Search Results (2,326)

This study addresses the control challenge of ventilated supercavitating vehicles during depth-change maneuvers, where variations in speed and depth induce unsteady cavity evolution and nonlinear planing forces. An unsteady cavity evolution model based on the independent cross-sectional expansion principle was developed and integrated with vehicle dynamics to form a heterogeneous coupled motion framework. A DQN-based controller was designed to maintain cavity length under unsteady conditions, while an ADRC-based pitch controller achieved decoupled attitude control, with depth tracking realized through cascaded outer-loop feedback. Numerical simulations were performed on the established heterogeneous coupled motion model under depth-change maneuvers. The results show that the proposed approach maintains the cavity length within ±10% of the commanded value and achieves rapid and stable depth tracking. The proposed modeling and control framework offers an effective approach to enhance the maneuverability and robustness of ventilated supercavitating vehicles in complex hydrodynamic environments. Full article

The effect of third-order shear deformation theory (TSDT) on thick functionally graded material (FGM) spherical shells under sinusoidal thermal vibration is investigated by using the generalized differential quadrature (GDQ) numerical method. The TSDT displacement field and an advanced nonlinear shear correction coefficient are used to derive the equations of motion for FGM spherical shells. The simple stiffness of FGM spherical shells under a temperature difference along the linear vs. z-axis direction is considered in the heat conduction equation. The dynamic GDQ discrete equations of motion subjected to thermal load and inertia terms can be expressed in matrix form. A parametric study of environmental temperature, FGM power-law index, and advanced nonlinear shear correction on thermal stress and displacement is conducted under the vibration frequency of a simply homogeneous equation and applied heat flux frequency. This is a novel method for obtaining the numerical GDQ results, comparing cases with linear and advanced nonlinear shear correction. The novelty of the present work is that an advanced varied-value type of shear correction coefficient can be successfully used in the thick-walled structure of FGM spherical shells subject to thermal vibration while considering the nonlinear term of TSDT displacements. The purpose of the present work is to investigate the numerical thermal vibration data for a two-material thick FGM spherical shell. Full article

The skyrmion racetrack is a promising concept for future information technology. The primary issues with skyrmion racetrack memory are now error codes and Hall motion. Here, we propose a skyrmion pair racetrack memory. The Oersted fields generated by the non-contact current-carrying wire in the middle of the magnetic nanostrip stabilize the skyrmion pairs in the nanostrip, which are separated by a naturally formed domain wall. Through numerical models and micromagnetic simulations, we demonstrate that such a skyrmion pair can produce linear Hall motion along the nanostrip under the linear control of the Oersted field gradient. These findings offer a high-reliability method for skyrmion racetrack memory and a more efficient approach to designing devices that use the skyrmion Hall effect. Full article

The frequency content of strong ground motion significantly affects the response of engineered systems under seismic excitation. Among some scalar parameters which exist in the literature, the mean period T_m has proved to be the most efficient. Ground Motion Predictive Equations (GMPEs) are usually developed for ground motion parameters through the calibration of coefficients of predefined functional forms, via linear or nonlinear regression, and based on recorded ground motion data. Such expressions of T_m are rare in the literature. Recently, the use of machine learning (ML) algorithms in earthquake engineering and engineering seismology has increased. The Artificial Neural Network (ANN) is an effective ML algorithm which has already been explored for the development of GMPEs for amplitude-based ground motion parameters. Within the work presented herein, multiple nonlinear regression (NLR)- and ANN-based GMPEs are developed for T_m using the latest strong motion database for shallow earthquakes in Greece. To the author’s knowledge, the implementation of ANN for producing GMPEs for T_m for shallow earthquake events has not been explored. Direct comparison between the NLR- and ANN-based GMPEs is performed, in terms of performance indexes, aleatory uncertainty, and working examples, as well as testing against earthquake events not included in the original dataset. The results reveal that the ANN-based GMPEs are useful in reducing aleatory uncertainty, although care should be taken in their implementation to avoid overfitting issues. Full article

