Search Results (4,027)

The extended Kalman filter (EKF) remains one of the most widely used tools for state estimation, tracking, forecasting, and data assimilation in nonlinear stochastic dynamical systems. This paper does not propose a replacement for the EKF or a modification of the filter itself. Instead, it investigates how the classical EKF may fail when used as a default estimation tool in a unified but practically interpretable nonlinear tracking problem. A stochastic moving-target observation model with angular and range measurements from two identical independent radar channels co-located at the origin of the coordinate system is used as the test environment. To the standard EKF scheme, we add only the technique of linear pseudomeasurements: the EKF is kept in its classical recursive form, and only the observation representation is changed, while the filtering algorithm itself remains unchanged. Within this framework, several systematic model modifications are considered: inaccurate state initialization, absence of prior information about the mean motion parameter, jump-like changes of motion parameters, and incorrect specification of observation-noise characteristics. The experiments show that EKF instability is not limited to explicit divergence. It may also appear as hidden degradation of estimation quality, coordinate-selective failure, physically counterintuitive accuracy behavior, and cases in which the filter remains formally bounded but performs worse than a simple direct estimate based on current measurements. In several experiments, unstable behavior becomes visible only when the Monte Carlo sample size is increased. The results provide a classification of qualitatively different EKF failure modes and support practical diagnostic criteria for testing EKF applicability in nonlinear observation models. Full article

The complex dispersion and modal sensitivity of an axisymmetric transverse magnetic surface wave supported by a dielectric-coated perfectly conducting cylinder are investigated. Starting from Maxwell’s equations, the boundary-value problem is reduced to a nonlinear complex dispersion equation for the longitudinal propagation constant $\beta$. A numerical framework combining zero-level localization of the real and imaginary parts of the dispersion function, nonlinear root refinement, numerical clustering, and adaptive continuation in the complex coating permittivity is used to identify and track a selected spectral branch. One- and two-parameter computations characterize the mapping $\epsilon \mapsto \beta \left(\epsilon \right)$ over prescribed subsets of the complex-permittivity plane. At fixed $\Im \epsilon$, increasing $\Re \epsilon$ increases $\Re \beta$ and decreases $\Im \beta$, whereas at fixed $\Re \epsilon$, increasing $\Im \epsilon$ increases both components of $\beta$ in the investigated parameter range. The rectangular-grid, concentric-circle, and radial-beam experiments show that the spectral response is smooth on the considered parameter sets but non-affine, coupled, and direction-dependent. The corresponding longitudinal electric field is reconstructed, normalized, and phase-aligned along the tracked branch. Difference fields, radial localization measures, a global modal distance, and a normalized correlation coefficient show that the same qualitative radial TM mode is retained throughout the sampled parameter domain, while its propagation constant and spatial localization vary continuously with the complex coating permittivity. Full article

To complete scientific experiments on an underwater tension leg platform, a new cable-driven underwater vehicle is proposed, which is subjected to not only unknown underwater disturbances but also time-varying nonlinear cable tractions. To achieve displacement tracking control despite the high-order nonlinearities and matched and mismatched uncertainties with unknown upper bounds, a radial basis function neural network-based adaptive multiple-surface sliding control strategy (RBFNN-AMSC) is proposed. Utilizing a backstepping design procedure and the Lyapunov approach, the system is decomposed into six subsystems, and the stability is ensured. By employing multiple-surface sliding mode control and exponential reaching law design, the robustness and convergence rate of each subsystem are improved. Moreover, with an adaptive radial basis function neural network, the influences of matched and mismatched uncertainties are compensated, which improves the system anti-jamming capability and avoids the “differential explosion” problem. To verify the effectiveness of the proposed approach, numerical simulations are carried out under different conditions, which show that the proposed control strategy can achieve accurate displacement tracking control regardless of complex nonlinear dynamics and various unknown external disturbances. Full article

