This review paper presents a comprehensive evaluation and forward-looking perspective on the underexplored topic of servicing target objects using spacecraft swarms. Such targets can be known or unknown, cooperative or uncooperative, and pose significant challenges in modern space operations due to their inherent complexity and unpredictability. Successfully servicing space objects is vital for active debris removal and broader on-orbit servicing tasks such as satellite maintenance, repair, refueling, orbital assembly, and construction. Significant effort has been invested in the literature to explore the servicing of targets using a single spacecraft. Given its advantages and benefits, this paper expands the discussion to encompass a swarm approach to the problem. This review covers various single-spacecraft approaches and presents a critical examination of the existing, although limited, body of work dedicated to servicing orbital objects using multiple spacecraft. The focus is also broadened to include some influential studies concerning the characterization, capture, and manipulation of physical objects by general multiagent systems, a subject with significant parallels to the core interest of this manuscript. Furthermore, this article also delves into the realm of simultaneous localization and mapping, highlighting its application within close-proximity operations in space, especially when dealing with unknown uncooperative targets. Special attention is paid to the benefits that this field can receive from distributed multiagent architectures. Finally, an exploration of the promising field of swarm robotics is presented, with an emphasis on its potential to revolutionize the servicing of orbital target objects. Concurrently, a survey of general research directly engaging swarms in the orbital context is conducted. This review aims to bridge the knowledge gap and stimulate further research in the underexplored domain of servicing space targets with spacecraft swarms.
Reducing the effects of external disturbance on overhead crane systems is crucial, as they can impair the controller performance and cause excessive vibrations or oscillations of the payloads. One such external disturbance is the inclination of the supporting track of the crane trolley, which causes the system dynamics model to change. An open-loop control strategy is widely utilized to control the payload sway motion and generally does not require any alterations in the physical structure of a system or the installation of sensors and/or actuators. Input and command shaping are two common open-loop control techniques applied to control overhead cranes. In this paper, the effect of moving an overhead crane system along an inclined supporting track is investigated. In addition, the ability of different types of input- and command-shaping control schemes to suppress the residual vibrations due to trolley track inclination is demonstrated. Two types of input-shaping controllers, which are double-step, zero vibration, and one command waveform (WF) shaper based on a trigonometric function, are used and tested. A linear equation of motion of the overhead crane resting on an inclined surface is developed to simulate the overhead crane and payload motion. The effectiveness of the different types of open-loop controllers to suppress residual vibrations is verified by both simulation and experimental results. In addition, a newWF command shaper is proposed and designed to overcome track inclination while eliminating payload residual vibration. A comprehensive comparative analysis, both numerically and experimentally, is performed on the new proposed shaper to measure its effectiveness in handling inclination when compared to other types of open-loop controllers. The new shaper outperforms other controllers in eliminating payload residual vibration for a wider range of inclination angles.
Superior surface finish remains a fundamental criterion in precision machining operations, and tool-tip vibration is an important factor that significantly influences the quality of the machined surface. Physics-based models heavily rely on assumptions for model simplification when applied to complex high-end systems. However, these assumptions may come at the cost of compromising the model's accuracy. In contrast, data-driven techniques have emerged as an attractive alternative for tasks such as prediction and complex system analysis. To exploit the advantages of data-driven models, this study introduces a novel convolutional enhanced transformer model for tool-tip vibration prediction, referred to as CeT-TV. The effectiveness of this model is demonstrated through its successful application in ultra-precision fly-cutting (UPFC) operations. Two distinct variants of the model, namely, guided and nonguided CeT-TV, were developed and rigorously tested on a data set custom-tailored for UPFC applications. The results reveal that the guided CeT-TV model exhibits outstanding performance, characterized by the lowest mean absolute error and root mean square error values. Additionally, the model demonstrates excellent agreement between the predicted values and the actual measurements, thus underlining its efficiency and potential for predicting the tool-tip vibration in the context of UPFC.
