Search Results (3,545)

To address the trajectory tracking problem of underwater manipulators operating in complex marine environments with strong multi-degree-of-freedom coupling, pronounced nonlinearities, and actuator saturation constraints, this paper proposes a super-twisting sliding mode control scheme integrated with an extended state observer and an anti-saturation auxiliary system. A dynamic model of the underwater manipulator incorporating major hydrodynamic effects (added mass and drag) is first established. Based on this model, a super-twisting sliding mode controller is designed to achieve fast convergence of the tracking errors while effectively alleviating the chattering phenomenon associated with conventional sliding mode control. An improved extended state observer is then introduced to estimate unmodeled dynamics and external time-varying disturbances in real time, providing feedforward compensation to enhance system robustness. To explicitly handle actuator output limitations, an anti-saturation auxiliary system is further developed to dynamically regulate the control input and mitigate the adverse effects of saturation. Comparative simulation studies conducted on the Oberon7 underwater manipulator demonstrate that the proposed control strategy achieves higher trajectory tracking accuracy, improved disturbance rejection capability, and faster recovery after saturation release compared with conventional control methods. These results indicate that the proposed approach offers an effective and reliable solution for high-precision trajectory tracking control of underwater manipulators under input saturation constraints. Full article

This paper develops an extended-state-observer (ESO)-enhanced actor–critic reinforcement learning (RL) scheme for the trajectory tracking control of 3-DOF marine vessels subject to uncertain hydrodynamics and environmental disturbances. A coordinate-consistent error construction is provided to obtain an exact strict-feedback second-order uncertain template. On this basis, an Hamilton–Jacobi–Bellman (HJB)-inspired optimised control structure is implemented: the critic approximates the optimal value-gradient and the actor generates the optimised control law. A key simplification is employed: rather than minimising the squared Bellman residual via complex gradients, we introduce an HJB-inspired actor–critic consistency regularisation through a weight-matching coupling. This yields computationally light online update laws and enables transparent Lyapunov-based stability analysis while not claiming exact HJB satisfaction or policy optimality. The ESO estimates lumped uncertainty and provides feedforward compensation, so the RL module learns only the observer residual. A composite Lyapunov analysis establishes the semi-global uniform ultimate boundedness of tracking errors and boundedness of all observer signals. Practical implementation with thruster allocation, explicit wind–wave–current disturbance shaping filters, and a theory-aligned ablation protocol are provided for reproducibility. Full article

The export of Hass avocado (Persea americana Mill.) from Colombia requires accurate determination of harvest maturity, currently assessed through destructive dry matter (DM) measurements that are wasteful and limited in throughput. The objective of the article is to propose a low-cost, non-destructive approach to determine the maturity of the Hass avocado crop based on machine learning techniques. The approach consists of a low-cost, non-invasive bioimpedance spectroscopy system operating in the 1–10 kHz range, featuring a custom Analog Front End (AFE) and a tetrapolar surface probe to mitigate skin contact resistance, which collects data for predictive models of avocado maturity. To evaluate the quality of the approach, a longitudinal field study (n = 100) was conducted in a commercial orchard in Cundinamarca, Colombia, tracking complex impedance features—Magnitude, Phase Angle, Resistance, and Reactance—of tagged fruits over 8 weeks across four measurement timepoints. The predictive performance of a classical chemometric model (PLS-DA), non-linear classifiers (SVM, Random Forest), and a temporal Deep Learning (LSTM) architecture was compared using a Stratified Group K-Fold Cross-Validation scheme to prevent data leakage across fruits from the same tree. The 4-electrode configuration successfully isolated mesocarp impedance, identifying the 5–7.2 kHz band as the most sensitive to physiological maturation. In turn, the LSTM model achieved a mean accuracy of 92.0% and an AUC of 0.94, outperforming the other models by 4.0% in mean accuracy. The results demonstrate that modeling the temporal trajectory of impedance, rather than single-point measurements, improves harvest maturity classification in Hass avocados, providing a scalable, low-cost alternative to destructive testing. Full article

This paper focus on the high accuracy force control of electro-hydraulic proportional load simulator (EHPLS). Firstly, to weaken the influence of the unknown dead zone of the proportional valve, a mathematic model with a smooth inverse dead zone was constructed. Then, finite-time prescribed performance function, of which the desired steady-state value can be achieved within finite time, is defined to impose constraints on the tracking error, while the neural network feedback is introduced to compensate for the unknown dynamic, which can ensure the tracking accuracy further improved for the entire tracking process in the presence of unknown dead-zone parameters, unknown system parameters and disturbance. Finally, through design modification, the proposed control technologies are realized based on the output feedback signal. Comparative simulations under two desired force trajectories are carried out to verify the effectiveness of the proposed controller. Full article

