Search Results (230)

Filtering-based stereo visual-inertial odometry often assigns fixed or uniformly scaled covariance to tracks accepted by the front-end, although accepted observations can differ in terms of tracking reliability, local directional identifiability, and agreement with the batch-level innovation model. This issue is important for UAV-based multisensor inspection platforms, where pose estimates support autonomous flight, measurement registration, repeatable survey lines, and multisensor data fusion. This paper presents RDIC-MSCKF, a Risk–Direction-Decoupled and Innovation-Calibrated MSCKF, where innovation calibration denotes bounded empirical scaling within the visual update. RDIC-MSCKF maps robust tracking diagnostics to a bounded standard-deviation multiplier and uses a robust local photometric information matrix to add penalty-only anisotropic covariance along weak image directions. The resulting observation covariance is preserved during MSCKF landmark elimination through full projected-covariance whitening. In parallel with feature-block innovation gating, bounded minimum measurement-noise inflation is estimated from the pre-gate innovation population and applied through a Kalman-equivalent modal update. On ten evaluated EuRoC MAV sequences, RDIC-MSCKF obtains lower ATE RMSE than the S-MSCKF baseline on nine sequences; averaged over five runs per sequence, the mean RMSE decreases from 0.1869 m to 0.1236 m, corresponding to a 33.8% reduction, and the sequence-mean P90 error decreases by 32.0%. Runtime profiling on five representative EuRoC sequences gives a 26.32 ms mean and 37.33 ms P95 per-frame processing time for RDIC-MSCKF, with 0.01% of profiled frames above the 50 ms reference. Outdoor UAV flights with RTK reference trajectories further demonstrate lower Sim(2)-aligned horizontal RMSE than S-MSCKF on all three evaluated flights. Full article

To address the influence of multi-source probabilistic uncertain parameters on the dynamic transmission error (DTE) of harmonic drives, this paper proposes a robust DTE modeling and multi-objective optimization method. First, a Chebyshev surrogate model is constructed by integrating the measured static transmission error (STE) probability model, system dynamic equations, and identified nominal parameters. Prototype validations show a prediction mean absolute percentage error (MAPE) of 8.14% and a mean absolute error (MAE) of 7.761″. Meanwhile, compared to the original dynamic equations, the surrogate model reduces the single-evaluation time from 0.147 s to 0.000003 s (a 49,000-fold acceleration), effectively overcoming the efficiency bottleneck of numerical integration in dynamic response evaluation. Secondly, to achieve the collaborative optimization of system transmission accuracy and anti-disturbance robustness, a Chebyshev–AMP–MOPSO algorithm integrating a diversity entropy state-driven weight and a pyramid-hierarchical dual-track search strategy is proposed, which improves upon the issues of local convergence and uneven solution set distribution in the classical MOPSO and NSGA-II algorithms. On this basis, parameter optimization under three decision preferences was completed. The accuracy-first scheme reduces the DTE mean by 3.67%, the robustness-first scheme reduces the standard deviation by 9.36%, and the balanced scheme improves both. Finally, comparative tests on five prototypes show the actual dynamic parameters’ deviation (D_i) relative to the theoretical optimal configuration exhibits a consistent corresponding trend with measured DTE means. Prototypes with the minimum (D_i = 0.365) and maximum (D_i = 0.474) deviations yield the lowest and highest measured means, respectively, matching theoretical optimization expectations. Full article

Synthetic Aperture Radar (SAR) backscatter serves as a key tool for tracking surface water dynamics; however, single-source data dependencies introduce systematic bias tied to the specific physical limitations of the signal. A primary challenge in SAR analysis is the backscatter ambiguity created by ‘water look-alike’ surfaces, which frequently result in false-positive water detections. We show that integrating interferometric repeat-pass coherence significantly enhances the robustness of hydrological mapping in environments where backscatter is prone to signal ambiguity. Using global-scale C-band Sentinel-1 (S1) VV-polarized one-year mosaics (December 2019 to November 2020), we first analyzed normalized backscatter and coherence signatures across major land cover and land use (LULC) classes. To benchmark the complementary value of these data streams, a tile-based minimum-error thresholding approach was applied to detect permanent water surfaces across five challenging global test sites. This evaluation was conducted without post-processing or masking to isolate the fundamental strengths of each dataset. The results indicate that coherence is an optimal complement to backscatter in arid and bare soil regions, where it vastly outperforms backscatter in mapping inland water surfaces. Crucially, since the spatial overlap of False Positives and False Negatives between datasets is minimal, the inherent complementarity of the datasets is proven here via a logical AND fusion rule, which significantly mitigates commission errors and yields substantial improvements in the aggregated F1-score and IoU performance. Analysis-ready L-band NISAR products could contribute to a more comprehensive approach for operational, large-scale surface water assessments. Full article

