An experimental campaign was undertaken to demonstrate the applicability of the Bouc–Wen hysteretic model to describe the hysteretic system of the recent Framatome dimple-less spacer grid design for Pressurized Water Reactors. The experimental setup subjected a reduced-length fuel rod, supported at both ends by dimple-less grids, to forced sinusoidal excitation while immersed in water. The resulting frequency–amplitude curves, taken at different excitation amplitudes, exhibited an initial softening and then hardening behavior. This behavior is consistent with recent literature on reduced-length rods supported by traditional grids that feature both springs and dimples. Additionally, there was a nonlinear damping increase observed around the fundamental resonance frequency. To simulate the response to forced vibrations, a numerical model based on a single-degree-of-freedom nonlinear equation was employed. The model includes a Bouc–Wen stiffness term, quadratic and cubic stiffness terms, and an equivalent viscous damping which is a function of the vibration amplitude. Notably, when dimple-less grids were used, the resulting fuel rod vibrations exhibited greater damping compared with traditional grids. These findings contribute to our understanding of fuel rod behavior and provide insights for grid design. The interactions between fuel rods and spacer grids were also investigated using an experimental setup capable of measuring the force exchange and sliding relative motion at the interface of the fuel rods and spacer grids. The main conclusions from these tests indicate that the maximum amplitude of force and slippage at the boundary is directly related to the maximum amplitude of excitation and displacement experienced by rods. When dimple-less grids are used, lower amplitudes of interaction and sliding are reached. This reduction may be beneficial in mitigating fretting-related issues. Furthermore, in the time domain, evidence of detrimental phenomena—such as impacts and losses of contact—is also weaker with the novel Framatome dimple-less grid design.

Robot swarms can be deployed as moving surveillance systems, for instance, as mobile anti-poaching systems for monitoring wildlife and detecting poaching activities. Since poachers have an interest in evading detection, robots are at risk of being hijacked and manipulated to behave antagonistically, for example, to prevent the correct surveillance of a group of animals in a certain area. While the detection of cyber-attacks on robots is commonly considered in the context of network activity and communication, this work focuses on the detection of anomalies that manifest in the robots' physical motion behavior. It builds upon our previous work, where a contextual data-driven anomaly detection method was proposed and applied to motions of a robot swarm monitoring a static area. Such data-driven, unsupervised methods for detecting anomalies in the physical behavior of robots are a suitable way of evading the need to define behavioral signatures or rules but only use information on the behavior of “normal,” cooperating robots, and the method proposed earlier demonstrated a strong performance both for recognizing normal robots and for the detection of a single antagonistic robot within a swarm monitoring a static area. Motivated by anti-poaching scenarios, this article goes beyond the earlier work by considering multiple antagonists and a dynamic scenario, where the robots monitor a continually changing area around a herd of animals and therefore do not converge to fixed, optimal positions as in a static scenario. The statistical evaluation shows that the anomaly detection method consistently yields a correct categorization of normal and antagonistic behavior, and that the method's performance is unaffected by an increased number of antagonistic robots in a swarm. Furthermore, the results indicate that, given the availability of a limited amount of new data, the method can be fine-tuned to efficiently adapt to new settings.

This paper presents a three-layer control architecture designed to enhance vehicle lateral stability under uncertain and varying road conditions. The system utilizes output-feedback-based finite-time controllers to track the desired yaw rate and longitudinal slip in the upper and lower layers, respectively. The middle layer incorporates a finite-time robust dynamic control allocation to distribute longitudinal slips among the tires. This approach effectively handles uncertainties and changing road conditions without the need for direct estimation of unmeasurable variables such as tire-road friction and sideslip angle. The proposed output feedback control law consists of a stabilizer component to ensure finite-time stability, a compensator to eliminate the unknown function in the upper and lower layers, and an auxiliary tracking term. Key advantages of the proposed framework include: no requirement for additional sensors, finite-time convergence, reduced computational complexity compared to optimization-based methods, and the ability to perform finite-time stability analysis for the integrated closed-loop system. The system performance is evaluated using a validated 10-degree-of-freedom vehicle dynamics model and CarSim simulations during a double-lane change maneuver. Simulation results demonstrate the superiority of the proposed control structure over sliding mode control, offering improved tracking accuracy and robust performance under varying road conditions.

