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Motivated by the benefits that recent finite-time continuous control approaches have proven to give rise, this work aims to design a proportional–integral–derivative (PID) type control scheme for the global regulation of constrained-input mechanical systems that incorporates design features characteristic of such finite-time continuous algorithms. This is proven to be achieved through a more general PID type control structure that incorporates exponential weights on the P and D type terms, through which such control actions are permitted to loose Lipschitz-continuity at the desired equilibrium values. This entails an important challenge consisting on the introduction of an appropriate analytical framework and the development of a suitable closed-loop analysis through which the resulting design is properly supported. The study is complemented by experimental tests which show that appropriate (less-than-unity) values on the incorporated exponential weights indeed give rise to closed-loop improvements characteristic of finite-time continuous control approaches, such as reduction of overshoot on the position error responses and of the control effort, alleviating such a performance adjustment task. • PID control with non-Lipschitz control actions improve the closed-loop performance. • Exponential weights on the control actions potentiate performance adjustment. • PID control innovated by involving continuous finite-time control design techniques. [ABSTRACT FROM AUTHOR]

In this note we study the generation of non‐trivial oscillations of a class of mechanical systems with underactuation one. The proposed design consists of two terms, that is, a partial linearizing state feedback, and an immersion and invariance orbital stabilization controller. The first step is adopted to simplify analysis and design, however, bringing an additional difficulty that the model loses its Euler–Lagrange structure after the collocated pre‐feedback. To address this, we propose a constructive solution to the orbital stabilization problem via a smooth controller in an analytic form, and the model class identified in the article is characterized via some easily a priori verifiable assumptions on the inertia matrix and the potential energy function. [ABSTRACT FROM AUTHOR]

The implementation of morphing wing mechanisms shows significant potential for improving aircraft performance, as highlighted in the recent literature. The Clean Sky 2 AirGreen 2 European project team is currently performing ground and wind tunnel tests to validate improvements in morphing wing structures. The project aims to demonstrate the effectiveness of these morphing designs on a full-scale flying prototype. This article describes the design methodology and structural testing of a scaled morphing-flap structure, which can adapt to three different morphing modes for various flight conditions: low-speed (take-off and landing) and high-speed (cruise). A scale factor of 1:3 was selected for the wind tunnel test campaign. Due to challenges in scaling the embedded mechanisms and actuators necessary for shape-changing, a full geometrical scale of the real flap prototype was not feasible. Static analyses were performed using the finite element method to address critical load conditions determined through three-dimensional computational fluid dynamic (CFD) analysis. The finite element (FE) analysis was conducted and the results were compared with the empirical data from the structural test. Good correlations were found between the structural testing results and numerical predictions, including static deflections and elastic deformations under applied loads. This indicates that the modeling approaches used during the design and testing phases were highly successful. Based on simulations for the ultimate load conditions tested during the wind tunnel tests, the scaled flap prototype has been deemed suitable for further testing. [ABSTRACT FROM AUTHOR]

Under the general unbalanced directed communication graphs, distributed fixed-time optimization problem is studied for multiple mechanical systems modeled by Euler-Lagrange equations. Distributed controllers have been developed to achieve the optimal position of each agent within a fixed time by integrating distributed fixed-time estimator techniques and a fixed-time control design in a cohesive manner. The effectiveness of the proposed control protocol is also illustrated by a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper presents a novel and constructive solution for the velocity estimation of a large class of mechanical systems. By employing the technical results in non-smooth analysis, a specific eigenvalue analysis problem that can be systematically solved through structured computational procedures is formulated, and the resulting globally exponentially convergent observer can therefore be generated automatically for any appropriate model input with arbitrarily complex configurations. The proposed method applies directly to non-holonomic systems with Pfaffian constraints, and its structural simplification can be further achieved for systems with uniformly bounded inertia matrix. The established results provide a major breakthrough on both the dimension and practical realizability of the prevailing Immersion & Invariance velocity observer designs, and its effectiveness is verified with numerical simulations of several representative examples. [ABSTRACT FROM AUTHOR]

