Adolescent idiopathic scoliosis (AIS) is a sideway curvature of the spinal column combined with a vertebral rotation that usually occurs in adolescents without any known causes. Bracing, the most common conservative treatment of AIS, has not fully exploited the benefits of the active control approaches powered by artificial intelligence (AI), although AI has entered a wide range of applications. The correction forces exerted by the brace are controlled passively by regulating the tightness of the brace's strap. Besides, training the learning-based control methods using a virtual model is of high importance in the AIS brace treatment, since training using trial and error on human subjects may result in unexpected pressure and injuries on the patient's torso. However, digital twin (DT) modeling, an emerging technology, has not been implemented into the AIS brace treatment yet. In this paper, reinforcement learning-based position-based impedance control (RLPIC) is proposed to enable a robotic brace to learn the desired physical interaction between the robotic brace and the human torso. A five-dimensional (5D) three-layer DT is also developed to be used for training the RLPIC in a simulated environment. The 5D three-layer DT consists of a physical system, a three-layer digital model of the physical system, including the robotic brace, human torso, and the physical human–robot interaction (HRI), a bidirectional connection between them, and an optimization dimension. A neural network-based regression model is also proposed to estimate the unknown parameters of the digital model. Numerical simulations and real-time experiments are performed to validate the 5D three-layer DT model. The proposed RLPIC trained using the 5D three-layer DT is verified using numerical simulations in terms of position tracking, velocity tracking, and HRI control. It is concluded that the proposed learning-based interaction control approach can improve the HRI control by learning the desired interaction in a simulated environment.

Operational modal analysis (OMA) is vital for identifying modal parameters under real-world conditions, yet existing methods often face challenges with noise sensitivity and stability. This study introduces NExT-LF, a novel method that combines the well-known Natural Excitation Technique (NExT) with the Loewner Framework (LF). NExT enables the extraction of Impulse Response Functions from output-only vibration data, which are then converted into the frequency domain and used by LF to estimate modal parameters. The proposed method is validated through numerical and experimental case studies. In the numerical study of a two-dimensional Euler–Bernoulli cantilever beam, NExT-LF provides results consistent with analytical solutions and those from standard methods, NExT with Eigensystem Realization Algorithm (NExT-ERA) and stochastic subspace identification with canonical variate analysis. Additionally, NExT-LF demonstrates superior noise robustness, reliably identifying stable modes across various noise levels where NExT-ERA fails. Experimental validation on the Sheraton Universal Hotel is the first OMA application to this structure, confirming NExT-LF as a robust and efficient method for output-only modal parameter identification.

In rest-to-rest maneuvers, input shapers like the double step (DS), zero vibration (ZV), and zero vibration derivative (ZVD) are widely utilized to eliminate residual vibrations in single-mode systems. These shapers can be used to eliminate residual oscillations in multimode systems, given that the higher frequencies are odd multiples of the system's fundamental frequency. However, the natural frequencies depend on the physical properties of the system, and such ratios cannot be guaranteed. In this study, an analytical frequency modulation technique is proposed to eliminate the residual oscillations of a double pendulum using a modified single-mode shaper. The proposed technique is based on altering the natural frequencies of the system, forcing the odd multiple ratio. This involves modifying a single-mode double-step (SMDS) input shaper by adding scaled state variables, first and second angles, to the original shaper. This addition allows the user to choose the first natural frequency and force the second natural frequency to be an odd multiple of the chosen frequency. To apply the proposed technique, the double pendulum nonlinear equations of motion are derived, linearized, and then solved analytically using modal analysis. The scaling parameters used to modify the natural frequencies are then solved analytically. To prove the concept, several numerical simulations with randomly selected parameters are presented and then experimentally tested on a scaled overhead crane. The numerical and experimental results demonstrate the effectiveness of the proposed technique.

