Search Results (3,914)

To address the issues of slow response to preceding vehicles and poor string stability in distributed platoon control of connected and autonomous vehicles (CAVs) under mixed traffic flow, this paper proposes a sliding mode control method based on LSTM trajectory prediction, denoted as LSTM-SMC, within a multi-agent framework. The LSTM model is trained using the HighD naturalistic driving dataset to achieve high-precision prediction of the leader vehicle’s trajectory over a horizon of 3 s, with root mean square errors (RMSE) of 8.52 m in the X-direction and 0.896 m in the Y-direction. The predicted trajectory information is converted into a preview error and embedded directly into the design of the sliding surface, enabling each following vehicle to anticipate disturbances before they propagate. A diminishing preview gain strategy (${\gamma }_1=0.4$, ${\gamma }_2=0.2$, ${\gamma }_3=0.1$) is employed to suppress error propagation along the platoon, while a saturation function is introduced to eliminate chattering and ensure smooth control inputs. Three simulation scenarios—prescribed leading, HDV (human-driven vehicle) leading, and curved road scenario—are constructed to validate the proposed method against traditional constant time headway (CTH) control, pure sliding mode control (SMC), and LSTM-MPC. Results demonstrate that under extreme conditions, the proposed method reduces the speed RMSE of the 3rd following vehicle by 18.3% compared to CTH and by 39.7% compared to SMC. Under HDV leading conditions, all string stability amplification factors are less than 1, and the position RMSE of the 3rd vehicle is only 5.03 m in the curved road scenario. Compared with LSTM-MPC, the proposed LSTM-SMC achieves comparable tracking accuracy while reducing computational cost by 1.43–3.51×. The proposed method achieves a native integration of prediction and robust control, significantly improving tracking accuracy, string stability, and computational efficiency across diverse operating conditions in mixed traffic flow. Full article

Many continuous industrial processes are non-linear, multi-variable, subject to transport or reaction delays, and described by operating-point-dependent parameters. These characteristics reduce the reliability of fixed models used in model predictive control (MPC), particularly when slow disturbances, regime changes and operational constraints are dominant. This paper proposes an output-bias reference-correction framework based on Long Short-Term Memory (LSTM) networks for predictive control of industrial processes with delays and variable parameters. The dominant dynamics are represented by a fixed compact linear nominal model in deviation coordinates; this model drives a standard constrained MPC that remains structurally unchanged throughout operation. The persistent output bias between the actual process and the nominal model is learned from closed-loop data by an LSTM network. At each sampling step, the predicted bias is used to correct the future reference trajectory fed to the nominal MPC, so that the controller compensates for model–process mismatch without modifying its internal model, constraint set or solver. The final implementation uses a one-step bias predictor, selected by ablation, and it extends this one-step estimate across the MPC horizon by exponentially decayed persistence. A closed-loop bias-error bound links the LSTM identification error, the adaptive correction gain and the resulting tracking deviation. The framework is illustrated using a mining thickener, a representative process characterised by slow dynamics, delays, variable parameters and stringent safety constraints. A three-controller Monte Carlo study compares the nominal MPC, a classical offset-free MPC and the proposed LSTM-MPC, and it highlights the resulting tracking–actuation–constraint trade-off. Applied to a mining thickener, the LSTM corrector reduces the first-step output-prediction RMSE by 96.6 % (FIT from $-14.8\%$ to $96.1\%$). In a 50-scenario Monte Carlo closed-loop evaluation, the LSTM-MPC outperforms the nominal MPC in 92 % of scenarios on RMSE while using substantially less actuator activity than the offset-free baseline (mean input total variation: 67.0 vs. 119.4). Full article

UAVs operating in cluttered and dynamic environments face limitations when controlled using conventional Euclidean frameworks, which leads to degraded performance during aggressive maneuvers. This paper presents a geometry-aware predictive control framework for multirotor UAVs based on a two-manifold-inspired representation of actuator geometry. The proposed control strategy adopts an MPC-inspired prediction structure without solving a full online optimization problem. A comparative simulation study is conducted in MATLAB/Simulink under identical mission, obstacle, and noise conditions for both the conventional point-mass controller and the proposed two-manifold-based controller. Performance is evaluated in terms of trajectory tracking accuracy and control effort. Results indicate that the proposed framework achieves modest improvements in tracking accuracy and produces smoother control inputs compared to the point-mass model. It suggests that geometry-aware representations may enhance predictive control performance in obstacle-rich environments, although further validation under diverse scenarios is required. Full article