Mode veering is a modal-interaction phenomenon found in vibration systems. For inertially coupled structures, the combined influence of coupling delay, nonlinear restoring force, and stochastic coupling perturbation remain insufficiently understood. This work analyzes an inertially coupled two-coordinate prototype in which a discrete delay, a delayed cubic stiffness, and positive multiplicative stochastic modulation all enter through the same relative-coordinate coupling channel. We formulate the delayed linear spectrum through a quasi-polynomial characteristic equation. We also characterize the veering by the two positive-frequency characteristic-root branches descending from the mechanical modes. Coupling delay shifts the veering center, alters the minimum frequency gap, and moves the tracked rightmost roots toward the stability boundary. An analytical imaginary-axis-crossing criterion is derived to determine the delay-induced stability boundary of the deterministic linearized system, and the resulting boundary is independently validated by direct multi-start characteristic-root searches and Chebyshev-collocation approximation of the DDE generator. A fixed-reference modal-coordinate representation identifies the off-diagonal modal terms associated with branch exchange while retaining the full delayed characteristic equation. A first-harmonic treatment of the delayed cubic term can yield an amplitude-dependent nonlinear veering backbone. For the stochastic problem, frozen lognormal coupling samples and a time-dependent Ornstein–Uhlenbeck-driven multiplier are constructed from the same unit-mean positive lognormal marginal law. The former is used to quantify realization-wise spectral broadening, whereas the latter retains temporal correlation and is used to evaluate finite-time branch residence and pathwise delayed-work statistics. The pathwise energy balance reveals that the delayed relative-coordinate work rate is sign-indefinite. This provides a common energy-transfer mechanism through which delay, nonlinearity, and stochastic modulation reshape mode veering in the inertially coupled system. Full article

Blisk, as a critical component of aero−engines, is prone to fatigue cracks that can severely impair its dynamic performance and operational safety. This paper develops a finite element model of a blisk with breathing cracks to investigate the effects of crack distribution (adjacent blades vs. separated blades) and blade twist angle on natural frequencies, frequency−veering and mode localization, and response localization under varying operating conditions. Resonance vibration tests are conducted on blisk and cracked blisk specimens using the frequency dwell method to obtain natural frequencies and vibration responses, thereby experimentally validating the numerical model. The results indicate that cracks reduce the global stiffness, leading to a decrease in natural frequencies and modal localization phenomena. Furthermore, in the frequency−domain responses, nonlinear components such as sub−harmonics, ultra−sub−harmonics, and super−harmonics are observed in addition to the harmonics of the excitation frequency. Moreover, the cracked blisk with a 45° blade twist angle exhibits typical nonlinear vibration behaviors and the most pronounced response localization. This study provides a basis for crack monitoring and resonance fatigue warning. Full article

Wide- and narrow-band composite-axis servo systems in inter-satellite laser communication face critical challenges in balancing high-precision dynamic tracking and strong robust anti-disturbance performance under the coupling effect of high-speed inter-satellite relative motion and multiple strong disturbances. To address this issue, this paper proposes a composite control method integrating adaptive non-singular terminal sliding-mode control and a nonlinear extended state observer. First, a full-link dynamic model covering electromechanical coupling and inter-axis disturbance transmission is constructed to accurately quantify the disturbance characteristics of coarse- and fine-tracking loops. Second, a third-order nonlinear extended state observer is designed to realize real-time high-precision estimation and feedforward compensation of lumped disturbances. On this basis, a self-consistent adaptive non-singular terminal sliding-mode control law is formulated. Under the explicitly stated observer-residual and reaching-phase assumptions, the ideal continuous model provides finite-time convergence of the sliding variable and tracking error. Finally, a wide- and narrow-band cooperative strategy based on error frequency division is introduced to achieve complementary performance between large-stroke coarse tracking and ultra-high-precision fine tracking. Numerical simulations yield a steady-state tracking-error point estimate of 0.30 μrad and a 20 dB disturbance-suppression bandwidth of 1200 Hz. In the semi-physical dynamic-tracking test, the proposed controller limits the peak error to 1.2 μrad; the instrument-only expanded uncertainty of the detector output is estimated as 0.12 μrad (coverage factor k = 2). At the reported evaluation points, the proposed method outperforms PID, conventional sliding-mode control, and linear active-disturbance-rejection control. Deterministic robustness simulations also show smaller tracking errors and shorter recovery times under parameter perturbation, actuator saturation, and temporary link occlusion. No Monte Carlo loss-of-lock probability is claimed. Full article