Convection-permitting simulations resolve the deep convective cells that organise Mediterranean tropical-like cyclones. They also generate localised pressure minima that can capture a conventional cyclone tracker and pull it away from the synoptic-scale centre. We introduce High-Resolution Multilevel Python-Based Algorithm for Cyclones’ Centroid Tracking (HIMPACT), an open-source Python algorithm developed by the corresponding author within the CETEMPS framework, that stabilises cyclone-centre identification by combining three elements: a multi-level geopotential analysis restricted to the 800–950 $\mathrm{hPa}$ layer, a percentile-based threshold that isolates the vortex core from convective perturbations, and a convex-hull centroid that depends on the geometry of a percentile-defined core rather than on a single extreme grid point, so that an isolated convective pressure deficit cannot displace the estimate by more than a fraction of the core radius. HIMPACT was evaluated in four tracking experiments across three Mediterranean cyclones at grid spacings from 2 to 28 $\mathrm{k}\mathrm{m}$ using WRF, ICON-DREAM and ERA5, while MPAS was additionally used to test portability and computational scaling on an unstructured Voronoi mesh. Across the three experiments in which the driving data resolve a coherent lower-tropospheric cyclone structure, the best five-level configurations reduce root-mean-square displacement errors by approximately 16–48% relative to the corresponding single-level configurations. Activating the absolute minimum alongside the centroid more than doubles the error variance when the pressure field is multi-modal. The 800–950 $\mathrm{hPa}$ window avoids both surface extrapolation artefacts below 950 $\mathrm{hPa}$ and mid-tropospheric steering signatures above 800 $\mathrm{hPa}$. A counterexample with an extratropical storm exposes a data-quality threshold: when the driving dataset does not resolve a vertically coherent cyclone structure, the multi-level weighted mean diverges, and single-level tracking becomes the safer choice. HIMPACT is model-agnostic, requires no format conversion, and runs on a single CPU core at approximately 9.8–41.3 s per time step for the recommended five-level configuration across the tested back-ends; substantially larger costs occur for high-level-count MPAS configurations. Full article

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

Autonomous commercial vehicles (ACVs) frequently encounter drastic variations in payload and complex road conditions during practical operations. Consequently, effectively suppressing external disturbances caused by payload and road slope uncertainties has become a critical challenge in enhancing the robustness of their low-level control systems. To address this issue, this paper proposes a sliding mode control (SMC) strategy based on Luenberger observer disturbance compensation (LOSM), aiming to simultaneously mitigate the adverse effects of these two uncertainties on the vehicle’s speed control performance. First, according to the driving characteristics of commercial vehicles, a full-condition longitudinal dynamic model encompassing uphill, downhill, and flat road scenarios is established. Second, by deeply integrating the Luenberger observer with sliding mode control theory, an active disturbance rejection LOSM speed controller is designed. Furthermore, the boundary conditions for the closed-loop system to achieve asymptotic stability are rigorously derived and proven using Lyapunov functions. Finally, to comprehensively verify the effectiveness of the proposed strategy, eight typical testing scenarios are constructed, and three benchmark algorithms—PI control, radial basis function adaptive sliding mode (RBFSM) control, and radial basis function backstepping sliding mode (RBFBSSM) control are introduced for comparative analysis. The validation results demonstrate that although all four methods can achieve speed tracking and suppress disturbances, the proposed LOSM strategy exhibits the optimal comprehensive performance across various scenarios. Specifically, its steady-state mean error is typically maintained below 2.5%, and it yields the minimum steady-state variance in the majority of scenarios. These results demonstrate that the designed LOSM method can significantly improve the precision and smoothness of ACVs’ speed control under the dual disturbances of unknown mass and road slope. Full article