Tethered unmanned surface vehicle (USV) and remotely operated vehicle (ROV) systems are widely used in deep-sea inspection, observation, and intervention tasks. During cooperative operations, the USV must follow the mission trajectory of the ROV while avoiding surface obstacles and maintaining a prescribed tether-related safety envelope. This study proposes a multi-objective nonlinear model predictive control (NMPC) framework for USV-side cooperative tracking and dynamic obstacle avoidance in tethered USV-ROV operations. The framework integrates the predicted three-dimensional ROV trajectory, USV nonholonomic motion, surface-obstacle avoidance, straight-line tether-length-related geometric constraints, and control-smoothness regulation into a unified receding-horizon optimization problem. Sequential Least Squares Programming is used to compute the online control sequence. Numerical simulations include obstacle-free tracking under bounded ocean-current disturbances and heterogeneous surface–underwater obstacle scenarios. The results show that the proposed controller provides improved tracking performance and maintains the straight-line tether-length proxy below the prescribed limit in the tested simulations. The current-disturbance results further indicate preliminary disturbance-rejection capability under bounded time-varying ocean currents. The study provides a controller-level numerical framework for cooperative tracking and surface obstacle avoidance in tethered USV-ROV operations. Full article

Addressing the challenge that, in real-world dynamic multi-objective optimization problems (DMOPs), the severity of changes between pareto optimal set (PS) varies at different times and exhibits nonlinear characteristics rather than simple translations or rotations—making them difficult for traditional prediction strategies to track accurately—this paper proposes a dynamic multi-objective optimization algorithm via trend-cycle decoupling and hybrid time-series prediction. The algorithm first applies the Hodrick-Prescott (HP) filter to decompose the time-series of historical PS centers into a smooth trend component and a fluctuating cycle component to cope with uncertainty in the severity of changes. Then, an AR(p) model is used to fit the trend sequence and infer the long-term linear direction of PS movement; a long short-term memory (LSTM) network learns the cycle sequence to capture nonlinear variation patterns. By fusing the two prediction results, the center of the PS in the new environment is located, and an initial population is constructed using a manifold-based population generation strategy. Comparative experiments on 13 standard dynamic test functions show that the proposed algorithm achieves an effective trade-off between prediction accuracy and computational cost and demonstrates strong robustness to complex time-varying environments. In particular, in scenarios where the pareto optimal front (PF) undergoes rotation, discontinuity, or time-varying shape (convexity/concavity) due to complex mappings in the decision space, the algorithm maintains notable tracking accuracy and population diversity by precisely capturing the PS evolution trajectory. Full article

Feature alignment and decision-level fusion in multimodal BCI–VR interaction were investigated using Transformer-based cross-modal embeddings, Lab Streaming Layer time synchronization, attention masks, and wavelet filtering for robust representation. A four-modal acquisition and synchronization platform covering electroencephalography, electromyography, eye-tracking, and speech was constructed, and fusion was achieved by introducing a stacking meta-learner together with a confidence-aware dynamic weighting mechanism. Prototype validation and comparative evaluations were conducted on virtual reality (VR) target-selection, trajectory-following, and object-manipulation tasks. The results showed that the proposed approach outperformed baselines such as weighted voting and independent single-modality classifiers in accuracy, cross-session and cross-subject generalization, and noise robustness, while achieving a measurable reduction in end-to-end response latency, indicating that an integrated semantic alignment–adaptive fusion pipeline enhanced stable outputs and robustness in multimodal interaction. The unified semantic alignment model tailored to BCI–VR can be used for establishing an integrated engineering workflow spanning synchronization, robust representation, and adaptive fusion, and for providing transferable evaluation metrics and application paradigms that offer methodological and technical references for scenarios such as rehabilitation training, virtual education, and intelligent control. Full article