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

Mobile robots and autonomous vehicles require tightly regulated direct current (DC) power under rapidly varying load conditions, motivating control strategies that combine fast nonlinear regulation with predictive optimization. This paper proposes a Hybrid Sliding Mode Control and Model Predictive Control (Hybrid SMC + MPC) strategy for a DC-DC buck converter supplying a representative mobile-robot mission load. The controller employs a cascade SMC structure for fast inner-loop regulation and an MPC component that provides finite-horizon duty-cycle correction using planned load information. The MPC problem is formulated in condensed form and solved analytically without an external optimization solver. A Lyapunov-based analysis establishes a sufficient reaching condition for the sliding variable under the ideal averaged-model assumptions, and the condition is verified for the simulated mission. The proposed approach is evaluated in MATLAB using a 10-phase, 10 s load profile with resistance varying from 7 Ω to 100 Ω and is compared with SMC-only, MPC-only, PID, constant-duty, and reconstructed fuzzy-logic benchmarks. In the averaged-model study, the Hybrid SMC + MPC achieves a maximum absolute voltage deviation of 0.388 V, an RMSE of 0.0115 V, and a final-phase mean absolute error of 0.0076 V. It provides the lowest maximum voltage deviation among the principal closed-loop controllers, while PID achieves the lowest RMSE and final-phase error and SMC-only exhibits the shortest mean settling time. Relative to MPC-only, the Hybrid controller reduces the maximum voltage deviation by approximately 43.6% and the mean settling time by approximately 66.1%. An ablation study shows that the MPC contribution substantially improves overall and steady-state regulation accuracy, while load preview primarily reduces the worst-case voltage deviation. Switching-level MATLAB/Simulink validation with explicit 20 kHz PWM and converter parasitics confirms that the output remains within ±2% of the 25 V reference throughout the complete mission, with a maximum absolute deviation of 0.443 V and a maximum steady-state switching ripple of 21.6 mV peak-to-peak. These results demonstrate that the proposed Hybrid SMC + MPC architecture provides a favorable balance between worst-case transient regulation, steady-state accuracy, and predictive control capability for dynamically varying robotic power loads. Full article

To improve trajectory estimation for highly maneuvering aerial targets under time-varying noise, an adaptive multiple-model filtering framework is developed based on Kalman filtering. The framework integrates online model-probability updating, higher-order error-propagation correction, and adaptive noise adjustment to accommodate motion-mode transitions, nonlinear estimation errors, and measurement uncertainty. Simulation results show that, at a relative velocity of $800 \mathrm{m}/\mathrm{s}$, the proposed method reduces the position root-mean-square error (RMSE) by $57.73\%$ compared with the raw measurements and outperforms the individual motion-model filters. A field-programmable gate array (FPGA)-based laboratory validation platform is further developed, and the experimental results are consistent with the simulation results. The measured single-frame processing latency is $152 \mathsf{\mu }\mathrm{s}$, demonstrating the effectiveness and real-time feasibility of the proposed framework for maneuvering-target trajectory estimation. Full article