Hierarchical multi-UAV planning can produce scheduled paths that become unsafe during execution because tracking dynamics, actuator limits, sampling, and communication are not fully represented upstream. We introduce a certificate-guided safe-tracking framework that treats each planned path as a versioned execution contract with explicit tube, separation, timing, uncertainty, communication, and input bounds. A rigid-body-derived translational interface supports a command-producing control Lyapunov function–control barrier function quadratic program with hard safety constraints. When a candidate becomes infeasible, a separate minimum-slack program localizes the conflict without passing its command to the plant, and an explicit repair map converts the resulting witness into timing or vertical-spacing updates, with escalation when local repair fails. Randomized comparisons show that certificate feedback removes the observed tube and separation failures of a plain CBF-QP while maintaining reliable completion and competitive tracking relative to a tracking-error-bound comparator. Disturbance and sensor-noise sweeps characterize robustness, and separate indoor flights confirm single-reference trackability. The framework therefore turns execution infeasibility into actionable planning feedback rather than a terminal controller failure. Full article

Modern aeronautical engineering increasingly demands multifunctional materials that provide capabilities beyond passive load-bearing. While self-healing composites offer autonomous repair against impact damage, their practical application requires reliable in situ perception of structural states. Addressing this, this study proposes a novel multifunctional composite structural framework that synergistically integrates damage self-healing with guided wave-based structural health monitoring. Rather than treating monitoring and repair as isolated processes, this integrated strategy endows the composite with self-aware and self-healing characteristics, enabling closed-loop tracking of the dynamic “healthy–damaged–healed” processing. The interactive effects of simulated damage and various structural impact damaged states on distinct guided wave modes were systematically analyzed. Furthermore, a state index method is introduced to characterize damage propagation and accurately distinguish between evolving structural states. Experimental validations demonstrated spatial awareness: the framework achieved a minimum localization error of 5 mm for simulated damage. Crucially, for barely visible impact damage, the damaged and self-healing states were precisely localized with errors of 3 mm and 5 mm, respectively, with all overall localization errors strictly bounded below 9 mm. These findings validate the efficacy of the proposed multifunctional strategy, providing a highly reliable paradigm for the next generation of intelligent, damage-resilient aeronautical composite structures. Full article

Passive acoustic localization in complex shallow waters requires algorithms tailored to specific operational constraints. This paper investigates the adaptability, computational efficiency, and statistical performance boundaries of five matched-field processing (MFP) methods—Bartlett, Minimum Variance Distortionless Response (MVDR), Multiple Signal Classification (MUSIC), Reduced Covariance Matrix (RCM), and Rank and Trace Minimization (RTM)—using the Elba-93 sea trial dataset. Error metrics and processing complexities are systematically evaluated across stationary and maneuvering target scenarios. Rigorous non-parametric statistical tests reveal distinct operational boundaries: under stationary conditions dominated by systemic environmental mismatch, energy-based processors guarantee reliable baseline stability. Conversely, under snapshot-deficient dynamic conditions tracking a receding target, standard high-resolution subspace methods become highly vulnerable to trajectory jumps. In such highly dynamic scenarios, adaptive energy-based processors (specifically MVDR) exhibit the most stable tracking continuity and lowest numerical peak errors. Simultaneously, the operational adaptability of subspace methods is improved via covariance matrix reconstruction (CMR). Specifically, the RCM technique effectively decouples unstructured sensor noise, mitigating maximum trajectory deviations and providing a balanced trade-off between computational efficiency and robustness. Statistical evaluations confirm the fundamental performance boundaries in static environments, while highlighting sample-size limitations in highly dynamic scenarios, thereby establishing a realistic, evidence-based benchmark for marine engineering applications. Full article