In this research work, we investigate a new three-dimensional jerk system with three parameters in which one of the nonlinear terms is a sinusoidal nonlinearity. We show that the new jerk system has two unstable equilibrium points on the ��-axis. Numerical integrations show the existence of periodic and chaotic states, as well as unbounded solutions. Consideration of the Poincaré sphere at infinity found no periodic states. We show that the new jerk system exhibits multistability with coexisting attractors. We also present results for the offset boosting of the proposed chaotic jerk system. Using MultiSim version 14.1, we design an electronic circuit for the new jerk system with a sinusoidal nonlinearity. As a control application, we design complete synchronization for the master-slave jerk systems using backstepping control technique. Simulations are presented to illustrate the main results of this research work.

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize Computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach. To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the results demonstrate the effectiveness of incorporating constraints into the design optimization process and the usage of Bayesian optimization (BO), providing insights into the obtained optimum. This approach offers probabilistic insights into the expected improvement of the optimum, highlighting that the full optimization potential is utilized in a computationally efficient manner. [ABSTRACT FROM AUTHOR]

Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems. [ABSTRACT FROM AUTHOR]

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade.

Over the years, agricultural management practices are being improved as they integrate Information and Communication Technologies (ICT) and Precision Agriculture tools. Regarding sugarcane crop production, this integration aims to reduce production cost, enhance input applications, and allow communication among different hardware and datasets, improving system sustainability. Sugarcane mechanization has some particularities that mandate the development of custom solutions based on digital tools, which are being applied globally in different crops. Digital mechanization can be conceived as the application of digital tools on mechanical operation. This review paper addresses different digital solutions that have contributed towards the mechanization of sugarcane crop production. The process of digitalization and transformation in agriculture and its related operations to sugarcane are presented, highlighting important ICT applications such as real-time mechanical operations monitoring and integration among operations, demonstrating their contributions and limitations regarding management efficiency. In addition, this article presents the major challenges to overcome and possible guidance on research to address these issues, i.e., poor communication technologies available, need for more focus on field and crop data, and lack of data interoperability among mechanized systems.

This article addresses a cooperative game-oriented optimal constraint-following problem for fuzzy mechanical systems. The state of the concerned system is affected by possibly (fast) time-varying uncertainty. The fuzzy set theory is adopted to describe such uncertainty. The task is to drive the system to obey a set of prescribed constraints optimally. Since the control objective may be changing along with the system uncertainty, a time-varying β-measure is defined to gauge the constraint-following error; based on which, an adaptive robust control scheme with two tunable parameters is then proposed to render it to be uniform boundedness and uniform ultimate boundedness. For the seeking of the optimal design parameters, two cost functions, each of which is dominated by one tunable parameter, are developed with the fuzzy information, and thereout a two-player cooperative game is formulated. Finally, the optimal design problem is successfully solved: with the existence, uniqueness, and analytical expression of the Pareto optimality.

This paper extends a kinetic-potential energy shaping method to stochastic mechanical port-Hamiltonian systems. The kinetic-potential energy shaping brings a new class of Lyapunov function candidates involving a cross term of the position and the momentum without solving partial differential equations for deterministic port-Hamiltonian systems. However, the conventional kinetic-potential energy shaping does not necessarily work for stochastic port-Hamiltonian systems due to energy increase by stochastic noise. Therefore, we first provide a modification properly compensated by using stochastic generalized canonical transformations. Then, two stochastic stability results are presented. The first result shows a necessary condition for stochastic asymptotic stability for an equilibrium state. The other one shows a necessary condition for stochastic bounded stability for a target state, which is not necessarily an equilibrium point. [ABSTRACT FROM AUTHOR]

In this research work, we investigate a new three-dimensional jerk system with three parameters in which one of the nonlinear terms is a sinusoidal nonlinearity. We show that the new jerk system has two unstable equilibrium points on the x-axis. Numerical integrations show the existence of periodic and chaotic states, as well as unbounded solutions. Consideration of the Poincaré sphere at infinity found no periodic states. We show that the new jerk system exhibits multistability with coexisting attractors. We also present results for the offset boosting of the proposed chaotic jerk system. Using MultiSim version 14.1, we design an electronic circuit for the new jerk system with a sinusoidal nonlinearity. As a control application, we design complete synchronization for the master-slave jerk systems using backstepping control technique. Simulations are presented to illustrate the main results of this research work. [ABSTRACT FROM AUTHOR]

