Tracking Properties Research Articles

Advanced Electron Microscopy Techniques for PGM-Free Catalyst Characterization

Hydrogen fuel cells currently rely on expensive platinum group metal nanoparticle catalysts [1]. For green hydrogen production and utilization to become widely commercially viable, the cost of the devices that produce and utilize hydrogen must be significantly reduced. Platinum group metal-free (PGM-free) catalysts have the potential to greatly reduce this cost, and materials consisting of single transition metal atoms embedded in a nitrogen-doped graphitic carbon structure have shown particular promise for use as fuel cell cathodes [2]. A better understanding of the active site properties in these materials is still needed, however, to improve their stability and design new active site structures with enhanced properties [3]. Due to the atomic-scale nature of the active sites in these materials, scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy (EELS) have proven invaluable for demonstrating their atomically dispersed nature and composition [4]. Conventional STEM techniques have limited ability to correlate the local bonding environment and oxidation state of the metal atoms, for example, or track changes in the catalyst structure both during synthesis and as a result of cycling, which would provide a deeper understanding of the relationship between active site and catalyst properties.Here, we demonstrate advanced electron microscopy techniques that provide both enhanced and previously inaccessible information about PGM-free catalysts and their active sites. We show developments in automated identification of metal atom positions, which we use both to generate statistics about interatomic distances and to automatically position the STEM probe on individual atoms for EELS data acquisition. The former allows information about the presence of dual-metal site structures to be extracted, for example, and the latter allows compositional information with improved SNR to be obtained. Rapid automatic probe positioning also presents the opportunity for measuring the effect of local bonding environment on metal atom oxidation state, which cannot be obtained manually since these sites are typically unstable under the beam. In addition, we will show identical-location STEM (IL-STEM) techniques that allow the evolution of catalyst morphology and properties to be tracked at high resolution across synthesis steps and accelerated stress tests [5]. In particular, we use IL-STEM imaging and EELS to track deposition of graphitic material on the surface of a PGM-free catalyst that significantly improves the material’s durability, as well as track the change in the nanoscale graphitic carbon structure of the material as a function of electrochemical cycling. By providing access to enhanced compositional and bonding state information, as well as the ability to track properties as a material evolves, these techniques will advance our knowledge of PGM-free catalysts and enable better control over their properties in the future, accelerating wide-spread use of hydrogen fuel cells [6].

Passive robust control for uncertain Hamiltonian systems by using operator theory

In this study, the passivity-based robust control and tracking for Hamiltonian systems with unknown perturbations by using the operator-based robust right coprime factorisation method is concerned. For the system with unknown perturbations, a design scheme is proposed to guarantee the uncertain non-linear systems to be robustly stable while the equivalent non-linear systems is passive, meanwhile the asymptotic tracking property of the plant output is discussed. Moreover, the design scheme can be also used into the general Hamiltonian systems while the simulation is used to further demonstrate the effectiveness of the proposed method.

Powder-bed-fusion additive manufacturing of molybdenum: Process simulation, optimization, and property prediction

In this paper, the whole process of laser powder-bed-fusion (LPBF) additive manufacturing (AM) in the fabrication of molybdenum (Mo) material was numerically reproduced. Firstly, parameters of Mo powder with continuous size distribution utilized in actual AM production were calibrated and input into discrete element model (DEM) for parametric study on powder spreading. Then, the laser melting of the spread powder bed was simulated by computational fluid dynamics (CFD). On this basis, a back propagation neural network (BPNN) model was built for powder bed quality and molten track property prediction and evaluation. Results show that properly reducing the spreading velocity V or increasing the gap height H can contribute to the optimal structure of the powder bed. Corresponding mechanism analyses reveal that the residual velocity of particles and force chain block are the main reasons for the decrease of powder bed quality. And the molten track performance is not positively correlated with the packing density of the powder bed due to the defects such as balling and porosity caused by the over-thickness of powder bed. The selection rule of powder bed should satisfy as higher as possible the packing density within the bed thickness threshold. The BPNN model can accurately predict the powder bed quality and the molten track property and develop a reasonable map for the appropriate choice of operating parameters in real processes.