Distributed-drive all-wheel steering (AWS) six-axle vehicles possess distinct advantages in power performance, maneuverability, and environmental adaptability. However, when navigating tight curves under sudden low-friction road conditions, their inherent long wheelbase and strong inter-axle coupling typically lead to compromised spatial maneuverability, trajectory decoupling between the vehicle nose and tail, and lateral dynamic instability. To resolve these critical issues, this paper proposes a geometry–dynamics coupled lateral control scheme with adaptive speed planning for six-axle vehicles under confined spatial and low-friction conditions by seamlessly fusing a dual-point preview mechanism with multi-mode steering mappings. First, a three-degree-of-freedom nonlinear vehicle dynamic model incorporating longitudinal, lateral, and yaw motions is constructed, alongside the formulation of extended Ackermann kinematic steering manifolds for three distinct modes: rear-axle steering, center steering, and crab steering. To rectify the kinematic under-constrained deficiency inherent in conventional single-point preview path-tracking architectures, a joint front-and-rear dual-point preview constraint mechanism is established. This framework permits the quantitative derivation of a spatial geometric reconstruction method for the instantaneous center of rotation (ICR), which algebraically maps the ideal ICR trajectory requirements onto the physical constraints of the selected steering modes. Consequently, complete geometric constraints on both the front and rear trajectories are achieved, enabling active compression of the vehicle’s turning radius. Furthermore, to handle sudden low-friction disturbances, road adhesion limits and vehicle lateral stability boundaries are explicitly incorporated to design a multi-scale adaptive preview distance dynamic scaling mechanism driven by dynamic safety margin corrections. By adaptively scaling the spatial constraint at the geometric layer, this mechanism proactively mitigates nonlinear tire sideslip force saturation via feedforward action, thereby preventing tracking divergence and catastrophic sideslip instability under physical adhesion limits. Co-simulations based on the high-fidelity TruckSim-Simulink platform demonstrate that, in standard curves, the proposed dual-point preview manifold fusion strategy reduces the minimum turning radius by 9.6–10.1% and shortens the cornering transit time by 7.5% compared with the traditional single-point preview mechanism. By actively constraining the front and rear trajectories, the trajectory decoupling between the vehicle nose and tail is effectively resolved. Under narrow-lane scenarios, the maximum lateral error is restricted within 0.78 m, representing a 37.6% reduction relative to the single-point preview, while the maximum steering angle of the front axle is compressed by approximately 18%, thereby significantly improving spatial passability and preventing intermediate body interference. Most notably, under low-friction surface disturbances, the dynamic-margin-corrected adaptive preview adjustment mechanism exhibits remarkable robustness, constraining the maximum lateral tracking error to within 0.68 m. The proposed geometry–dynamics coupled lateral control strategy successfully elevates the tight-curve maneuverability of heavy transport vehicles while concurrently reinforcing their lateral dynamic stability under limit combined spatial and adhesion constraints. Full article

Multi-UAV formation control in low-altitude urban environments faces critical challenges from atmospheric boundary layer (ABL) disturbances, including turbulence, wind gusts, and communication inefficiency in resource-constrained swarms. This paper proposes an adaptive dynamic event-triggered formation control (ADETFC) strategy integrated with a finite-time disturbance observer (FTDO) for multi-agent hexarotor UAV formations operating under ABL conditions. The novelty of the proposed ADETFC lies in employing dual adaptive parameters to simultaneously account for tracking error magnitude and inter-agent formation geometry, dynamically adjusting communication frequency. A nonsingular terminal sliding mode manifold ensures rapid transient convergence and robustness against nonlinearities and inter-agent coupling. The FTDO estimates lumped disturbances with finite-time convergence to a bounded residual neighborhood, enabling reduced control gains that mitigate chattering and actuator wear. Lyapunov-based analysis establishes finite-time reachability of the sliding manifold and guarantees that the tracking error converges to a bounded residual set in finite time. The Zeno-free operation is guaranteed by a strictly positive minimum inter-event time analytically derived from system dynamics. Simulations under three ABL scenarios, including Dryden turbulence, wind gusts, and sinusoidal disturbances, demonstrate formation tracking RMSE reductions of up to 29.4%, disturbance estimation RMSE reductions of up to 54.3%, and communication-event reductions of 46.4–63.2% compared with benchmark schemes. These results confirm accurate formation tracking, efficient communication, and robust multi-agent networking under challenging wind conditions, making the framework suitable for networked UAV applications in complex environments. Full article

This paper presents an approach for tracking a person’s position by integrating inertial measurement unit (IMU) sensor values, utilising a particle filter with known physical constraints, such as a map of the space. While such approaches are well established, the effect of constraint choice in the absence of observation measurements remains poorly understood. The effect of varying the tolerance of these constraints is investigated with data collected from a Movella DOT held by a human participant walking around a 100 m running track. In particular, the dimensions of the map are varied, along with the shape. Results showed (a) that it is viable to correct particle filter error without external sensor feedback, provided constraints are provided, with a mean error of 1.8 m, and (b) there is a minimum acceptable tolerance of map width around the edge of the true activity zone, in this case approximately 8 m, and that tightening the map boundary further than this can counterintuitively lead to reduced accuracy. Full article