This paper presents a study on the nonlinear vibrations in the impact-echo (IE) method for void flaw detection of solid structures. Linear theory has historically served as the foundational framework for non-destructive methods, including the IE method, particularly for estimating flaws in solids. This paper gives a comprehensive analysis of the nonlinear theory behind the IE method for detection of voids in solids such as concrete structures. The general equation of motion is presented for the flexural vibration of a void-defected solid with general nonlinear constitutive material properties, and then the simplified solutions for polynomial nonlinearity and hysteresis nonlinearity are derived comprehensively. The solutions of principal frequency and sub- and super-harmonics as well as the frequency of combined modes are elaborated, and the theoretical formula of resonant frequency shift with amplitude is derived. As conventional nonlinear IE methods have been conducted by only using a phenomenological model of linear shift in resonant frequency with amplitude, the proposed new frame of nonlinear vibration theory can be used to implement the IE method more comprehensively and accurately for void detection in solids. Full article

This work investigates the stability analysis of linear control systems defined on Lie groups, with a particular focus on low-dimensional cases. Unlike their Euclidean counterparts, such systems evolve on manifolds with non-Euclidean geometry, where trajectories respect the group’s intrinsic symmetries. Stability notions, such as inner asymptotic, inner, and input–output (BIBO) stability, are studied. The qualitative behavior of solutions is shown to depend critically on the spectral decomposition of derivations associated with the drift, and on the algebraic structure of the underlying Lie algebra. We study two classes of examples in detail: Abelian and solvable two-dimensional Lie groups, and the three-dimensional nilpotent Heisenberg group. These settings, while mathematically tractable, retain essential features of non-commutativity, geometric non-linearity, and sub-Riemannian geometry, making them canonical models in control theory. The results highlight the interplay between algebraic properties, invariant submanifolds, and trajectory behavior, offering insights applicable to robotic motion planning, quantum control, and signal processing. Full article

The fluctuation in the rotational speed of the inner ring can lead to significant instability in the motion of both the inner ring and the cage of rolling bearings. This instability seriously impacts the operational performance and service life of the bearings. In this paper, a nonlinear dynamic model of a fully flexible angular contact ball bearing was established by comprehensively considering various nonlinear factors, including elastic contact relationships, internal collisions, friction, and clearance. The dynamic characteristics of the inner ring and cage under sinusoidal rotational speed fluctuations were studied. The effects of amplitude and frequency of rotational speed fluctuation of the inner ring on the motion stability of the inner ring and cage were analyzed. The results show that a greater the fluctuation amplitude leads to a higher the fluctuation amplitude in the cage’s rotational speed curve, while a higher fluctuation frequency correlates with an increased frequency in the cage’s rotational speed curve. These results indicate that increases in both the amplitude and frequency of rotational speed fluctuations result in more pronounced oscillations of the inner ring. The validity of the model was confirmed by comparing the LS-DYNA results with the analytical results and experimental results. The research findings can provide a theoretical foundation for enhancing motion stability and optimizing design of the bearings. Full article

Self-oscillating systems are capable of transforming ambient energy directly into mechanical output, and exploring novel designs is of great value for energy harvesters, actuators, and engine applications. The inspiration for this study is drawn from the four-stroke engine; we designed a new self-rotating engine formed by a turnplate, a hinge, and an LCE fiber, operating with steady illumination applied. To analyze its rotation dynamics, a nonlinear theoretical framework was formulated constructed with the dynamic LCE model as a framework. The central discovery is that the light-driven LCE engine can operate in three distinct states under steady illumination—static equilibrium, pendulum-like oscillation and sustained self-rotation—switching between them through a supercritical Hopf bifurcation. The persistence of both the pendulum and rotary motions stems from an energy balance in which the positive work produced by photo-induced contraction of the LCE fiber is exactly offset by damping dissipation, while oscillation amplitude and rotation frequency are strongly governed by light intensity, contraction coefficient, damping coefficient, spring constant and turntable radius. Compared with many previously reported self-oscillating designs, the present self-rotating engine is distinctive for its lightweight and simple configuration, tunable size, and rapid operation. These features enable compact integration and broaden its potential applications in micro-scale systems and devices. The advancement in artificial muscles, medical instruments and micro sensors is strongly promoted by this, making it possible to create devices that are both smaller in size and superior in functionality. Full article

