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Abstract This study focuses on laying the groundwork for the effective vibration suppression of power lines using mobile damping robots (MDR). Earlier research shows that effective vibration suppression is achieved by positioning the MDR at the anti-nodes of the power line. This study focuses on accurately estimating the dynamic state of the power line using a data-driven approach, hence identifying the position of the antinode. The entire dynamics of the vibration of the system is estimated from the displacement data of the power line using Dynamic Mode Decomposition (DMD) and the resulting system is stabilized with Tikhonov Regularization. The stabilized system is then used in conjunction with a Kalman Filter to accurately estimate the dynamic state of the power line using minimal displacement. All displacement data used in this study is acquired from a Galerkin model of the power line. This study shows that this method is a viable alternative to existing numerical methods which are often computationally expensive and time-consuming. 

Abstract Pathological tremors significantly affect the quality of life for patients worldwide. Rehabilitation exoskeletons serve as one of the solutions to alleviate these pathological tremors, and voluntary motion prediction-based motion planning has been employed to enhance the performance of these devices. This paper presents a method for predicting future voluntary movement in tremor-alleviating rehabilitation exoskeletons that use voluntary motion prediction-based motion planning. In this study, a Convolutional Neural Network and Transformer architecture based neural network work with EMG sensors to predict future voluntary movements. The results show that approach performs well in predicting future voluntary movements, but there is still a limitation to filter out the tremors completely. In summary, we provide a concept for predicting future voluntary movement, which has the potential to improve the effectiveness of rehabilitation exoskeletons in tremor alleviation. 

Abstract Pathological tremor is a common neuromuscular disorder that significantly affects the quality of life for patients worldwide. With recent developments in robotics, rehabilitation exoskeletons serve as one of the solutions to alleviate these tremors. Accurate predictive modeling of tremor signals can be used to provide alleviation from these tremors via various currently available solutions like adaptive deep brain stimulation, electrical stimulation and rehabilitation orthoses, motivating us to explore better modeling of tremors for long-term predictions and analysis. This study is a preliminary step towards the prediction of tremors using artificial neural networks using EMG signals, leveraging the 20–100 ms of Electromechanical Delay. The kinematics and EMG data of a publicly available Parkinsonian tremor dataset is first analyzed, which confirms that the underlying EMGs have similar frequency composition as the actual tremor. 2 hybrid CNN-LSTM based deep learning architectures are then proposed to predict the tremor kinematics ahead of time using EMG signals and tremor kinematics history, and the results are compared with baseline models. The motivation behind hybrid CNN-LSTM models is to exploit both the temporal and spatial dependencies using CNN and LSTM respectively. This is then further extended by adding constraints-based losses in an attempt to further improve the predictions. 

Abstract Ensuring the structural integrity of the overhead power line conductor is crucial for maintaining the safety and reliability of the electrical transmission system. Exposure to environmental hazards like moisture, dust, and Wind-Induced Vibrations (WIV) can lead to defects and corrosion in power line conductors, which are primary contributors to fatigue and shortened lifespan. Thus, this paper presents a vision-based health inspection of power line conductors for a maintenance robot. The method involves image filtering techniques such as Sobel, Scharr, and Gray-scale Variance Normalization (GVN). After filtering the image, row and column analysis is conducted to identify relevant patterns that distinguish healthy and unhealthy conductors, utilizing histograms for data representation. From the histogram data analysis, 10 features were chosen from observation. Subsequently, the collected image data is classified into either healthy or unhealthy categories through supervised machine learning models, including Random Forest (RF), Multi-Layer Perception (MLP), and Gradient Boosting (GB). The best combination of features is extracted to optimize each machine-learning models accordingly. Experimental results validated the effectiveness of our method, which has been specifically fitted for the Mobile Damping Robot (MDR), presenting its potential for enhancing power line maintenance. 

Abstract Aeolian vibration is a significant factor contributing to the fatigue failure of power transmission lines. The mitigation of such vibrations in power lines has traditionally been achieved using Stockbridge dampers along the line spans, which are modeled as fixed vibration absorbers. They largely depend on their resonant frequencies and placement on the cable. Therefore, given the stochastic nature of the wind, recent studies have explored the concept of dynamic/moving absorbers. Although the effectiveness of the moving absorber has been demonstrated in the literature to be superior to that of the fixed absorber, analyses have primarily been limited to linear cases and have not accounted for nonlinearity introduced by the moving absorber or the wind inflow on the powerline. Aiming to fill this gap, this work combines the nonlinearities from the fluctuating lift force modeled as a van der Pol oscillator, with a nonlinear moving absorber into a single model to investigate the effect of a nonlinear mobile damper relative to its linear counterpart. We observe that the system with a nonlinear moving absorber exhibits smaller amplitude oscillations when compared to its linear counterpart. This finding underscores the superior mitigation characteristics of nonlinear vibration absorbers and suggests the potential for designing an optimal nonlinear moving vibration absorber. 