The rehabilitation training of patients with lower limb motor dysfunction highly relies on the precise control of biomimetic knee joint robots. Existing control strategies generally suffer from insufficient control accuracy and weak anti-interference ability, and an optimization plan that balances high precision and strong anti-interference has not yet been formed, which seriously affects the effectiveness of rehabilitation training. In order to improve the control accuracy and anti-interference ability of biomimetic knee joint robots for leg rehabilitation training of patients with lower limb movement disorders, the purpose of this study is to address the performance shortcomings of existing biomimetic knee joint robot control strategies. The goal is to propose a high-precision and strong anti-interference control strategy to provide more reliable rehabilitation support for patients with lower limb movement disorders. Therefore, this article proposes an optimization strategy based on the Spider Bee Algorithm (SWO) combined with fuzzy PID control. Based on a biomimetic knee joint robot model, this study simulates three common pathological states of knee joint ligament injury, meniscus injury, and muscle atrophy in patients, and compares the trajectory tracking and anti-interference performance of PID, fuzzy PID, and SWO fuzzy PID control strategies. The experimental results show that the SWO fuzzy PID control strategy has the best comprehensive performance: the overshoot of knee joint angle control is only 9.7%, and the peak angle error is reduced to 2.1948°; when simulating pathological conditions, the system takes the shortest time to recover stability: 1.068 s for ligament injuries and 0.929 s for meniscus injuries, with maximum response errors below 0.017°. Simulation experiments on healthy subjects showed that the system had a tracking error of ≤5° under two rehabilitation training modes, meeting clinical accuracy requirements, and had good performance in restoring stability under irregular vibration interference. The core contribution of this study is the proposal of the SWO fuzzy PID optimization control strategy, which effectively addresses the shortcomings of existing strategies and significantly improves the control accuracy and anti-interference ability of bionic knee joint robots, providing theoretical support and practical reference for the application of bionic knee joint robots. Full article

High-precision cooperative control of dual-arm manipulators faces significant challenges arising from complex dynamic coupling, parametric uncertainties, and external disturbances. Furthermore, in networked control scenarios, communication bandwidth and computational resources are inevitably constrained. To address these issues, this paper proposes a novel composite control framework that integrates adaptive dynamic programming (ADP) with active disturbance rejection control (ADRC) under a static event-triggering mechanism (SETM). First, to handle model uncertainties and external perturbations, a smooth nonlinear extended state observer (ESO) based on continuous fractional-power functions is developed. This observer guarantees finite-time convergence of the disturbance estimation without inducing the high-frequency chattering inherent in conventional sliding-mode observers. Second, leveraging the disturbance-compensated dynamics, a radial basis function (RBF) neural network-based ADP controller is designed to learn the optimal control policy online, thereby minimizing a quadratic performance index without requiring accurate model knowledge. Third, to improve resource utilization, a static event-triggering strategy is introduced to schedule control updates based on the system state and tracking error. Extensive simulation studies on a 3-DoF dual-arm system demonstrate that the proposed scheme achieves superior trajectory tracking accuracy and disturbance robustness while significantly reducing the communication frequency compared to time-triggered approaches. Full article

Ground incidents on airport aprons can cause substantial operational disruption and economic loss, while conventional surveillance (e.g., Surface Movement Radar (SMR), Closed-Circuit Television (CCTV)) often lacks the resolution and proactive decision support required for close-proximity operations. This study proposes a UAV-deployable, camera-agnostic Computer Vision (CV) framework for collision-risk warning from elevated viewpoints. An optimised YOLOv8-Seg backbone performs multi-class aircraft segmentation (airplane, wing, nose, tail, and fuselage) and is integrated with four MOT algorithms under identical evaluation settings. For quantitative tracker benchmarking, DeepSORT provides the strongest overall performance on the airplane-only MOTChallenge-format ground truth (MOTA 92.77%, recall 93.27%). To mitigate the scarcity of annotated apron-incident data, a labelled 997-frame MOT dataset is created via an MSFS simulation-based reenactment inspired by the 2018 Asiana–Turkish Airlines wing-to-tail event at Istanbul Ataturk Airport. The framework further introduces a dual-module warning mechanism that can operate independently: (i) a reactive module using image-plane proximity derived from segmentation masks, and (ii) a proactive module that predicts short-horizon conflicts via trajectory extrapolation and IoU-based future overlap analysis. The approach is evaluated on multiple simulated incident scenarios and assessed on a real apron video from Hong Kong International Airport; additionally, laboratory-scale UAV experiments using diecast aircraft models provide end-to-end feasibility evidence on unmanned-platform imagery. Overall, the results indicate timely warnings and practical feasibility for low-overhead UAV-enabled apron monitoring. Full article