One major challenge of today's product development is to master the constantly increasing product complexity driven by the interactions between different disciplines, like mechanical, electrical and software engineering. An approach to master this complexity is function-oriented model-based systems engineering (MBSE). In order to guide the developer through the process of transferring requirements into a final product design, MBSE methods are essential. However, especially in mechanics, function-oriented product development is challenging, as functionality is largely determined by the physical effects that occur in the contacts of physical components. Currently, function-oriented MBSE methods enable either the modeling of contacts or of structures as part of physical components. To create seamless function-oriented mechanical system architectures, a holistic method for modeling contacts, structures and their dependencies is needed. Therefore, this paper presents an extension of the motego method to model structures, by which the seamless parametric modeling of function-oriented mechanical system architectures from requirements to the physical product is enabled. [ABSTRACT FROM AUTHOR]

The flap strut fairing system is made of movable construction inside, but the outer skin is made of composite material. Each day, we can face an expensive repair for movable construction and outer skin as most of the aviation companies are dealing with mechanical issues that are not solved during a long period of time or somebody misses it due to human errors. In this abstract, we will look for options for easier and faster solutions to avoid the expensive operation process. [ABSTRACT FROM AUTHOR]

One major challenge of today’s product development is to master the constantly increasing product complexity driven by the interactions between different disciplines, like mechanical, electrical and software engineering. An approach to master this complexity is function-oriented model-based systems engineering (MBSE). In order to guide the developer through the process of transferring requirements into a final product design, MBSE methods are essential. However, especially in mechanics, function-oriented product development is challenging, as functionality is largely determined by the physical effects that occur in the contacts of physical components. Currently, function-oriented MBSE methods enable either the modeling of contacts or of structures as part of physical components. To create seamless function-oriented mechanical system architectures, a holistic method for modeling contacts, structures and their dependencies is needed. Therefore, this paper presents an extension of the motego method to model structures, by which the seamless parametric modeling of function-oriented mechanical system architectures from requirements to the physical product is enabled.

This paper presents a gripper structure that improves the known potential limitations of curved fingertip movements and the restricted range of objects that can be grasped during power grasp in two-finger adaptive grippers. In the proposed gripper, a Grasshopper mechanism is applied to improve the movement of the fingertips, making it closer to a straight line. Additionally, a grasping method is implemented that allows the fingertips to move towards the palm of the gripper after grasping an object, thereby expanding the size range of objects that can be adaptively grasped. This movement of the fingertips also results in an increase in gripping force. The performance theoretically confirmed through kinematics and force analysis is verified through the development of a prototype. This prototype is used to validate the basic functions of the adaptive gripper and to assess improvements in the fingertip trajectory, demonstrating movement closer to a straight line. Additionally, the improvements in the size range of objects that can be adaptively grasped are verified. The proposed power grasp range ratio shows that the gripper can grasp objects up to 76.7% of the stroke, demonstrating an improved range compared to other two-finger adaptive grippers. Furthermore, the grasping motion of the proposed gripper demonstrates an increase in gripping force in the experiments.

The principal contribution of this paper is the feedback design of an output trajectory tracking controller applied to a certain class of mechanical systems. The controller does not need velocity measurements for its implementation and achieves the trajectory tracking control aim. We propose adaptive estimators to determine the plant parameters and constant external perturbations. When the plant is affected by time-variant external perturbations, an observer filter that can be implemented to recover an estimation of the time-variant perturbations can replace the adaptive estimator. The closed-loop stability is analysed throughout Lyapunov tools. We illustrate the performance of the proposed control structure via numerical simulations and experiments, the numerical simulations are carried out for a pendulum coupled to a DC motor and the experiments are accomplished for a mass-spring-damper system.