Reliability assessments of subsea systems are generally performed at two levels: structural reliability analysis of individual components and functional reliability analysis of the overall system using generic failure-rate databases. This study develops a component-to-system multi-scale framework that integrates these two levels for a subsea separation system operating at 3000 m water depth. At the component level, a Gaussian process regression (GPR) surrogate is developed from 474 finite element simulations of a vertical gravity separator. First-order reliability method (FORM) and Monte Carlo simulation (MCS) are then employed to assess the structural reliability, followed by a time-variant reliability analysis that accounts for corrosion effects. At the system level, the structural reliability model is integrated with functional failure rates through a Bayesian network that considers five equipment items and relevant risk-influencing factors. The surrogate model accurately predicts collapse pressure with an R² value of 0.996. The intact separator achieves a reliability index of 4.55, satisfying the DNV high-safety-class target, with the structural failure mode contributing only 0.0034% of the separator failure rate. Under a corrosion rate of 0.4 mm/year, the reliability index decreases to 3.12 over a 25-year service period. The structural failure rate crosses the DNV medium-safety-class target of 10⁻⁴ per year at year 12, increasing the structural contribution to the overall system failure frequency to 0.33%. Sensitivity analysis indicates that initial ovality and wall thickness are the most influential parameters affecting structural reliability and should therefore be prioritized in design and integrity management strategies. The framework is demonstrated on this physics-consistent dataset; validation against independent nonlinear finite element analyses and experimental collapse data is identified as the necessary next step before the results are used for design. Full article

We develop a reaction–diffusion–chemotaxis model for spatial tumour–immune–chemokine dynamics that couples logistic tumour growth, immune-mediated killing, chemokine-dependent immune recruitment, chemotactic migration, and signal production. For the non-dimensional system, we establish local classical solvability, nonnegativity, a uniform tumour-density bound, and global mass estimates for the immune and chemokine components. The tumour-free equilibrium is stable precisely when the baseline immune-control index satisfies ${\sigma }_0/\delta >1$, whereas positive homogeneous coexistence is characterized by a scalar nonlinear equation. Linearization in the Neumann Laplacian eigenbasis yields a mode-dependent cubic dispersion relation, showing that chemotaxis does not alter the tumour-invasion threshold but can destabilize homogeneous coexistence through a finite-wavelength oscillatory instability above a critical sensitivity ${\xi }_c$. A conservative finite-volume discretization with upwind chemotactic fluxes and implicit backward differentiation formula time integration is used to test these predictions. Numerical experiments recover the analytical equilibria and growth rates, identify the dominant unstable mode, reproduce the transition to spatial heterogeneity, and quantify the effects of immune recruitment, decay, and diffusion on the stability boundary. Grid-refinement, mass-balance, residual, and nonnegativity diagnostics support the computational reliability of the results. Full article

The Luzon Strait is a critical generation site for global internal tides. Their generation and propagation are significantly modulated by background currents, including the Kuroshio Current and mesoscale eddies. This study investigates nonlinear effects of these background currents on low-mode (modes 1–3) internal tides using a high-resolution numerical simulation. We apply the Taylor–Goldstein equation considering the Earth’s rotation and background currents to perform modal decomposition, and utilize a nonlinear internal tidal energy equation to quantify three crucial energy pathways: inter-modal energy conversion, nonlinear energy exchange with background currents, and nonlinear advection effects. Results demonstrate that while stationary mode-1 internal tides dominate in the generation region of the Luzon Strait, non-stationary energy increases significantly in the western and eastern propagation regions, driven largely by seasonal variability of the Kuroshio Current. Inter-modal energy conversion follows a cascade from lower to higher modes, with conversion efficiency increasing with mode number. Nonlinear exchanges between background currents and internal tides are one order of magnitude smaller than inter-modal conversions but exhibit a bidirectional transfer, where advection redistributes internal tidal energy within the eddy structures. This study provides a quantitative framework for understanding multiscale energy pathways of internal tides under complex ocean dynamics. Full article