This study quantifies the influence of soil–structure interaction (SSI) on key parameters of performance-based seismic design (PBSD) for steel moment-resisting frames. Specifically, PBSD is extended as a methodology in which explicit structural performance levels, such as immediate occupancy, damage limitation, life safety, and collapse prevention, serve as the basis for sizing and detailing structural members under specified seismic hazard levels, instead of traditional force-based design. The PBSD framework is further developed to incorporate SSI by adopting a beam on a nonlinear Winkler foundation model. This model captures the nonlinear soil response and its interaction with the structure, enabling a more realistic design framework within a performance-based context. To evaluate and quantify the influence of SSI in the PBSD method, an extensive parametric study is performed using 100 far-field ground motions, categorized into four groups (25 records each) corresponding to EC8 soil types A, B, C, and D. Nonlinear time history analyses reveal consistent trends across the examined frames. When SSI is neglected, the fundamental natural period (T) is systematically underestimated by approximately up to 3.5% on EC8 soil type C and up to 15% on soil type D. As a result, the base shear and the mean values of maximum interstorey drift ratios (IDRs) are overestimated compared to cases accounting for soil flexibility, with the largest drift discrepancies observed in frames with eight or more storeys on soil D. The analyses further reveal that softer soils (e.g., Soil D) lead to significantly higher q values, particularly for moderate-to-long period structures, whereas stiffer soils (e.g., Soil B) cause only minor deviations, remaining close to fixed-base values. A complementary machine learning module, trained on the same dataset, is employed to predict base shear, maximum IDR, and the behavior factor q. It successfully reproduces the deterministic SSI trends, achieving coefficients of determination (R²) ranging from 0.986 to 0.992 for maximum IDR, 0.947 to 0.948 for base shear, and 0.944 to 0.952 for q. Feature importance analysis highlights beam and column ductility, soil class, and performance level as the most influential predictors of structural response. Full article

Traditional cooperative navigation algorithms for multiple AUVs are typically designed for a single specific configuration, such as parallel or leader-slave. This paper proposes a novel cooperative navigation algorithm based on factor graph and Lie group to address the multi-AUV localization problem, which is applicable to various multi-AUV configurations. First, the motion state of an AUV is represented within the two-dimensional special Euclidean group (SE(2)) space from Lie theory. Second, the motion of the AUV and acoustic-based range and bearing measurements are modeled to derive the motion error function and the range and bearing error function, respectively. Depending on the formulation of the motion error function, the proposed approach comprises two methods: Method 1 and Method 2. Third, the Gauss-Newton method is employed for nonlinear optimization to obtain the optimal estimates of the motion states for all AUVs. Finally, a parameter-level simulation system for AUV cooperative navigation is established to evaluate the algorithm’s performance under two different multi-AUV configurations. Method 1 is designed for parallel configurations, reducing the average RMSE of position and orientation errors by 29% compared to the EKF. Method 2 is tailored for leader-slave configurations, reducing the average RMSE of position and orientation errors by 38% compared to the EKF. Simulation results demonstrate that the proposed algorithm achieves superior performance across different AUV configurations compared to conventional EKF-based approaches. Full article

The Magnetic Suspension Balance System (MSBS) serves as a core apparatus for interference-free aerodynamic testing in wind tunnels, where its high-precision levitation control performance directly determines the reliability of aerodynamic force measurements. This paper addresses the strong coupling issues induced by rigid-body motion in the MSBS vertical suspension system and proposes a decoupling control framework integrating classical decoupling methods with geometric feature transformation. First, a nonlinear dynamic model of the six-degree-of-freedom MSBS is established. Through linearization analysis of the vertical suspension system, the intrinsic mechanism of displacement-pitch coupling is revealed. Building upon this foundation, a state feedback decoupling controller is designed to achieve decoupling among dynamic channels. Simulation results demonstrate favorable control performance under ideal linear conditions. To further overcome its dependency on model parameters, a decoupling strategy based on geometric feature transformation is proposed, which significantly enhances system robustness in nonlinear operating conditions through state-space reconstruction. Finally, the effectiveness of the proposed method in vertical suspension control is validated through both numerical simulations and a physical MSBS experimental platform. Full article

A specific challenge in robotic control applications is the identification and regulation of actuators that provide mechanical traction and motion to the robot links. The design of actuator control laws, grounded in parametric identification and experimental motor characterization, enables numerical simulations to explore diverse operating scenarios. This article presents the initial phases in the development of a robotic rehabilitation system, focused on the kinematic modeling of a parallelogram-configuration robot for upper-limb therapy, the fuzzy identification of its actuators, and their closed-loop evaluation using a fuzzy Parallel Distributed Compensation (PDC) controller with state feedback (Ackermann), whose poles are optimized via the Grey Wolf Optimizer (GWO) metaheuristic. This controller was selected for its congruence with the nonlinear universe of discourse defined by the identified model, a key feature for operation within specific functional ranges in medical applications. The simulation and hardware platform results provide evidence that fuzzy dynamic models constitute a valuable tool for application in rehabilitation systems. This work serves as a foundation for future physical implementations with the fully coupled robotic system, in order to ensure operational safety prior to the start of clinical trials. Full article