This paper proposes an optimal parameter design of control scheme for mechanical systems by adopting the Stackelberg game theory. The goal of the control is to drive the mechanical system to follow the prescribed constraints. The system uncertainty is (possibly fast) time-varying and bounded. A β-measure is defined to gauge the performance. A robust control is proposed to render the β-measure uniformly ultimately bounded. This control scheme is based on feasible design parameters (i.e., parameters within prescribed range), and the choices of these parameters may not be unique. For optimal (unique) parameter selection, a Stackelberg game is formulated. By taking the control design parameters as the players, for each player, a cost function is built with the consideration of the performance cost, the time cost and the control cost. To follow, the Stackelberg strategy is then carried out via backward induction, which results in the choice of the optimal parameters.

The principal contribution of this paper is the feedback design of an output trajectory tracking controller applied to a certain class of mechanical systems. The controller does not need velocity measurements for its implementation and achieves the trajectory tracking control aim. We propose adaptive estimators to determine the plant parameters and constant external perturbations. When the plant is affected by time-variant external perturbations, an observer filter that can be implemented to recover an estimation of the time-variant perturbations can replace the adaptive estimator. The closed-loop stability is analysed throughout Lyapunov tools. We illustrate the performance of the proposed control structure via numerical simulations and experiments, the numerical simulations are carried out for a pendulum coupled to a DC motor and the experiments are accomplished for a mass-spring-damper system. [ABSTRACT FROM AUTHOR]

While the kinematic and dynamic modeling of mechanical systems is well developed, the numerical solution of the governing equations of many of these models is still a large field under investigation. Since in any constrained mechanical system, the connections of the connected bodies restrict their relative movement, therefore the governing equations of the model usually form a system of differential-algebraic equations that includes both differential and algebraic equations. It should be noted that, unlike the analytical and numerical solution of differential equations, the analytical and numerical behavior of differential-algebraic equations is more complex and completely different from differential equations. In this paper, a high accuracy method for solving the system of differential-algebraic equations arising in mechanical systems is presented. The presented method is based on the use of a pseudospectral method that will transform the solution of the system of differential-algebraic equations into the solution of a system of algebraic equations. Then, an optimization technique is utilized to facilitate solving the obtained system of algebraic equations. At the end, some numerical experiments are performed on several benchmark problems to show the accuracy and applicability of the method. [ABSTRACT FROM AUTHOR]

Kinematics is a fundamental topic in engineering, robotics, mechatronics, and control systems and significantly resolves some of these fields' most pressing issues. It is essential to assess the balance between a topic's theoretical framework and its empirical validation to succeed in engineering. Educational tools have gained significant attention for their ability to enhance the learning experience by providing the hands-on experiences necessary to assess theoretical frameworks and empirical validations. This paper presents a system incorporating state-of-the-art features, including a fuzzy controller enabling precise control of a linear actuator and a USB camera, to provide an interactive experience. The USB camera captures the position of the actuator, providing real-time visual feedback and allowing the students to validate their theoretical understanding through practical experiments. Precision, accuracy, resolution, and the implementation of the fuzzy controller are measured to evaluate the whole system's performance. The design, implementation, and control of our educational electrical linear actuator for teaching kinematics concepts contribute to a practical educational tool and advance interactive learning approaches in the field. [ABSTRACT FROM AUTHOR]

The problem of delay-independent stability is studied for a class of mechanical systems under dissipative, non-conservative and potential forces, which are described by homogeneous terms. The obtained conditions are extended to the case of switched force model under arbitrary and restricted commutation laws. The proof is based on analysis of a complete Lyapunov–Krasovskii functional (common and multiple ones are considered for switched scenario). The proposed stability conditions are illustrated by simulation examples. [ABSTRACT FROM AUTHOR]