This study proposes a computational framework for developing a multibody dynamics (MBD) model to accurately predict the vibration behavior of front-loading washing machines. The framework integrates component-level experiments and mathematical modeling to characterize the dynamic behavior of key components, including the free-stroke damper, connecting bushing, and gasket, which significantly influence the machine's vibration. Simplified, yet precise, mathematical models were developed and validated against experimental data to represent these components' dynamic characteristics. The validated models were then integrated into a comprehensive MBD model of a front-loading washing machine. This model was further verified by comparing its predicted vibrations with experimental results obtained from actual washing machines. A parametric study assessed the model's accuracy under various unbalanced mass conditions and revolutions per minute ranges, which revealed that the model is capable of generalization across different operating scenarios. Although some errors remain in specific cases involving phase differences, the overall average error is 20.11%, with a standard deviation of 4.10%. These results demonstrate that the proposed framework effectively captures the vibration behavior of front-loading washing machines, offering a reliable tool for enhancing design and operational efficiency.

A robust control method for the uncertain vertical electric stabilization system (VESS) with flexible nonlinearity is proposed, and the mismatched uncertainty is considered and compensated based on the backstepping idea. First, based on evaluating the coupling effects of the flexible nonlinearity, the analytical dynamics model of the VESS is established. Second, the tracking error is defined as the evaluation of the system's pitch-pointing tracking control, and then the mismatched state space model with two interconnected subsystems is established as the controlled system. Third, the original mismatched system is converted to the locally matched system using the backstepping design to transform the system state variables. The robust control is proposed to handle the flexible nonlinearity and mismatched uncertainty, which can make both the original system and the reconfigured system present practical stability. Finally, the effectiveness of the proposed control is verified by numerical simulation experiments. This study should be the first to consider flexible nonlinearity coupling and two different uncertainties (matched and mismatched uncertainty) in the design of pitch-pointing tracking control for the vertical electric stabilization system (VESS).

The supercritical drive shaft is becoming increasingly popular in helicopter transmission system. Dry friction dampers are specially employed to ensure the supercritical shafts crossing the critical speed safely. Due to design tolerances, manufacturing errors and time-varying factors, the parameters of the damper are inherently uncertain, affecting the safety performance of the rotor system. This paper incorporates these parameter uncertainties to investigate the dynamic response uncertainties of a supercritical shaft and dry friction damper system, which is characterized by its high dimensionality and nonlinear behaviors of rub-impact and dry friction. The nonintrusive Polynomial Chaos Expansion (PCE) is adopted to achieve the propagation of uncertainties in the rotorsystem. To achieve efficient uncertainty quantification for this high-dimensional nonlinear system, a double-layer dimensionality reduction algorithm combining modal superposition with sparse grid technique has been applied. In the computational workflow, the inner layer uses modal superposition and the outer layer uses sparse grid techniques. The stochastic dynamic response of the rotorsystem is analyzed considering the uncertainty of five design parameters of the damper. Furthermore, as a post-processing of the PCE coefficients, the Sobol global sensitivity analysis is conveniently conducted. The influence of individual parameters or groups of parameters on the dynamic response is studied. A multi-objective optimization design for the key parameters is then carried out based on the established PCE model. The dynamic model and optimization design method are verified by experiments. The results will benefit uncertainty quantification analysis of high-dimensional nonlinear rotorsystem.

Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties; it requires an integrated approach that captures the interdependencies between various parameters, including elastic modulus, tensile strength, yield strength, and strain-hardening exponent. Neglecting these correlations in sensitivity analyses can compromise prediction accuracy and physical interpretability. In this study, we introduce a dependency-aware sensitivity analysis framework, assisted by machine learning-based surrogate models, to evaluate the contributions of these mechanical properties to fatigue life variability. Tensile strength emerged as the most influential parameter, with significant second-order interactions, particularly between tensile and yield strength, highlighting the central role of coupled effects in fatigue mechanisms. By addressing these interdependencies, the proposed approach improves the reliability of fatigue life predictions and offers a solid foundation for the optimization of metallic components subjected to cyclic stresses.

Marine environment is a harsh environment that can cause major issues for marine structures while operating in this environment, including fatigue cracking and corrosion damage, which can yield catastrophic consequences, such as human life losses, financial losses, environmental pollution, and so forth. Therefore, it is critical to take necessary actions before undesired situations happen. One potential solution is to install structural health monitoring systems on marine structures. Structural health monitoring is a technology to enhance the safety, stability, and functionality of large engineering structures. The inverse Finite Element Method (iFEM) is a promising technique for this purpose. In this study, the corrosion damage detection capability of iFEM is presented by introducing two new damage parameters for plates under tension and bending loading conditions. The contribution of newly introduced parameters to the accuracy of iFEM on damage detection is demonstrated for multiple corrosion scenarios and sensor configurations.

