological state. The more advanced the ecosystem, the more complex and higher-order nonlinearities and uncertainties that are presented. For such an uncertain parameter system with complex nonlinearity, backstepping fuzzy control is a good control method. When the backstepping control method is introduced into the Type-2 fuzzy T-S control principle, the equality index symmetry function composed of ecological factors is used as the backstepping control consequence, and the Lyapunov function is constructed to analyze the stability and find out the adaptive law of the ecological factors in the equality index symmetry function of the control consequence. This reflects that the individual organisms always develop in their own favorable direction, highlighting the bionic intelligent control of the method. Through simulation analysis, the Type-2 Backstepping control method is effective in stability and parameter tracking, which reflects the self-development ability and self-coordination ability of individual organisms, highlighting the physical background and symmetry of the bionic intelligent control of this method. Full article

Multi-object tracking (MOT) aims to detect objects in video sequences and associate them across frames. Currently, the mainstream research direction regarding MOT is the tracking-by-detection (TBD) framework. Tracking results are highly sensitive to detection outputs, and challenges from object occlusion and complex motion present significant obstacles in the field of MOT. To reduce dependence on detection outputs, we propose a method that integrates predictive information to improve Non-Maximum Suppression (NMS). By applying secondary modulation to the suppression scores and dynamically adjusting the suppression threshold using tracking information, our method better retains candidate boxes for occluded objects. Furthermore, to track occluding and overlapping objects more effectively, we introduce an adaptive measurement noise method that adjusts the measurement noise to mitigate the impact of object occlusion or overlap on tracking accuracy. Additionally, we enhance the affinity matrix in the association algorithm by incorporating height information, thereby improving the stability of complex moving objects. Our method outperforms the baseline model ByteTrack on the DanceTrack dataset, increasing Higher Order Tracking Accuracy (HOTA), Multi-Object Tracking Accuracy (MOTA), and the ID F1 Score (IDF1) by 10.2%, 3.0%, and 4.8%, respectively. Full article

This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system. Full article

Objectives: Muscles and their tendons present a considerable diversity of morphological variations. The aim of this study was to explore variants of muscles and tendons from compartments of the thigh and to raise awareness about potential problems during ultrasound examination. Materials and Methods: This comprehensive review of the literature was created on the basis of scientific articles sourced from PubMed. The search included all relevant papers related to the topic, ensuring that the most up-to-date studies were incorporated. In order to achieve these results, we created the exclusion criteria and extracted papers that did not meet the requirements of our review. Relevant papers were incorporated, and tracking of citations was fulfilled. The described method allowed for a broad yet detailed understanding, ensuring that the review of the literature covers all key aspects of the presented research. Results: Various aspects of thigh muscle anomalies were already undertaken; however, as this study has shown, current knowledge, while valuable, is insufficient to draw definitive conclusions regarding the prevalence and clinical implications of these muscle variations. A more robust body of ultrasound-based research is essential to accurately characterize these anomalies, establish their frequency, and assess their impact on clinical decision-making, including diagnostic accuracy, surgical planning, and therapeutic interventions. Conclusions: Numerous anatomical variations of the thigh muscles and tendons that were described in literature over the years might have clinical implications and could lead to mistakes during diagnosis by ultrasound imaging. Full article

In order to improve the driving stability of distributed-drive intelligent electric vehicles under different roadway attachment conditions, this paper proposes a multi-parameter control algorithm based on the estimation of road adhesion coefficients. First, a seven-degree-of-freedom (7-DOF) vehicle dynamics model is established and optimized with a layered control strategy. The upper-level control module calculates the desired yaw rate and sideslip angle using the two-degree-of-freedom (2-DOF) vehicle model and estimates the road adhesion coefficient by using the singular-value optimized cubature Kalman filtering (CKF) algorithm; the middle-level utilizes the second-order sliding mode controller (SOSMC) as a direct yaw moment controller in order to track the desired yaw rate and sideslip angle while also employing a joint distribution algorithm to control the torque distribution based on vehicle stability parameters, thereby enhancing system robustness; and the lower-level controller performs optimal torque allocation based on the optimal tire loading rate as the objective. A Speedgoat-CarSim hardware-in-the-loop simulation platform was established, and typical driving scenarios were simulated to assess the stability and accuracy of the proposed control algorithm. The results demonstrate that the proposed algorithm significantly enhances vehicle-handling stability across both high- and low-adhesion road conditions. Full article

In order to improve the operation control performance of high-speed maglev trains, an improved finite-time rotor magnetic Field-Oriented Control method was proposed in this paper. Aiming at the stator current control problem of long-stator linear synchronous motors under parametric perturbation, this paper investigates the double-feeding mode, combines the predefined-time stability theory and designs an improved sliding mode controller to optimise the dynamic characteristics of the inner-loop system. In the outer-loop cruise control, the predefined-time sliding mode control is combined with a finite-time disturbance observer, which effectively solves the problems of inaccurate modelling and parameter ingestion. It was verified through simulation and analysis that the control strategy has significant advantages in improving the dynamic tracking performance and anti-interference ability, with the stator current stabilisation time within 0.1 s, the absolute value of the fluctuation error within 20 A, the outer-loop response time within 0.5 s, the maximum speed error within 0.0005 m/s and the maximum displacement error within 0.0005 m. The control strategy has the advantages of improving the dynamic tracking performance and anti-interference ability. Full article