Equivalent-Input-Disturbance-Based Dynamic Tracking Control for Soft Robots via Reduced-Order Finite-Element Models

This article develops a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">systematic</i> framework for dynamic tracking control of soft robots. To this end, we propose a new projector for the proper orthogonal decomposition algorithm to significantly reduce the large-scale robot models, obtained from finite-element methods (FEM), while preserving their structure and stability properties. Such a property preservation enables an effective equivalent-input-disturbance-based scheme for dynamic tracking control of elastic soft robots with various geometries and materials. The proposed control scheme is composed of three key components, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , feedforward control, disturbance-estimator control, and feedback control. To account for the trajectory reference, the feedforward action is designed from the dynamic FEM reduced-order robot model. The disturbance-estimator control action is obtained from an unknown input observer, which also provides the estimates of the reduced states for the feedback control design. The feedback gains of the observer-based controller are computed from an optimization problem under linear matrix inequality constraints. The closed-loop tracking properties are guaranteed using the Lyapunov stability theory. The effectiveness of the proposed dynamic control framework has been demonstrated via both high-fidelity SOFA simulations and experimental validations, performed on two soft robots with different natures. In particular, comparative studies with state-of-the-art control methods have been also carried out to highlight the interests of the new soft robot control results. This article is complemented with a video: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://bit.ly/2VVwtLn</uri> .

Analysis on Damage and Mechanical Properties of Ballastless Track in a Tunnel after a Fire.

In order to explore the damage and mechanical properties of ballastless track after a fire, the uniaxial compressive strength, shear strength, peak strain, and elastic modulus changes due to temperature were obtained through uniaxial compressive and shear tests of concrete after exposure to high temperatures. The test results showed that with increases in temperature, the uniaxial compressive strength, shear strength, and elastic modulus of concrete all presented a decreasing trend, while the peak strain had an increasing trend. Then, based on the classical damage theory model and the strength probability distribution function of concrete micro-units, the high-temperature damage constitutive equation for concrete was established, and the compressive stress–strain curve of concrete after exposure to high temperature was reproduced. Finally, using the CFD numerical simulation software, the temperature field of a ballastless track structure in a tunnel during a fire was obtained, and the temperatures at different positions of ballastless track bed were acquired. Combined with the high-temperature damage constitutive equation for concrete deduced from tests and theoretical analysis, the strength and damage values of the ballastless track bed at different positions after a tunnel fire were obtained.

Determining Map, Data Assimilation and an Observable Regularity Criterion for the Three-Dimensional Boussinesq System

In this paper, we consider the analysis of a nudging based algorithm for data assimilation for the three-dimensional Boussinesq system, which we call the AOT system. A rigorous analysis of this algorithm for well-posed dissipative partial differential equations was first provided by Azouani, Olson and Titi (J Nonlinear Sci 24:277–304, 2014); thus justifying our terminology for the associated nudging system. We provide a sufficient condition, based solely on the observed velocity data obtained from a Leray-Hopf weak solution, for the global well-posedness, regularity and most crucially, the asymptotic tracking property of solutions of the associated (three-dimensional) nudging system. It is to be noted that neither regularity nor any knowledge of a uniform \({\mathbb {H}}^1\)-norm bound is a priori assumed on the solution of the original three-dimensional Boussinesq system from which the observations are obtained. As a corollary of our result, we obtain a novel observable regularity criterion based on finitely many observational data. Our condition also guarantees the construction of the Lipschitz continuous determining map, which is known to play a crucial role in the construction of the so-called determining form and in statistical data assimilation.