A crucial problem for autonomous aerial operations is to provide dependable and strong control of unmanned aerial vehicles (UAVs) in adverse environmental circumstances. The current paper provides an extensive analysis of the vehicle dynamics and control of drones in strong wind fields with altitude-dependent wind shear, wind gusts, and turbulence. A comparative evaluation of sliding mode control (SMC), linear quadratic regulator (LQR), model predictive control (MPC), adaptive constrained adaptive linear control (ACALC), and higher-order control barrier function (HOCBF)-based control in the context of trajectory tracking performance, control effort, and robustness is carried out. Simulation outcomes show that SMC exhibits superior robustness to sudden wind disturbances and the most consistent tracking accuracy under stochastic variations; HOCBF and ACALC provide comparable high precision with added constraint enforcement and adaptive capability, respectively; MPC has smooth control and minimal energy consumption; and LQR has a high level of computational efficiency with significantly tolerable tracking performance. Monte Carlo calculations are conducted to measure tracking errors and control energy under the stochastic wind variations, and the capability of the proposed control strategies to remain resilient in uncertain conditions is brought to light. The results provide useful information about the architecture of effective controllers used in UAVs during severe weather conditions and underline the compromises between the accuracy of tracking, the control effort, and the energy consumption. The suggested framework offers an effective and scalable system suitable for reliable autonomous drone activity in complicated reality settings. Full article

This study examines whether targets’ environmental, social, and governance (ESG) performance is associated with acquirers’ post-merger ESG outcomes and market valuation over the merger year and the subsequent two years. We treat controversies-adjusted ESG scores (ESGC) as outcome-based indicators. Using a global panel of 4572 acquirer-year observations from 47 countries between 2002 and 2023, we analyze the association between targets’ ESGC and acquirers’ post-merger ESG trajectories and market value. Tobit estimations trace combined and pillar-level ESG dynamics over the merger year and the first two post-merger years. The results indicate that target ESG performance is associated with persistent improvements in acquirer sustainability, with the strongest effects in social and environmental dimensions and more gradual adjustments in governance, reflecting institutional and organizational integration complexity. Heterogeneity analyses reveal that cross-border within-industry acquisitions generate the largest ESG gains, whereas domestic within-industry transactions are associated with ESG deterioration. Regarding market valuation, acquirers’ own ESG performance is reflected in Tobin’s Q, while target ESG becomes value-relevant with a one-year lag, highlighting a two-stage valuation mechanism linked to post-merger absorption and institutionalization. Adopting a multi-period perspective, the study shows that ESGC track post-merger sustainability outcomes in ways consistent with learning, institutionalization, and legitimacy-based interpretations. Full article

Previous research indicates that cognitive and affective-motivational factors, along with gender, influence students’ educational choices, especially regarding STEM tracks. However, few longitudinal studies have examined these factors during middle school, a critical stage in shaping future academic trajectories. This study investigated the longitudinal contribution of gender, cognitive abilities, and affective-motivational factors, such as self-concept, math interest, and math anxiety, in predicting students’ school choice between STEM and non-STEM tracks at the end of middle school. Data were collected from 159 Italian students, followed from seventh to eighth grade. Findings indicated that gender and positive attitudes toward math were strongly associated with STEM school choice. Boys were more likely than girls to choose STEM tracks (b = 5.048). Higher levels of math self-concept (b = 4.848) and interest (b = 0.887) significantly predicted the likelihood of choosing a STEM school. These results highlight how gender and affective-motivational factors shape educational pathways during adolescence. Full article