Compliant mechanisms with curved flexure hinges/beams have potential advantages of small spaces, low stress levels, and flexible design parameters, which have attracted considerable attention in precision engineering, metamaterials, robotics, and so forth. However, serial–parallel configurations with curved flexure hinges/beams often lead to a complicated parametric design. Here, the transfer matrix method is enabled for analysis of both the kinetostatics and dynamics of general serial–parallel compliant mechanisms without deriving laborious formulas or combining other modeling methods. Consequently, serial–parallel compliant mechanisms with curved flexure hinges/beams can be modeled in a straightforward manner based on a single transfer matrix of Timoshenko straight beams using a step-by-step procedure. Theoretical and numerical validations on two customized XY nanopositioners comprised of straight and corrugated flexure units confirm the concise modeling process and high prediction accuracy of the presented approach. In conclusion, the present study provides an enhanced transfer matrix modeling approach to streamline the kinetostatic and dynamic analyses of general serial–parallel compliant mechanisms and beam structures, including curved flexure hinges and irregular-shaped rigid bodies.
Piezoelectric material-based semi-active vibration control systems may effectively suppress vibration amplitude without any external power supply, or even harvest electrical energy. This bidirectional electrical energy control phenomenon is theoretically introduced and validated in this paper. A flyback transformer-based switching piezoelectric shunt circuit that can extract energy from or inject energy into piezoelectric elements is proposed. The analytical expressions of the controlled energy and the corresponding vibration attenuation are therefore derived for a classical electromechanical cantilever beam. Theoretical predictions validated by the experimental results show that the structure vibration attenuation can be tuned from −5 to −25 dB under the given electrical quality factor of the circuit and figure of merit of the electromechanical structure, and the consumed power is in the range of −13 to 25mW, which is a good theoretical basis for the development of self-sensing, self-adapting, and self-powered piezoelectric vibration control systems.
The objective of dynamical system learning tasks is to forecast the future behavior of a system by leveraging observed data. However, such systems can sometimes exhibit rigidity due to significant variations in component parameters or the presence of slow and fast variables, leading to challenges in learning. To overcome this limitation, we propose a multiscale differential-algebraic neural network (MDANN) method that utilizes Lagrangian mechanics and incorporates multiscale information for dynamical system learning. The MDANN method consists of two main components: the Lagrangian mechanics module and the multiscale module. The Lagrangian mechanics module embeds the system in Cartesian coordinates, adopts a differential-algebraic equation format, and uses Lagrange multipliers to impose constraints explicitly, simplifying the learning problem. The multiscale module converts high-frequency components into low-frequency components using radial scaling to learn subprocesses with large differences in velocity. Experimental results demonstrate that the proposed MDANN method effectively improves the learning of dynamical systems under rigid conditions.
Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems. However, the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions. To address this challenge, this paper proposes a novel deep-learning model for multisource data augmentation and small sample fault diagnosis. The raw multisource data are first converted into two-dimensional images using the Gramian Angular Field, and a generator is built to transform random noise into images through transposed convolution operations. Then, two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability. The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator. Furthermore, a global optimization strategy is designed to upgrade parameters in the model. The discriminators and generator compete with each other until Nash equilibrium is achieved. A real-world multistep forging machine is adopted to compare and validate the performance of different methods. The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities. Compared with other state-of-the-art models, the proposed approach has better fault diagnosis accuracy in various scenarios.
In this paper, an asymmetric vibroacoustic system that can passively realize nonreciprocal transmission of acoustic energy is reported. This experimental system consists of a waveguide, a strongly nonlinear membrane, and three acoustic cavities with different sizes. The theoretical modeling of the system is verified by experiments, and parametric analysis is also carried out. These intensive studies reveal the nonreciprocal transmission of acoustic energy in this prototype system. Under forward excitation, internal resonance between the two nonlinear normal modes of the vibroacoustic system occurs, and acoustic energy is irreversibly transferred from the waveguide to the nonlinear membrane. However, under backward excitation, there is no internal resonance in the system. Energy spectra and wavelet analysis are used to highlight the mechanism of nonreciprocal transfer of acoustic energy. Consequently, nearly unidirectional (preferential) transmission of acoustic energy transfer is shown by this system. The nonreciprocal acoustic energy transfer method illustrated in this paper provides a new way to design the odd acoustic element.