Overlapping optical cells create geometry-dependent inter-cell interference, while receiver-geometry and channel-estimation errors can reverse the effective non-orthogonal multiple access (NOMA) decoding order and increase successive interference cancelation (SIC) failures. This paper develops a chance-constrained receiver–scheduler co-design framework for a multi-cell NOMA visible-light communication network with an asymmetrically clipped DC-biased optical orthogonal frequency-division multiplexing physical layer. Correlated position, photodetector-orientation, and channel-estimation errors are propagated through nonlinear geometry-based scenarios. For each SIC direction, a joint three-SINR event defines a layer-, resource-, and direction-labeled probabilistic decodability graph. Candidate NOMA and orthogonal modes are screened on optimization scenarios, admitted by independent one-sided confidence bounds, and selected through resource-constrained mixed-integer linear programming. With the matching fixed, hierarchical powers are adapted under empirical conditional-value-at-risk constraints using trust-region sequential quadratic programming. Because candidate-edge certificates need not remain valid after global matching and power redistribution, the frozen complete assignment is independently recertified before held-out testing. Under the specified uncertainty generator, the proposed method maintains selected-pair outage probabilities of approximately $2.7\times {10}^{-3}$–$3.3\times {10}^{-3}$ over the half-power-angle sweep, compared with $0.027$–$0.060$ for nominal-CSI allocation. Additional experiments quantify network-wide outage, model misspecification, unbalanced deployments, feasibility, and computational cost. The results support reliable slow-timescale scheduling under the adopted link and uncertainty models, without implying distribution-free, waveform-level, or real-time guarantees. Full article

Electromechanical thrust-vector control systems are subject to friction, backlash, and configuration-dependent dynamics that are difficult to model explicitly, motivating controllers that adapt from operational data without requiring an identified plant. This paper proposes a data-driven, disturbance-observer-based controller in actuator space for a dual-axis thrust-vectoring platform. The controller operates on a sliding surface defined over the actuator tracking error and uses an adaptive input matrix gain together with a lumped disturbance observer, both of which are updated from encoder and control data through gradient descent applied to a joint identification loss. The Newton–Euler equations of the mechanical system are projected onto the actuator coordinates through the angular-velocity map, yielding a structurally well-posed actuator-space model, which is used to design the adaptive gain; nonetheless, the dynamic model is never evaluated online. The resulting controller requires only encoder measurements of actuator displacements, a reference trajectory computed from the platform geometry, and bounded normalized commands; the mechanism Jacobian and the inertia and Coriolis matrices are not evaluated online. Boundedness of the adaptive gain and disturbance estimate is established, and uniform ultimate boundedness of the tracking error follows under a mild alignment condition. Simulation results on a coupled nonlinear plant and hardware-in-the-loop experiments on a dual-channel actuator testbed are presented, comparing the proposed controller against PID, super-twisting, unit-vector sliding mode, and MFAC baselines on a circular thrust-vector reference. Full article

Accurate prediction of the state trend of pumped storage units (PSUs) is essential for timely anomaly detection and preventive maintenance to improve the overall economic performance of power plants. Nevertheless, the complex and time-varying characteristics of PSU vibration data increase the difficulty of accurately modeling their dynamic evolution. In response to this problem, an integrated vibration trend prediction (VTP) method for PSUs is developed by combining a temporal-enhanced generative adversarial network (TEGAN) with an improved bidirectional long short-term memory network (IBiLSTM). Firstly, TEGAN expands the original dataset by synthesizing artificial samples, thereby improving the structural diversity and representativeness of vibration data. Within TEGAN, a temporal characterization (TC) module is designed to collaboratively guide the generator and the discriminator, while a data processing module is adopted to incorporate structural priors into the learning process. Secondly, variational mode decomposition (VMD) is applied to decompose the original vibration data into intrinsic modes, followed by PSR to reconstruct the components of each modality into a higher-dimensional state space representation. Subsequently, by incorporating the proposed multi-order Kolmogorov–Arnold network (M-KAN) for high-order nonlinear fitting, IBiLSTM is employed to model each reconstructed sub-sequence. Finally, the outputs of all sub-sequences are aggregated to produce the final VTP results. The comparative evaluation verifies the advantages of the developed method in terms of prediction accuracy and robustness for PSU vibration trend forecasting. Full article