Abstract Recent work in nonlinear topological metamaterials has revealed many useful properties such as amplitude dependent localized vibration modes and nonreciprocal wave propagation. However, thus far, there have not been any studies to include the use of local resonators in these systems. This work seeks to fill that gap through investigating a nonlinear quasi-periodic metamaterial with periodic local resonator attachments. We model a one-dimensional metamaterial lattice as a spring-mass chain with coupled local resonators. Quasi-periodic modulation in the nonlinear connecting springs is utilized to achieve topological features. For comparison, a similar system without local resonators is also modeled. Both analytical and numerical methods are used to study this system. The dispersion relation of the infinite chain of the proposed system is determined analytically through the perturbation method of multiple scales. This analytical solution is compared to the finite chain response, estimated using the method of harmonic balance and solved numerically. The resulting band structures and mode shapes are used to study the effects of quasi-periodic parameters and excitation amplitude on the system behavior both with and without the presence of local resonators. Specifically, the impact of local resonators on topological features such as edge modes is established, demonstrating the appearance of a trivial bandgap and multiple localized edge states for both main cells and local resonators. These results highlight the interplay between local resonance and nonlinearity in a topological metamaterial demonstrating for the first time the presence of an amplitude invariant bandgap alongside amplitude dependent topological bandgaps. 

Abstract Numerous recent works have established the potential of various types of metamaterials for simultaneous vibration control and energy harvesting. In this paper, we investigate a weakly nonlinear metamaterial with electromechanical (EM) local resonators coupled to a resistance-inductance shunt circuit, a system with no previous examination in the literature. An analytical solution is developed for the system, using the perturbation method of multiple scales, and validated through direct numerical integration. The resulting linear and nonlinear band structures are used for parametric analysis of the system, focusing on the effect of resonator and shunt circuit parameters on band gap formation and vibration attenuation. This band structure analysis informs further study of the system through wavepacket excitation as well as spectro-spatial analysis. The voltage response of the system is studied through spatial profiles and spectrograms to observe the effects of shunt inductance, nonlinearity, and their interactions. Results describe the impact of adding a shunted inductor, including significant changes to the band structure; multiple methods of tuning band gaps and pass bands of the system; and changes to wave propagation and voltage response. The results demonstrate the flexibility of the proposed metamaterial and its potential for both vibration control and energy harvesting, specifically compared to a previously studied system with resistance-only shunt. 

Abstract Several investigators have taken advantage of electromagnetic shunt-tuned mass dampers to achieve concurrent vibration mitigation and energy harvesting. For nonlinear structures such as the Duffing oscillator, it has been shown that the novel nonlinear electromagnetic resonant shunt-tuned mass damper inerter (NERS-TMDI) can mitigate vibration and extract energy for a wider range of frequencies and forcing amplitudes when compared to competing technologies. However, nonlinear systems such as the NERS-TMDI are known to exhibit complex stability behavior, which can strongly influence their performance in simultaneous vibration control and energy harvesting. To address this problem, this paper conducts a global stability analysis of the novel NERS-TMDI using three approaches: the multi-parametric recursive continuationWe emphasize that these assume method, Floquet theory, and Lyapunov exponents. A comprehensive parametric analysis is also performed to evaluate the impact of key design parameters on the global stability of the system. The outcome indicates the existence of complex nonlinear behavior, such as detached resonance curves, and the transition of periodic stable solutions to chaotic solutions. Additionally, a parametric study demonstrates that the nonlinear stiffness has a minimal impact on the linear stability of the system but can significantly impact the nonlinear stability performance, while the transducer coefficient has an impact on the linear and nonlinear stability NERS-TMDI. Finally, the global sensitivity analysis is performed relative to system parameters to quantify the impact of uncertainty in system parameters on the dynamics. Overall, our findings show that simultaneous vibration control and energy harvesting come with a considerable instability trade-off that limits the range of operation of the NERS-TMDI. 

Within the field of elastic metamaterials, topological metamaterials have recently received much attention due to their ability to host topologically robust edge states. Introducing local resonators to these metamaterials also opens the door for many applications such as energy harvesting and reconfigurable metamaterials. However, the interactions between phenomena from local resonance and modulation patterning are currently unknown. This work fills that gap by studying multiple cases of spatially modulated metamaterials with local resonators to reveal the mechanisms behind bandgap formation. Their dispersion relations are determined analytically for infinite chains and validated numerically using eigenvalue analysis. The inverse method is used to determine the imaginary wavenumber components from which each bandgap is characterized by its formation mechanism. The topological nature of the bandgaps is also explored through calculating the Chern number and integrated density of states. The band structures are obtained for various sources of modulation as well as multiple resonator parameters to illustrate how both local resonance and modulation patterning interact together to influence the band structure. Other unique features of these metamaterials are further demonstrated through the mode shapes obtained using the eigenvectors. The results reveal a complex band structure that is highly tunable, and the observations given here can be used to guide designers in choosing resonator parameters and patterning to fit a variety of applications.