The design optimization of mechanisms is promising as it results in more energy-efficient machines without compromising performance. However, machine builders do not apply state-of-the-art methods, as these algorithms require case-specific theoretical analysis. Moreover, the design synthesis approaches in the literature predominantly utilize heuristic optimizers, leading to suboptimal local minima. This paper introduces a widely applicable workflow, guaranteeing the global optimum. The constraints describing the feasible region of the possible designs are essential to find the global optimum. Therefore, kinematic analysis of the point-to-point planar four-bar mechanism is discussed. Within the feasible design space, objective value samples were generated through the CAD multi-body software. These motion simulations determine the required torque to fulfill the movement for a combination of design parameters. This replaces the cumbersome analytic derivation of the torque. This paper introduces sparse interpolation techniques to avoid brute force sampling of the design space. The advantage of this approach is that it is easily scalable to more design parameters, as the interpolation method minimizes the number of necessary samples. This paper explains the mathematical background of our developed interpolation approach. However, a step-by-step procedure is introduced to allow the employment of the interpolation technique by machine designers without the necessity to understand the underlying mathematics. Finally, the mathematical expression, obtained from the interpolation, enables applying global optimizers. In a case study of an emergency ventilator mechanism with three design parameters, 1870 CAD motion simulations allowed reducing the RMS torque of the mechanism by 67%. [ABSTRACT FROM AUTHOR]

Response measurement of various functionality states of machines is an inevitable part of smooth production. An effectively efficient measurement and control system of the machinery helps the inspection engineers to detect failures. In the recent age, the concept of industry 5.0, which focuses on the interaction between humans and machines, has increased the importance of sensors in the industry. Various sensing devices may aid and support the machining process, making it more efficient. These sensing devices support machine tools and enhance productivity by reducing failures. The application of an online monitoring system that includes vibration measurement and tool wear measurement, and the electrical energy consumption is getting fame in industry and academia. This paper mainly presents a holistic review of various sensors and their application in the manufacturing processes. Advancements in the sensor for quality measurement, cutting force measurement, and tool wear measurement are discussed. Furthermore, the adoption of the Internet of Things (IoT) in machining processes and conversion of conventional manufacturing processes into modern digitalized systems are discussed. Recent trends of research to improve the sensor technology have been improved. This study provides fundamental guidelines for using and adopting the various types of sensors in machining processes. [ABSTRACT FROM AUTHOR]

For two canonical models of mechanical systems with disturbances presented by Rayleigh‐ and Liénard‐type equations, several designs of Lyapunov functions are investigated. Under given restrictions, the existence of these functions implies global or local input‐to‐state stability property for the systems. The proposed stability conditions are constructive, which is demonstrated in applications. [ABSTRACT FROM AUTHOR]

Model order reduction (MOR) methods that are designed to preserve structural features of a given full order model (FOM) often suffer from a lower accuracy when compared to their non-structure-preserving counterparts. In this paper, we present a framework for structurepreserving MOR, which allows us to compute structured reduced order models (ROMs) with a much higher accuracy. The framework is based on parameter optimization, i.e., the elements of the system matrices of the ROM are iteratively varied to minimize an objective functional that measures the difference between the FOM and the ROM. The structural constraints can be encoded in the parametrization of the ROM. The method only depends on frequency response data and can thus be applied to a wide range of dynamical systems. We illustrate the effectiveness of our method on a port-Hamiltonian and on a symmetric second-order system in a comparison with other structurepreserving MOR algorithms. [ABSTRACT FROM AUTHOR]

For a canonical form of mechanical systems defined through gradients of potential energy and dissipative terms, the conditions of finite-time and fixed-time (integral) input-to-state stability are derived by finding suitable Lyapunov functions. The proposed stability conditions are constructive, which is demonstrated in several applications. [ABSTRACT FROM AUTHOR]

Summary: The autonomous vehicle is profoundly changing the future of transportation safety. The reduction in collisions with different injury degrees and the savings of crash-related economic costs if nine autonomous vehicle technologies were promoted to be wildly available in China are evaluated. The quantitative analysis was divided into three main parts: (1) Calculate the technical effectiveness of nine autonomous vehicle technologies in collisions through a systematic literature review; (2) Apply the technical effectiveness to estimate the potential effects on avoiding collisions and saving crash-related economic costs in China if all vehicles had these technologies; and (3) Quantify the influence of current technical limitations in speed applicability, weather applicability, light applicability, and active rate on potential effects. Definitely, these technologies have different safety benefits in different countries. The framework developed and technical effectiveness calculated in this study can be applied to evaluate the safety impact of these technologies in other countries.