LiDAR-based detection, tracking, and property estimation: A contemporary review

Object detection, Person tracking, and Person property estimation (PPE) are identical innovation areas trying to improve their accuracy in different parameters to fit various real applications. For many years, so much research has been done in these fields. Many scientists also used many more techniques and algorithms. But most of the innovations were deeply based on image-based analysis, where cameras were the critical components of data acquisition. Over the years, new technologies arrived, and different types of research are happening. Rather than cameras, some other sensors, like infrared, depth cameras, and very recently LiDAR sensors, are used to estimate person properties, track them, as well as to detect them. Especially, height, age, gender, region, etc., parameters can be measured as person property. Eventually, 3D object detection by LiDAR will be a state-of-the-art research field with the advent of autonomous driving initiations. We studied many articles and found enthusiastic outcomes with these sensor setups to understand contemporary technology and its efficacy. We categorized these research articles into video camera-based studies and other sensor-based studies. So many surveys have been done on video-based analysis, even with deep learning techniques. Another sensor-based research is very recent, and we do not get enough study on it. We thought to summarize these studies in a survey article, especially LiDAR-based analysis. This article covered most of the recent possible sensor-based studies of detection, person tracking and property estimation except cameras (all, RGB, RGB-D, etc.) based learning.

Global robust attitude tracking with torque disturbance rejection via dynamic hybrid feedback

In this paper an approach to the design of robust global attitude tracking controllers for fully actuated rigid bodies is proposed. The challenge of simultaneously dealing with topological obstructions to global attitude tracking and with disturbances affecting the attitude dynamics is tackled by means of a hybrid hierarchical design that exploits the cascade structure of the underlying mathematical model. The proposed hierarchical strategy is based on an inner–outer loop paradigm comprising a dynamic control law for angular velocity tracking (inner loop) and a hybrid control law for attitude tracking (outer loop). By leveraging recent tools for the stability analysis of hybrid systems, we prove a robust global tracking property by assuming mild properties on the dynamics of the velocity feedback. We also discuss a few relevant examples satisfying these properties, encompassing harmonic disturbance compensators and conditional integrators, capable of rejecting unknown constant disturbances with an intrinsic anti-windup action.

Hierarchical Structure-Based Fault-Tolerant Tracking Control of Multiple 3-DOF Laboratory Helicopters

This study proposes a hierarchical structure-based fault-tolerant tracking control methodology for multiple 3-DOF helicopters in the presence of system nonlinearities, uncertainties and simultaneous actuator faults (partial loss of effectiveness, stuck, and saturation), and sensor faults (bias and drift). The hierarchical structure consists of the decentralized fault estimation hierarchy and distributed fault-tolerant tracking control hierarchy. The distributed constant gain-based, node-based, and edge-based adaptive fault-tolerant tracking control designs are developed to cope with bidirectional interactions and to guarantee the robust asymptotic stability and the good tracking property of multihelicopter systems, respectively. Simulation results validate the effectiveness of the proposed hierarchical structure-based tracking control algorithm.

Adaptive Practical Optimal Time-Varying Formation Tracking Control for Disturbed High-Order Multi-Agent Systems

The adaptive practical optimal time-varying formation tracking problems of the disturbed high-order multi-agent systems with a noncooperative leader are considered. Different from the former achievements, the effects of the leader’s unknown control input and followers’ external disturbances are both considered in the optimal time-varying formation tracking issues. Firstly, an adaptive practical optimal time-varying formation tracking protocol is proposed. The extended state observers and adaptive neural networks are introduced to estimate the integrated uncertainty and value function for the adaptive protocol, respectively. Then, an algorithm is presented to determine the control parameters for the adaptive optimal protocol and neural networks weights update laws. Thirdly, the stability and the optimal formation tracking property are analyzed for the closed loop disturbed high-order multi-agent system. Finally, the numerical simulation results are presented for revealing the effectiveness of the obtained theoretical methods.