In recent years, learning robot manipulator dynamics with deep networks has been extensively studied, as it avoids deriving the analytical expression of the robot dynamics equations. In particular, deep Lagrangian networks that incorporate the prior knowledge of Lagrangian mechanics into the deep networks have shown prominent advantages in robot dynamics modeling. Inspired by this, this paper proposes a robot dynamics modeling approach based on a deep Lagrangian network and a torque separation technique. First, friction modeling is incorporated into deep Lagrangian networks to more comprehensively learn the dynamics of the robot manipulator. Meanwhile, the inputs of deep Lagrangian networks are reconstructed using the prior knowledge of robot joint types, which can entail valuable information for the model learning process that may result in better approximation and generalization. Moreover, a torque separation technique is introduced to extract the inertial–Coriolis–centrifugal torques and gravity–friction torques from the input torques, which allows us to accurately estimate each part of the robot dynamics and helps us understand the in-depth dynamic characteristics of robot manipulators. The simulation results on two-degrees-of-freedom (2-DOF) and 5-DOF robot manipulators demonstrate the feasibility and effectiveness of the proposed dynamics modeling method.

During the morphing process, the dynamic modeling of the morphing wing aircraft presents the characteristics of rigid-elastic coupling effects, moving boundaries, and low computational efficiency. Meanwhile, parameters such as aerodynamic forces/moments, center of pressure, center of mass, and moment of inertia would also change significantly, which would seriously affect the flight stability. Therefore, this paper mainly focuses on the rigid-elastic coupling dynamic modeling of morphing wing aircraft. Based on the virtual power principle, a floating coordinate system and the modal synthesis method are adopted to establish a rigid-elastic coupling dynamic model that accounts for the elasticity of the wings, which can accurately capture the cross-scale coupling effects between the rigid-body motion and the elastic deformation of the wings during the continuously morphing process. Considering the moving boundary problem between the fuselage and the wings during the continuously morphing process, a dynamic constraint modeling technique based on the Craig-Bampton method and a node's life and death technique describing the constraint states are proposed, which can avoid the complex operations of reestablishing the finite element model of the elastic bodies for traditional methods. Moreover, a method of smoothing model for the rigid–elastic coupling system is developed, which can significantly improve the computational efficiency for the dynamic equation of the morphing wing aircraft. And by changing the value of the smoothing factor, preservation of specific high-frequency components can be controlled. Finally, the numerical examples are carried out to verify the proposed rigid-elastic coupling dynamic model for the morphing wing aircraft.

This study used a six-degrees-of-freedom dynamic model to analyze the dynamic response of bimetal spur gears. Aluminum alloy forms the core of the bimetal gears, and a uniform-thickness steel ring surrounds the teeth. Gear mesh stiffness of bimetal gears and the transmission error have been assessed using a numerical approach based on finite element contact analysis. The system steady-state dynamic response is examined in both the frequency and temporal domains. Results indicate a 25%–48% decrease in gear mesh stiffness alongside a rise of 35%–90% in bimetal-gear static transmission error with a weight reduction of 30%–55% compared with an identical steel-gear pair for 2–9 mm ring thickness. Bimetal gears show a reduction of nearly 10%–12% in dynamic factor and 4%–15% in peak-to-peak extreme displacement amplitude compared with steel gears.

This study addresses the trajectory tracking control for manipulator robots with uncertainties. The main objective is to ensure that the robot follows a desired trajectory despite the presence of uncertainties/disturbances and considering control input constraints. The approach is developed in the framework of continuous integral sliding modes. A saturated continuous integral sliding mode controller is used to achieve asymptotic tracking while sustaining input constraints. By suitably designing the sliding variable, the saturated continuous integral sliding mode controller can be expressed in a decoupled input-by-input form. As a result, asymptotic trajectory tracking is ensured, and compensation for uncertainties/disturbances within finite time. Additionally, simulations were carried out for nominal controllers under ideal conditions and in the presence of uncertainties/disturbances. The proposed saturated continuous integral sliding mode approach is compared against other continuous sliding mode control schemes, and their performance is compared using the root-mean-square value. Finally, experimental tests on a 2-DOF manipulator validate the applicability of the proposal.