This article presents the methodology for developing a control laboratory project that provides practical experience based on the ABET criteria. The project is structured around a portable and cheap ball and beam whose integrated system is made using printed circuit boards as the first task. For the expression of the plant, students are guided to execute the essential stages of the control system design, from system modeling, through the design of the basic or advanced control strategy in the MATLAB and Arduino environment, to the implementation and validation of the closed loop. The proposed methods are clear and direct, greatly fostering the understanding of feedback control techniques and enabling students to gain extensive knowledge in practical implementations of control systems. The methodology is easy to interpret and modify in order to adopt it to any computer, allowing for the implementation of new practical tasks in control courses. Additionally, application examples and student-focused comments are included. This paper describes, in detail, the implementation and development of six laboratory practices for control courses, which have been developed based on ESP32 and other existing equipment. [ABSTRACT FROM AUTHOR]

This paper considers mechanical systems with linear velocity forces and highly non-linear positional forces containing distributed-delay terms. Asymptotic stability conditions of system equilibria are proved using Lyapunov's direct method and the decomposition method. The developed approaches are applied to the monoaxial stabilization of a solid body. The theoretical outcomes are confirmed by computer simulation results. [ABSTRACT FROM AUTHOR]

This article addresses the stabilization control issue for the postcapture tethered system by tethered space robot (TSR). Due to the physical characteristics of space tether by nature, there exists no control inputs on the in-plane/out-of-plane channels of the tether; therefore, the postcapture tethered system is a typical multiinput and multioutput underactuated system. In this article, we propose an energy-based controller for the underactuated system subject to input saturation and the nonnegativity constraint of the tether tension. First, we give the dynamic model of the postcapture tethered system, with consideration of the three attitude angles of the postcapture combination, the in-plane/out-of-plane angles, and the tether length. Second, we list the analysis process of the system’s equilibrium points. Third, we give the detailed controller design process, and verify the stability of the system by invoking the Lyapunov techniques and the extended Barbalat’s lemma. Finally, numerical simulations and comparison results with the hierarchical sliding mode controller are conducted to validate the performance improvement of the developed control strategy. [ABSTRACT FROM AUTHOR]

In order to realize the prediction of the remaining useful life (RUL) of the low-voltage conventional circuit breaker mechanical system in the whole life cycle, a prediction method based on opening-related vibration signal and stage attention hybrid neural network is proposed. The opening vibration signal contains richer mechanical state information. Therefore, the variational mode decomposition (VMD) based on energy loss is used to reconstruct the signal to increase the time-domain identification of the shock characteristics of the vibration signal, and the double threshold method (DTM) based on short-time energy entropy ratio (STEER) is used to realize the segmentation of the opening-related vibration signal. A stage attention mechanism (SA) is proposed, combining the convolutional neural network (CNN) and the gated recurrent unit (GRU) to construct the SA-CNN-GRU hybrid model. The first-stage distributed attention (DA) acts on the CNN to refine the mining of the information sensitive to degradation in the time and feature dimensions, and the second-stage time-step attention (TA) acts on the GRU to learn the time-series gradual information of the degradation process in a targeted manner and eliminate the influence of the degradation uncertainty of the complex system. Using the vibration data of three circuit breakers to test, the results show that the proposed method can solve the RUL prediction problem of circuit breaker mechanical systems and have high prediction stability, and the introduction of SA can effectively improve the prediction performance of the model. [ABSTRACT FROM AUTHOR]

Machine learning and in particular deep learning techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry. [ABSTRACT FROM AUTHOR]