Active suspension control with consensus strategy for dynamic posture tracking of wheel-legged robotic systems on uneven surfaces

This work presents a dynamic posture tracking control strategy for wheel-legged systems on uneven surfaces. Based on the kinematic model of a wheel-legged robotic system, the expected positions for the end-effectors of wheel-legs are calculated according to posture references and sensor feedback. The position control problem for a general wheel-leg is investigated for the active mechanism to imitate a passive suspension and respond to the external contact forces. The position tracking accuracy of the wheel-leg is sacrificed to enhance the compliance performance under rough terrain. Because of the unique contact state with the uneven ground for each wheel-leg, the position responses are different. As a result, the forces from the wheel-legs to the fuselage are inconsistent, which leads to the risk of posture oscillations. Equipping the wheel-legs with an undirected communication network, a consensus scheme for the robotic system is developed with proven global asymptotic stability to improve the posture tracking property. A novel robotic system is established with Stewart-structured wheel-legs, which are connected by a user datagram protocol network. Comparative experimental results are carried out on the physical prototype to validate the effectiveness of the proposed approach.

The equivalent condition of G-asymptotic tracking property and G-Lipschitz tracking property

Abstract In this paper, we introduce the concepts of G G -Lipschitz tracking property and G G -asymptotic tracking property in metric G G -space and obtain the equivalent conditions of G G -asymptotic tracking property in metric G G -space. In addition, we prove that the self-map f f has the G G -Lipschitz tracking property if and only if the shift map σ \sigma has the G ¯ \overline{G} -Lipschitz tracking property in the inverse limit space under the topological group action. These results generalize the corresponding results in [Proc. Amer. Math. Soc. 115 (1992), 573–580].

Modelling ballast via a non-linear lattice to assess its compaction behaviour at railway transition zones

Locations in railway tracks where significant variations of the track properties occur are subject to increased track deterioration. To successfully mitigate this, the mechanisms leading to the increased deterioration need to be understood. To this end, this work presents a non-linear constitutive law for a lattice model able to describe the compaction behaviour of railway ballast. The parameters of the non-linear connections are tuned against lab experiments of cyclic loading tests and direct shear tests. The tuned lattice can be used with different foundation properties provided that the ballast in the track is equivalent to that of the tests. The non-linear lattice model is applied to the case of railway transitions, for which ballast compaction under train loading is studied as a cause of geometry degradation. It is observed that for the studied cases of a culvert crossing and of a ballast-slab transition, the operation-induced compaction converges monotonously to a stable situation, without leading to significant changes in the vehicle-track interaction. Ballast compaction is therefore insufficient as a stand-alone mechanism to explain a process of progressive degradation of the track geometry. Other mechanisms like autonomous differential settlement at the foundation level must be taken into account in such cases.

Trajectory tracking and disturbance rejection control of random linear systems

A more general trajectory tracking and disturbance rejection problem for the random linear system is considered in this paper. The known structural properties of the reference and the disturbance signals motivate us to use the internal model principle, and the motion decomposition for the linear systems prompts us to utilize the superposition principle. Combining the spectral analysis method with the pole assignment algorithm, the stability and tracking property can be obtained. The simulation result demonstrates that all the closed-loop system is exponentially practically stable in the mean square (EpS-2-M), the tracking error can be regulated to an arbitrarily small constant by turning the parameters.

Comparative Study of Symmetrical Controlled Grid-Connected Inverters

The asymmetrical dynamics of the conventional synchronous reference frame phase-locked loop (PLL) enhance the complexity of the system and endanger the stability of the grid-connected voltage source converters. In this article, the admittance modeling of three different symmetrical control strategies, i.e., voltage oriented control with symmetrical PLL, voltage modulated direct power control, and proportional–resonant control, is proposed and uniformed based on the same power tracking property. The voltage feedforward and decoupling of the controllers are both considered. Moreover, due to the symmetrical structure of the three control strategies, their admittance models can directly be modeled under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha \beta$</tex-math></inline-formula> reference frame as single-input single-output systems. In addition, to reveal the similarity between different control methods and emphasize their differences, the admittance models are constructed by using the same framework. The admittance modeling is verified using frequency-scan, and the control performance is tested using MATLAB/Simulink Simscape Power System and experimental prototype. Finally, the stability criterion based on the proposed admittance modeling is verified by simulation results carried out in real-time simulation platform RTLAB from Opal-RT.