This paper presents a stability analysis of single-channel, slow-rolling, Semi-Automatic Command to Line of Sight (SACLOS) missiles using a comparison of the Routh–Hurwitz and the Frank–Wall stability criteria and a nonlinear analysis. Beginning with a six-degree-of-freedom (6-DOF) model in the Resal frame, a linearized model for the commanded motion is developed. This linearized model, which features complex coefficients due to the coupling of longitudinal channels in rolling missiles, is used to define the structural scheme of the commanded object and its flight quality parameters. The guidance kinematic relations, guidance device equations, and actuator relations, incorporating a switching function specific to slow-rolling, single-channel missiles, are also defined and linearized within the Resal frame to construct a comprehensive structural diagram of the SACLOS missile. From this, the characteristic polynomial with complex coefficients is derived and analyzed by comparing the Routh–Hurwitz and the Frank–Wall stability criteria. This analysis determines a stability domain for the guidance gain and establishes a minimum limit for the guidance time. The stability domain defined through the linear model is then validated using a nonlinear model in the body frame. Full article

In ocean engineering, path following serves as a fundamental capability for autonomous underwater vehicles (AUVs), enabling essential operations such as environmental exploration and inspection. However, for robotic dolphins employing dorsoventral undulatory propulsion, the periodic pitching induces strong coupling between propulsion and attitude, posing significant challenges for precise path following in disturbed environments. In this paper, a real-time robust path-following control framework is proposed for robotic dolphins to address these challenges. First, a novel robotic dolphin platform is presented by integrating a dorsoventral propulsion mechanism with a passive peduncle joint, followed by the systematic formulation of a full-state dynamic model. Then, a minimum-snap-based path optimizer is constructed to generate smooth and dynamically feasible trajectories, improving path quality and motion safety. Subsequently, a robust model predictive controller is developed, which incorporates control surface dynamics, a nonlinear disturbance observer, and a Sigmoid-based disturbance-grading mechanism to ensure fast attitude response and precise tracking performance. Finally, extensive simulations under various environmental disturbances validate the effectiveness of the proposed approach in both trajectory optimization and robust path following. The proposed framework not only demonstrates strong robustness in path following and disturbance rejection, but also provides practical guidance for future underwater missions such as long-term environmental monitoring, inspection, and rescue. Full article

Background: The study of non-Newtonian fluids in thin channels is crucial for advancing technologies in microfluidic systems and targeted industrial coating processes. Nanofluids, which exhibit enhanced thermal properties, are of particular interest. This paper investigates the complex flow and heat transfer characteristics of a Sutterby nanofluid (SNF) within a thin channel, considering the combined effects of magnetohydrodynamics (MHD), Brownian motion, and bioconvection of microorganisms. Analyzing such systems is essential for optimizing design and performance in relevant engineering applications. Method: The governing non-linear partial differential equations (PDEs) for the flow, heat, concentration, and bioconvection are derived. Using lubrication theory and appropriate dimensionless variables, this system of PDEs is simplified into a more simplified system of ordinary differential equations (ODEs). The resulting nonlinear ODEs are solved numerically using the boundary value problem (BVP) Midrich method in Maple software to ensure accuracy. Furthermore, data for the Nusselt number, extracted from the numerical solutions, are used to train an artificial neural network (ANN) model based on the Levenberg–Marquardt algorithm. The performance and predictive capability of this ANN model are rigorously evaluated to confirm its robustness for capturing the system’s non-linear behavior. Results: The numerical solutions are analyzed to understand the variations in velocity, temperature, concentration, and microorganism profiles under the influence of various physical parameters. The results demonstrate that the non-Newtonian rheology of the Sutterby nanofluid is significantly influenced by Brownian motion, thermophoresis, bioconvection parameters, and magnetic field effects. The developed ANN model demonstrates strong predictive capability for the Nusselt number, validating its use for this complex system. These findings provide valuable insights for the design and optimization of microfluidic devices and specialized coating applications in industrial engineering. Full article