To achieve manipulator trajectory tracking under uncertainties and external disturbances, this study develops a prescribed performance nonsingular sliding mode control strategy. A new sufficient condition for predefined-time stability is established and proved. A predefined-time nonlinear disturbance observer is designed to estimate the lumped disturbance, and a prescribed performance function is introduced to confine the tracking error within predefined bounds. A predefined-time nonsingular sliding mode surface is constructed, while a saturation function and a hyperbolic tangent function are adopted to address singularity and chattering, respectively. Numerical simulations are conducted on a two-degree-of-freedom manipulator subject to 20% parametric uncertainties and time-varying external disturbances. The effectiveness of disturbance compensation is evaluated by comparing the control performance with and without observer compensation, and the proposed method is further compared with fixed-time and finite-time sliding mode controllers. Quantitative results show that, with observer compensation, the integral absolute error (IAE), integral squared error (ISE), and root mean square error (RMSE) are reduced by 71.63%, 12.95%, and 6.60% for Joint 1, and by 79.24%, 35.57%, and 19.55% for Joint 2, respectively. Moreover, compared with the fixed-time method, the proposed controller reduces the IAE by 54.3% for Joint 1 and 63.1% for Joint 2, while the corresponding reductions relative to the finite-time method are 89.0% and 93.3%, respectively. These results verify the effectiveness of the proposed scheme in disturbance rejection and tracking accuracy. Full article

Freehand three-dimensional (3D) ultrasound enables cost-effective volumetric breast imaging, but accurate reconstruction requires reliable probe tracking during manual scanning. This study proposes a portable freehand 3D ultrasound framework using dual-rotary-encoder two-degree-of-freedom (2DoF) pose sensing to measure probe displacement and inclination during breast scanning. A slip-resistant roller mechanism and time-aware trajectory modeling were introduced to improve measurement robustness under practical scanning conditions. The framework was evaluated through robotic experiments and phantom-based volumetric reconstruction. Positional displacement experiments achieved root mean square errors (RMSEs) of 0.38 mm on dry surfaces and 0.81 mm under gel-coated conditions. Inclination sensing using the rotary encoder outperformed an inertial measurement unit (IMU), achieving an RMSE of 2.76° with improved temporal stability. Reconstruction experiments using a breast phantom with spherical inclusions demonstrated successful volumetric visualization across multiple scanning trajectories. Statistical analysis revealed significant effects of inclusion size and scanning trajectory on relative reconstruction error, as well as a significant interaction between the two factors. Larger inclusions generally exhibited lower relative errors, while the influence of scanning trajectory depended on the target size. These findings support the feasibility of the proposed reduced-dimensional mechanical pose sensing approach for reliable freehand 3D ultrasound reconstruction with reduced hardware complexity. Full article

This study presents a simulation-based multiphysics design, modeling, and adaptive control framework for a dual-propulsion unmanned aerial underwater vehicle intended for aerial, near-surface, and fully submerged operation. The proposed platform uses four aerial rotors for flight and six underwater thrusters for submerged maneuvering, allowing medium-dependent actuation in air and water. Separate aerial and underwater six-degrees-of-freedom models are formulated and connected through a smooth altitude-dependent coordination strategy for the simplified near-surface region. Computational fluid dynamics is used to estimate submerged drag forces, while finite element analysis evaluates pressure-hull structural integrity at a depth of 20 m. At 0.2 m/s, the predicted horizontal and vertical drag forces are 1.62 N and 3.92 N, corresponding to quadratic damping coefficients of 40.5 and 98.0 N·s²/m². The FEA results show that PMMA provides a safety factor of 7.8, with a maximum displacement of 0.53 mm under hydrostatic loading. An adaptive backstepping controller with projected gain tuning, disturbance compensation, and constrained actuator allocation is developed. MATLAB/Simulink simulations demonstrate bounded trajectory tracking under nominal conditions, 20% parametric uncertainty, modeled disturbances, and a 0.5 m/s ocean current. Full article