Research on the failure mechanisms of gears with multiple cracks has gained widespread attention. Among these, the study of the mesh stiffness characteristics of gears with multiple cracks is crucial for optimizing gear design and improving transmission reliability. However, for the case where cracks exist on both sides of a gear tooth, the impact of multiple cracks on stiffness is currently simplified as a “dead zone,” neglecting the differences in the stress characteristics of opened cracks and closed cracks under the mesh force. This simplification leads to lower accuracy in calculating the mesh stiffness of gears with multiple cracks. In this study, a mesh stiffness calculation model for a gear tooth with double cracks is established. The differences in the stress characteristics of opened cracks and closed cracks on both sides of the gear tooth under the mesh force are considered. Using the concept of microelement integration, the irregular geometrical region formed by the closed crack and the tooth profile is mapped to an equivalent rectangle of equal area. A load model for gear teeth with closed cracks is proposed, and the expressions for bending stiffness, shear stiffness, and mesh stiffness of a gear tooth with double cracks are revised. The mesh stiffness of a gear tooth with closed cracks is measured using the noncontact DIC technique to verify the accuracy of the proposed calculation method. The study shows that the weakening effect of closed cracks on bending stiffness and mesh stiffness is smaller than that of opened cracks, and the difference in their effects on stiffness gradually increases with the crack length and inclination angle. For both single-crack and double-crack cases with closed cracks, the theoretical prediction values based on the proposed mesh stiffness calculation method exhibit an error of less than 10% when compared with experimental measurements.

Modern engineering systems require advanced uncertainty-aware model updating methods that address parameter correlations beyond conventional interval analysis. This paper proposes a novel framework integrating Riemannian manifold theory with Gaussian Process Regression (GPR) for systems governed by Symmetric Positive-Definite (SPD) matrix constraints. Our methodology features three key innovations: (1) A semi-definite programming-optimized Minimum Volume Ellipsoid model that explicitly quantifies parameter interdependencies while ensuring computational efficiency; (2) A manifold-embedded GPR surrogate model employing Log-Euclidean kernels to intrinsically preserve SPD constraints during uncertainty updating; (3) A Riemannian gradient optimization scheme that enables efficient parameter updates via logarithmic matrix mapping. Validated through mechanical and aerospace case studies, the framework achieves a Log-Euclidean distance of 3.12 × 10⁻⁴ in uncertainty updating (compared to a baseline of 48.48) and provides a tenfold computational acceleration over Bayesian alternatives. Robustness tests demonstrate stable performance under 5% noise perturbation, with the Log-Euclidean distance increased only marginally to 1.41 × 10⁻². By unifying differential geometry with machine learning, our approach eliminates heuristic projections required in conventional methods while advancing uncertainty quantification through structure-preserving manifold operations. This study bridges geometric consistency, computational efficiency, and physical consistency in uncertainty-aware model updating.

Modern new tanks are difficult to achieve high precision control of the horizontal stabilization system due to electromechanical coupling and road surface disturbance during high-speed combat. A predefined-time dynamic surface controller (DSCPT) is proposed for the fast stabilization and strong disturbance problems of the tank horizontal stabilization system. Based on backstepping control technique, the controller introduces a time-varying tuning function and a first-order filter to construct the dynamic surface error, where the time-varying tuning function ensures that the tracking error is bounded within a predefined time, and the hyperbolic tangent function ensures robustness and the continuity of the control input. Using Lyapunov theory, it is rigorously demonstrated that the control method is capable of achieving tracking error convergence to a predetermined range at any predefined time in the presence of system uncertainty, and that it is able to ensure global stabilization of the closed-loop system. In addition, a transfer model is used to link pavement roughness and major disturbance, and then added to the simulation. Finally, the effectiveness of the proposed control method is verified through a large number of comparative simulations.