Accurate and computationally efficient trajectory tracking remains a critical challenge for unmanned aerial vehicles (UAVs), particularly when nonlinear model predictive control (NMPC) is combined with learning-based dynamics models that introduce significant computational burden. This paper proposes a sparse neural dynamics modeling approach by integrating structured pruning and robustness-enhancing fine-tuning techniques to enable efficient nonlinear MPC (NMPC) for UAV trajectory tracking. To this end, a structured neuron-level pruning strategy is introduced, combining L1-norm importance scores with adversarial sensitivity analysis to identify and remove redundant neurons from a neural dynamics model. To preserve smoothness and robustness in closed-loop control, spectral norm constraints and gradient regularization are further incorporated during fine-tuning. The resulting pruned neural dynamics model is embedded into an NMPC framework for online trajectory tracking. Simulation results on a fixed-wing UAV demonstrate that the proposed method reduces the number of trainable parameters by approximately 69% and achieves a 19% reduction in average NMPC solve time, leading to an effective control update frequency of about 39 Hz under the considered simulation settings. Compared with conventional controllers, including TECS and linear MPC, the proposed approach achieves significantly improved trajectory tracking accuracy, as reflected by lower MAE and RMSE across all position axes. These results indicate that structured sparsification of neural dynamics models provides an effective means to enhance both computational efficiency and tracking performance in NMPC-based UAV control. Full article

Surgical robots require sub-millimeter accuracy and reliable inverse kinematics across anatomies. Population-based metaheuristics address this, but static parameters may limit achieving the needed precision for clinical use. This study introduces the Rough Sets Meta-Heuristic Schema (RSMS) for dynamic, context-aware control. RSMS categorizes agents (Elite, Boundary, Poor) via Rough Set discretization based on fitness and distribution, allocating resources accordingly without problem-specific heuristics. To demonstrate the approach’s effectiveness, RSMS was implemented within Particle Swarm Optimization and evaluated as a surgical robotics inverse kinematics solver and path planner. In simulations using three surgical problems, RS-PSO allowed upgrading of the performance of the standard PSO in terms of consistent convergence and success in tight search spaces. Statistical tests confirmed these improvements. Using a 7-DOF KUKA LBR iiwa robot and surgical benchmarks of landmark acquisition, spiral trajectory tracking, and constrained path, RS-PSO achieved success rates of 100%, 67%, and 100%, respectively, meeting surgical requirements. The results demonstrate clinical gains in accuracy, consistency, and reproducibility for minimally invasive surgery. These findings support the practical advantages of RS-PSO and, more importantly, show that the RS-MH framework can be used as a general, reusable tool to improve the robustness, precision, and reproducibility of many swarm-based meta-heuristics for surgical robotics and other applications. Full article

When a doubly active bridge series resonant converter (DBSRC) performs phase shift switching under constant switching frequency and resonant frequency modes, significant transient oscillations occur in the resonant circuit due to the inability of its steady-state resonant voltage and current to achieve rapid transition. These transient oscillations impose substantial current and voltage stresses on power electronic devices, severely degrading the converter’s output voltage quality and dynamic response performance. To address this issue, this paper proposes a novel trajectory tracking modulation method. By precisely controlling the gate signals of both primary and secondary sides of the converter, this method enables the resonant voltage and the resonant current to track the target trajectory, thereby reducing transient oscillations that may last dozens of switching cycles to within half a cycle. Full article

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

In recent years, multi-UAV systems have demonstrated broad applications in both security and civilian domains, where cooperative encirclement has emerged as a key research focus. However, existing work predominantly addresses single-target scenarios with homogeneous UAVs using passive tracking strategies, which are inadequate for handling highly maneuverable targets. To overcome these limitations, this paper proposes an active interception decision framework integrating LSTM networks with an off-policy independent actor–critic framework employing a PPO-style clipped surrogate objective, referred to as LIPPO. It aims to address the complex problem of heterogeneous UAV swarms encircling multiple continuously learning targets. The framework employs an LSTM module for real-time trajectory prediction and uses the predicted future positions as interception points, shifting the paradigm from passive tracking to proactive interception. At the decision level, LIPPO adopts a hybrid architecture where each UAV acts as an independent learner, while a shared experience pool enables efficient knowledge transfer across the swarm. Comprehensive simulations demonstrate LIPPO’s superiority. In complex scenarios, it achieves an encirclement success rate up to 10 percentage points higher than non-predictive baselines and reduces energy consumption by nearly 28% compared to centralized training multi-agent reinforcement learning algorithms. These results confirm that LIPPO’s active interception is both effective and efficient. Full article