Conventional maize fertilization suffers from uneven distribution, fertilizer waste, and environmental pollution. To address these issues and achieve precision fertilization for maize, a straight grooved-wheel fertilizer apparatus (SGWFA) was designed and optimized using the discrete element method (DEM). The blocking characteristic of the SGWFA was also evaluated. The optimal configuration (eight grooves, inner diameter of 26 mm) yielded a minimum discharge uniformity coefficient of variation of 2.50% and mild blocking, with a maximum total force of 161.884 N. Furthermore, a nonsingular terminal sliding mode control (NTSMC) algorithm was proposed for the speed loop of the brushless DC (BLDC) motor drive, while the current loop used conventional proportional-integral (PI) control. The overall system achieved dual closed-loop speed and current regulation with finite-time convergence of the speed tracking error. Simulations showed that, compared with conventional PI and fuzzy PI controllers, NTSMC had the smallest overshoot of 3.4%, the shortest settling time of 0.165 s, and the fastest disturbance rejection. Bench tests confirmed that the coefficient of variation under NTSMC was 2.85%, markedly better than fuzzy PI’s 3.15% and conventional PI’s 4.03%. It is also basically consistent with the simulation results. Field tests at 6, 9, and 12 km/h demonstrated over 95% per-row fertilization accuracy, with a maximum relative error of only 4.61%. This integrated system can effectively achieve precise fertilizer application under variable field conditions. Full article

Soft pneumatic robotic joints driven by low-cost on/off solenoid valves are attractive for lightweight and compliant robotic systems, but precise control remains challenging because continuous actuation commands must be realized through discrete valve states subject to minimum pulse-width constraints. This paper presents a model-based constrained equivalent-control PWM (C-EC) framework for a dual-chamber bellows actuator driven by four on/off valves. An ideal duty ratio is derived so that the averaged differential pressure rate matches the desired value required to impose first-order inner-loop error dynamics. To make this law physically implementable, the ideal duty is projected onto the feasible duty set determined by the minimum reliable pulse width of the valves. The resulting duty projection error is explicitly incorporated into a Lyapunov-based analysis, yielding a uniform ultimate boundedness result for the closed-loop system under the proposed implementation and an analytical comparison with conventional discrete sliding-mode control (D-SMC). The valve flow model is parameterized through PWM step-test-based sonic conductance identification. The proposed framework is implemented on a custom 1-DOF rotary joint based on an aluminum-film spiral-duct bellows actuator. Experiments show that C-EC does not uniformly dominate D-SMC over all operating conditions, but it improves $e_{\mathrm{RMS}}$ and $R_{\Delta P}$ in the medium-to-large positive-step regime and in long-hold regulation. In the representative 45°–65°–45° step-hold test, C-EC reduced the RMS tracking error by 39.3% and the differential pressure ripple by 34.5% relative to D-SMC. In the 65° long-hold test, the RMS tracking error and pressure ripple were further reduced by 35.4% and 37.9%, respectively. A loop-period comparison also showed that a 10 ms control period reduced duty projection and pressure ripple relative to 5 ms without degrading tracking accuracy. Full article

A new class of modified minimum state error variance (MSEV) state-space based optimal linear quadratic Gaussian (LQG) regulators for closed-loop structures with estimated feedback has been proposed in this article. The negative feedback path is designed as the cascade of the digital LQG regulator and discrete Kalman state observer. The proposed design enables tracking of a time-varying reference input using the predictive control approach. Moreover, the proposed tracking method utilizes a multivariable continuous-time Cauchy state-space model of nonlinear, nonstationary dynamic systems. The resulting control strategy is approximately optimal, as the optimality of the LQG design holds locally for each linearized model around the respective operating point and does not extend to the global nonlinear system. In this sense, starting from the prespecified nominal state trajectory to be tracked, a numerical optimization procedure minimizing the squared tracking error at each step by using the Nelder–Mead direct search simplex algorithm under the required constraints on the input signal has been developed. The LQG regulator and Kalman state observer are designed by utilizing the linear discrete-time state variable models that properly approximate the nonlinear system dynamics across the nominal state trajectory. The performance of the proposed design is validated by simulating a six-degree-of-freedom nonlinear aircraft model across typical flight regimes. Full article