Vibrating flip-flow screens are widely used in the field of screening; its actual operation is affected by the impact force of materials, but existing research usually ignores this effect. Based on this background, considering the influence of material impact force and moment on vibrating flip-flow screens, this paper develops a dynamic model and a vibration differential equation of a vibrating flip-flow screen, performs the analysis of material movement and calculation of the material impact force, and includes the material impact force in the dynamic characteristic analysis of a vibrating flip-flow screen. The results indicate the following: (1) The impact forces F_x and F_y account for 29% and 57.58% of the excitation force amplitude, respectively, indicating that they are of the same magnitude as the excitation force. Material impact increases the vibration amplitudes of the main and floating frames, and therefore, cannot be ignored in vibrating flip-flow screen design. (2) By comparing the vibrating flip-flow screen's responses with and without the impact, it is found that impact force significantly influences the system response, causing the displacement curve to shift and the amplitude–frequency curve to have periodic fluctuations and peak values. (3) The effects of impact parameters on the dynamic characteristics of a vibrating flip-flow screen are studied. The results show that increases in material mass and material binding coefficient lead to a decrease in the system natural frequencies. Due to the impact force, the amplitude–frequency curve of the main frame peaks at a frequency lower than the first order of the natural frequency, and the amplitude–frequency curve of the floating frame peaks in the intervals of 5–10 Hz and 20–25 Hz. The results provide a theoretical reference for the design of vibrating flip-flow screens. The operating frequency of vibrating flip-flow screens should be selected to avoid the peak value due to the impact force, which helps extend the working life.

This article investigates the nonlinear vibration behavior of porous multidirectional piezoelectric functionally graded nonuniform (PFGN) plates resting on orthotropic variable elastic foundations and subjected to hygrothermal loading. The sigmoidal law is employed to define the multidirectional gradation properties, incorporating porosity along both the axial and thickness directions. The governing equations for the porous multidirectional PFGN plate are derived using the modified first-order shear deformation theory (FSDT) with nonlinear von Kármán strain and Hamilton's principle. A higher-order finite element (FE) approach, combined with a modified Newton-Raphson method, is utilized to solve the resulting equations. The study reveals that orthotropic variable elastic foundations significantly influence the vibration behavior of multidirectional PFGN porous plates compared to conventional elastic foundations under hygrothermal loading. Additionally, the effects of various parameters such as thickness ratio, tapered ratio, material exponent, boundary conditions, porosity distribution, electrical loading, temperature variation, and moisture change on the vibration behavior are comprehensively analyzed. The results of this study have direct applications in energy harvesting and structural health monitoring, offering a novel approach to designing and optimizing smart materials for engineering systems operating under hygrothermal and thermoelectrical conditions.

Nonlinearity in parallel compliance can be exploited to improve the performance of locomotion systems in terms of (1) energy efficiency, (2) control robustness, and (3) gait optimality; that is, attaining energy efficiency across a set of motions. Thus far, the literature has investigated and validated only the first two benefits. In this study, we present a new mathematical framework for designing nonlinear compliances in cyclic tasks encompassing all three benefits. We present an optimization-based formulation for each benefit to obtain the desired compliance profile. Furthermore, we analytically prove that, compared to linear compliance, using nonlinear compliance leads to (1) lower energy consumption, (2) better closed-loop performance, specifically in terms of tracking error, and (3) a higher diversity of natural frequencies. To compare the performance of linear and nonlinear compliance, we apply the proposed methods to a diverse set of robotic systems performing cyclic tasks, including a 2-DOF manipulator, a 3-DOF bipedal walker, and a hopper model. Compared to linear compliance, the nonlinear compliance leads to better performance in all aspects; for example, a 70% reduction in energy consumption and tracking error for the manipulator simulation. Regarding gait optimality, for all robotic simulation models, compared to linear compliance, the nonlinear compliance has lower energy consumption and tracking error over the considered set of motions. The proposed analytical studies and simulation results strongly support the idea that using nonlinear compliance significantly improves robotic system performance in terms of energy efficiency, control robustness, and gait optimality.