This paper focuses on a class of fractional-order systems with state delays and studies the design problem of the finite-time bounded tracking controller. The error system method in preview control theory is first used. By taking fractional-order derivatives of the state equations and error signals, a fractional-order error system is constructed. This transforms the tracking problem of the original system into an input–output finite=time stability problem of the error system. Based on the output equation of the original system and the error signal, an output equation for the error system is constructed, and a memory-based output feedback controller is designed by means of this equation. Using the input–output finite-time stability theory and linear matrix inequality (LMI) techniques, the output feedback gain matrix of the error system is derived by constructing a fractional-order Lyapunov–Krasovskii function. Then, a fractional-order integral of the input to the error system is performed to derive a finite-time bounded tracking controller for the original system. Finally, numerical simulations demonstrate the effectiveness of the proposed method. Full article

Aiming at the control challenges faced by unmanned surface vessels (USVs) in complex environments, such as nonlinearities, parameter uncertainties, and environmental perturbations, we propose a non-singular terminal integral sliding mode control strategy based on an extended state observer (ESO). The strategy first employs a third-order linear extended state observer to estimate the total disturbances of the USV system, encompassing both external disturbances and internal nonlinearities. Subsequently, a backstepping sliding mode controller based on the Lyapunov theory is designed to generate the steering torque control commands for the USV. To further enhance the tracking performance of the system, we introduce a non-singular terminal integral sliding mode surface with a double power convergence law and redesign the backstepping sliding mode controller for the USV heading control. Meanwhile, to circumvent the differential explosion issue in traditional backstepping control, we simplify the controller design by utilizing a second-order sliding mode filter to accurately estimate the differential signals of the virtual control quantities. Theoretical analysis and simulation results demonstrate that the proposed control algorithm improves the convergence speed, adaptive ability, and anti-interference ability in complex environments compared to traditional linear backstepping sliding mode control, thereby enhancing its engineering practicability. This research offers a more efficient and reliable method for precise heading control and path tracking of USVs in complex and dynamic environments. Full article

In the absence of a priori knowledge about target motion characteristics, the task of tracking complex maneuvering targets remains challenging. A multi-model polynomial-based complex target tracking method is presented to address this issue. Observation sequences of varying lengths are fitted by time polynomials of different orders, which are used to create a set of target motion models. Subsequently, the multi-model framework is employed to track maneuvering targets with uncertain motion characteristics. To verify the effectiveness of the suggested approach, three datasets were created with kinematic equation, the gazebo platform and real watercrafts. Based on the above three datasets, the proposed method is compared with classical multi-model methods and a deep learning method. Theoretical analysis and experimental results reveal that, in the lack of a priori information of target maneuvering features, the tracking error of the proposed method can be reduced by 12.5~30% compared with the traditional MM method. Moreover, the proposed method is able to overcome the problem of accuracy degradation caused by model misalignment and parameter tuning faced by the deep learning based methods. Full article

In response to the uncertainty of renewable energy output and the fluctuation of load, this paper proposes a hybrid energy storage management strategy based on the State of Charge (SOC) to smooth power fluctuations and thereby improve the power quality of photovoltaic energy storage DC microgrids. Firstly, a hybrid algorithm for power tracking control is formed by incorporating the Particle Swarm Optimization (PSO) algorithm into the variable step-size Incremental Conductance (INC) method, thereby optimizing the maximum power point tracking control system of the photovoltaic system. Then, a first-order filter is employed for the initial allocation of demand power. Taking the SOC of supercapacitors and energy storage batteries as a reference, a secondary power allocation energy management strategy based on rule-based control is proposed to ensure the service life and application safety of the hybrid energy storage system. Finally, simulation experiments are conducted in MATLAB/Simulink 23.2 (R2023b). The results indicate that the proposed energy management strategy can maintain the SOC of the hybrid energy storage system at a reasonable level and effectively smooth DC bus voltage fluctuations. Full article

This manuscript aims to increase the utilization of solar energy, which is both environmentally friendly and easily accessible, to satisfy the energy needs of developing countries. In order to achieve this goal, maximum power generation should be provided from photovoltaic panels. Several maximum power point tracking (MPPT) methods are utilized for maximum power generation in photovoltaic panel systems under different weather conditions. In this paper, a novel intelligent hybrid fuzzy logic-controlled maximum power point tracking algorithm founded on the perturb and observe (PO) algorithm is presented. The proposed fuzzy logic controller algorithm and the incremental conductivity maximum power point tracking algorithm were simulated in a MATLAB(2018b version)/Simulink environment and evaluated by comparing the results. Four Sharp ND-F4Q295 solar panels, two in series and two in parallel, were used for the simulation. In this study, the voltage ripple of the proposed hybrid method was measured at 1% compared to the classical incremental conductivity method, while it was 8.6% in the IncCon method. Similarly, the current ripple was 1.08% in the proposed hybrid FLC method, while the current ripple was 9.27% in the IncCon method. It is observed that the proposed smart method stabilizes the system voltage faster, at 25 ms, in the event of sudden weather changes. Full article

With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods. Full article