Nonlinear kickers (NLKs) enable off-axis injection in ultralow-emittance storage rings by providing a strong kick to the injected beam while remaining nearly transparent to the stored beam. In hybrid schemes, a conventional four-kicker bump defines the injected trajectory, and the NLK reduces the first-turn action under constrained beam offset and optics conditions. Effective operation additionally requires stable and reproducible first-turn injection trajectories. We develop a compact action–angle framework that expresses NLK dynamics in terms of Courant–Snyder invariants and yields an analytical bound on achievable action reduction. This formulation provides direct design rules for NLK placement, phase advance, injected-beam offset, and kicker field profile. Within this framework, we identify the 8-wire NLK as a practical baseline and extend its design by relaxing the square-geometry constraint, enabling inward shifting of the off-axis field peak while preserving on-axis field and gradient cancellation. Application to the NSLS-II lattice shows how aperture, pulsed-power, and mechanical constraints combine to determine a coupled design solution. Multi-turn tracking confirms that candidate NLK locations maintain sufficient stay-clear (aperture-clearance) margin, while the optimized wire geometry reduces the required current and Lorentz force load. The results establish a unified approach for NLK-assisted injection design and provide a practical pathway for upgrades in diffraction-limited storage rings. Full article

Since 2011, massive strandings of pelagic Sargassum have become a recurrent environmental hazard across the tropical Atlantic and Caribbean archipelago, creating an urgent need for reliable short-term drift forecasts to support coastal risk management. This study evaluates key sources of uncertainty in operational Sargassum drift forecasting by analyzing the sensitivity of Lagrangian simulations to the representation of floating material and to environmental forcing fields. The analysis uses two complementary observational datasets: trajectories of four GPS-tracked Sargassum mats deployed near Puerto Rico and thirteen 24 h displacement vectors derived from sequential Sentinel-3 satellite detections across the tropical North Atlantic. Drift simulations were performed with the MOTHY model under multiple configurations, testing two material parameterizations, different atmospheric forcings, and several ocean circulation products and vertical current integration strategies. The results indicate that the best agreement with observed trajectories is obtained for partially immersed structures, highlighting the importance of balancing wind exposure and hydrodynamic drag. Sensitivity experiments further show that ocean circulation forcing dominates trajectory skill, while higher-resolution atmospheric forcing provides limited improvement under offshore conditions. Overall, the study confirms the importance of accurately representing upper-ocean transport processes and provides observational support for several operational choices implemented in the Météo-France Sargassum forecasting system. Full article

Four-wheel independent steering (4WIS) vehicles can improve low-speed maneuverability and high-speed stability by independently regulating the steering angles of all four wheels. However, under large-curvature trajectories, parameter perturbations, and external disturbances, inconsistent coordination among the four-wheel steering angles may increase tire lateral slip, yaw response deviation, and trajectory tracking errors. To address the difficulty of conventional trajectory tracking methods in simultaneously ensuring geometric consistency, tracking accuracy, and robustness, this paper proposes an adaptive sliding mode trajectory tracking control method based on instantaneous center of rotation (ICR) constraints. First, the tire instantaneous turning center (TTC) of each wheel is derived using rigid-body spatial kinematics, and the TTCs are mapped onto a unified vehicle-body reference plane based on the SAE J670 coordinate system to obtain a real-time vehicle-level ICR estimation. Second, a lateral–yaw dynamic model and a trajectory tracking error model are established. The yaw rate and sideslip angle are corrected using ICR geometric information, and an adaptive sliding mode controller is designed with an equivalent control term, adaptive switching gain, adaptive boundary layer, and sideslip suppression term. The uniform ultimate boundedness of the sliding variable and closed-loop tracking errors is proven using Lyapunov theory. Finally, MATLAB (2023a)2024/CarSim (2019) co-simulations are conducted under small-curvature sinusoidal, double-lane-change, large-curvature sinusoidal, low-adhesion, and mass-perturbation conditions. The results show that the proposed ICR-SMC method significantly reduces lateral and heading errors compared with U-LQR and U-SMC, especially under large-curvature and low-adhesion conditions, demonstrating improved tracking accuracy and robustness for 4WIS vehicles. Full article