Abstract Machine learning and in particular deep learning techniques have demonstrated the most efficacy in training, learning, analyzing, and modelling large complex structured and unstructured datasets. These techniques have recently been commonly deployed in different industries to support robotic and autonomous system (RAS) requirements and applications ranging from planning and navigation to machine vision and robot manipulation in complex environments. This paper reviews the state-of-the-art with regard to RAS technologies (including unmanned marine robot systems, unmanned ground robot systems, climbing and crawler robots, unmanned aerial vehicles, and space robot systems) and their application for the inspection and monitoring of mechanical systems and civil infrastructure. We explore various types of data provided by such systems and the analytical techniques being adopted to process and analyze these data. This paper provides a brief overview of machine learning and deep learning techniques, and more importantly, a classification of the literature which have reported the deployment of such techniques for RAS-based inspection and monitoring of utility pipelines, wind turbines, aircrafts, power lines, pressure vessels, bridges, etc. Our research provides documented information on the use of advanced data-driven technologies in the analysis of critical assets and examines the main challenges to the applications of such technologies in the industry.

This work is largely concerned with trajectory tracking in linear complementarity systems (LCS). The main analytical tool for stability analysis and control design is passivity and the associated linear matrix inequalities (LMIs) for feedback passification. Cases with and without state-jumps, with and without parametric uncertainties, are analyzed. Theoretical findings are illustrated with examples from circuits with set-valued, nonsmooth electronic components, networks with unilateral interactions, and mechanical system with unilateral spring. [ABSTRACT FROM AUTHOR]

Data-driven artificial intelligence models play an important role in mechanical fault diagnosis. Generally, it is difficult to collect relative complete fault samples, which limits the application of artificial intelligence models for complex mechanical systems. To address this issue, numerical model-based or called physical model-based fault sample generation methods attracted many attentions but still an open problem: the difference in fault samples between numerical simulation and measurement of a physical system needs to be well decreased. Therefore, a noise generative network (NGN) for mechanical fault classification is developed. First, the NGN is trained using the simulation and measurement normal samples. The simulation fault samples are further fed into the trained NGN to obtain more solid simulation fault samples with minor differences from measurements of mechanical systems with faults. Second, deep convolutional neural network is trained by solid simulation fault samples, and the test samples of unknown fault types will be finally recognized. Finally, validation experiments using rotation vector reducers, bearings, and rotors classified test samples with average accuracies of 99.5%, 95.7%, and 99.9%, respectively. They indicate that the proposed NGN model effectively reduces the difference between the simulation and measurement samples to lead more precision results for fault classification in mechanical systems. [Display omitted] [ABSTRACT FROM AUTHOR]

We study the problem of seeking the source of a scalar signal with a force- and torque-controlled unicycle. The vehicle does not have the capability to measure or track its position. The only information about the vehicle's position relative to the source is given by measurements of the signal. We propose a feedback law that induces a uniform forward motion and an almost uniform rotational motion. This in turn leads to an almost circular motion of the unicycle; i.e., a motion along the boundary of a disk. An application of the divergence theorem reveals that the measurements of the signal along the boundary of the disk provide access to the gradient of an averaged signal, where the average is taken over the entire disk. Such a local average of the sensed signal can eliminate undesired local extrema, which in turn is beneficial for the goal of reaching a global extremum. We prove practical asymptotic stability for the closed-loop system with respect to extrema of the averaged signal. Our theoretical analysis takes L ∞ -measurement errors of the signal into account. We compare the performance of the proposed control law to another source seeking method in the presence of local extrema through numerical simulations. [ABSTRACT FROM AUTHOR]

Kinematics is a fundamental topic in engineering, robotics, mechatronics, and control systems and significantly resolves some of these fields’ most pressing issues. It is essential to assess the balance between a topic’s theoretical framework and its empirical validation to succeed in engineering. Educational tools have gained significant attention for their ability to enhance the learning experience by providing the hands-on experiences necessary to assess theoretical frameworks and empirical validations. This paper presents a system incorporating state-of-the-art features, including a fuzzy controller enabling precise control of a linear actuator and a USB camera, to provide an interactive experience. The USB camera captures the position of the actuator, providing real-time visual feedback and allowing the students to validate their theoretical understanding through practical experiments. Precision, accuracy, resolution, and the implementation of the fuzzy controller are measured to evaluate the whole system’s performance. The design, implementation, and control of our educational electrical linear actuator for teaching kinematics concepts contribute to a practical educational tool and advance interactive learning approaches in the field.