This paper presents a distributed coordination framework for simultaneous multi-target tracking using a mobile wireless sensor network (MWSN) based on discrete-event-system principles. The proposed framework employs a finite-state-machine architecture, where autonomous mobile sensors sequentially process detection and tracking events. Unlike passive tracking approaches that react to target loss after it occurs, the proposed strategy implements predictive handover through Extended-Kalman-Filter-based uncertainty propagation. This enables sensors to anticipate target loss and to reposition auxiliary sensors in advance, acquiring targets along their predicted trajectories. A bidding-based allocation mechanism coordinates sensor assignments by evaluating four competing objectives: network preservation, spatial proximity to handover points, temporal mission feasibility, and estimation uncertainty. The proposed framework integrates four components: EKF-convergence-triggered proactive handover, multi-objective competitive bidding, distributed min–max conflict resolution, and fusion-driven proportional navigation. Unlike existing methods, auxiliary sensors navigate using confidence-weighted EKF estimates shared by neighboring sensors rather than their own measurements. An ablation study over ten Monte Carlo trials confirms that each component contributes independently, with EKF-based predictive triggering identified as the dominant performance driver. Full article

Forest understorey herbs are an under-studied component of subtropical mountain forest biodiversity, yet they include several genera of high medicinal and economic value. The rhizomes of Polygonatum (Liliaceae) are a prominent example, but the forest-ecological controls on their bioactive composition in wild populations—particularly for co-occurring congeners—remain poorly resolved. We sampled 92 wild plants of Polygonatum cyrtonema and P. filipes along four altitudinal transects (330–1730 m) in a subtropical mountain forest reserve in southeastern China, quantifying total polysaccharide, three flavonoid monomers (rutin, quercetin, and methylophiopogonanone B), and two LC–MS class signals (ΣFlavonoid, ΣSaponin), together with 13 topographic, edaphic, and biotic predictors. The two species displayed the following distinct rhizome chemical phenotypes: P. cyrtonema tended toward higher ΣSaponin; P. filipes toward higher ΣFlavonoid. The clearest pattern was a robust species × altitude interaction for total polysaccharide (p = 0.002), with the two species following opposite altitudinal trajectories. In multivariate forward-selected redundancy analysis, canopy closure and species identity emerged as the only retained environmental predictors, identifying forest light environment as the strongest single environmental correlate of rhizome chemical variation. Species-specific bivariate analyses further revealed contrasting driver hierarchies as follows: P. cyrtonema chemistry tracked topography, whereas P. filipes chemistry tracked rhizosphere soil enzymes and chemistry; only soil temperature and urease activity were shared across species. These results argue that altitude is not a uniform predictor of rhizome chemistry in wild Polygonatum, and support species-specific, canopy-aware management of medicinal forest understorey herbs in subtropical mountain forests. Full article

Anti-UAV multi-object tracking in remote sensing videos is challenging because UAV targets are small, weakly textured, and often affected by cluttered backgrounds, abrupt motion, occlusion, and intermittent visibility. To address these challenges, we formulate anti-UAV multi-object tracking as a context-aware identity prediction task, in which target identities and locations are inferred from historical trajectory priors instead of current-frame observations alone. Under this formulation, we propose a dual-track parallel tracking framework. The adaptive identity disambiguation (AID) module combines motion cues with appearance features according to their estimated reliability, improving short-term association when visual evidence is weak. In parallel, the motion-evolution temporal memory (METM) module models trajectory dynamics using motion anomaly detection and time-decayed memory, enabling spatiotemporal recovery after occlusion, temporary disappearance, or abrupt motion. The outputs of the two branches are integrated by a unified identity decision layer to produce stable tracking results. Experiments are conducted on the public 4th Anti-UAV Benchmark Track-3 and our newly constructed Anti-UAV Multi-Object Tracking dataset, AU-MOT. On the 4th Anti-UAV Benchmark Track-3, our method achieves 63.6% HOTA and 64.1% IDF1, outperforming the strongest competing method by 3.5% and 3.9%, respectively, while reducing identity switches and track fragments by 20.8% and 23.8%. On AU-MOT, it achieves 67.2% HOTA and 67.8% IDF1, with 20.2% fewer identity switches and 22.3% fewer track fragments. These results demonstrate its effectiveness under long-range observation, weak target appearance, cluttered backgrounds, abrupt motion, and intermittent target visibility. Full article