Data-driven model predictive control design for offset-free tracking of nonlinear systems

We propose a design of data-driven Model Predictive Control (MPC) using a suboptimal trajectory and the linear time-varying (LTV) models from data-driven trajectory optimisation that achieves offset-free tracking. Data-driven constrained differential dynamic programming (CDDP) is exploited to improve the trajectory iteratively without the knowledge of the nonlinear model. A trajectory is divided to the transient and steady state regions, controlled by the Linear time-varying MPC (LTVMPC) and the offset-free linear MPC (LMPC), respectively. We prove the feasibility of the proposed LTVMPC in the transient region, and the offset-free tracking property of LMPC. The proposed scheme is validated to a continuous stirred tank reactor (CSTR) process. Simulation studies show that the suboptimal trajectory and LTV models are generated by CDDP, and the proposed MPC achieves offset-free tracking and disturbance rejection for a set of initial conditions and set points in the operating region.

Back-stepping control of delta parallel robots with smart dynamic model selection for construction applications

Applications of robotic manipulators in construction fields is notorious; however, changes in system dynamics in the presence of heavy external loads and disturbances in pick-and-place operations are inevitable. To elude this, a novel smart online dynamic model selection is introduced and accompanied by a back-stepping sliding mode controller which is implemented on a 3-Degrees-Of-Freedom (DOF) Delta Parallel Robot. In order to fit the dominant behavior of the disturbances, reduced-order extended models, based on external loads, are identified in an online manner; thereafter, an off-policy reinforcement learning approach is exploited for smart dynamic model selection. Consequently, a robust evolving controller emerges able to perform pick-and-place tasks under any configuration of external loads, resulting in better tracking properties in comparison to fitting a single external model. Data-driven methods have potential for further improving the external loads’ dominant behavior identification using the derived models’ kernels opening up new avenues as future works.

Machine Learning to Identify Dynamic Properties of Railway Track Components

Investigating the condition of rail track components is important for track maintenance and developing a greater understanding of track design. Railway inspection can be destructive and non-destructive approaches. In the railway industry, the non-destructive approaches are preferred because they retain the track in operation thus significantly reducing the cost of fault testing. One of the non-destructive approaches is using machine learning which is applied in this study. Field measurements and advanced analysis of results are used to extract track properties. This study creates, tunes and examines the validity of different machine learning techniques. The aim is to extract the dynamic track properties from the in-field measurements without needing the intermediary steps, saving both time and effort. Contributions of this study demonstrate that machine learning techniques have the potential to save cost and time for railway inspection. Moreover, the accuracy is satisfied. The following models are produced: Linear Regression, K-Nearest Neighbors, Gradient Boosting and a Convolutional Neural Network. We observe the limitations of linear regression and tune the remainder, producing three models with low errors.

Estimation of the nano-pores diameter by conductometric measurements

s. Ionic transport properties and diameter of asymmetric conical nano-pores in polyethylene terephthalate membranes were calculated. These nano-pores were synthesized by chemical etching method in irradiated polyethylene terephthalate (PET) films. After etching, the transport characteristics weredocumented under stepping voltage with 2M ionic concentrations of Potassium halides i.e. KCl, KBr and KI. The symmetric bathing condition (same temperature and same pH value solution in both side of chamber) in electrochemical cell were accomplished. It is investigated that diameter of nano-pores is also evaluate by measuring the current rectification properties of track etched nano-pores.

Application of FE-SEA approach in investigation of track properties influences on railway rolling noise generation

One of the main concerns in the railway transportation is environmental pollutions caused by railway noise. The most important source of this noise is the rolling contact between wheel and rail, and therefore, prediction of the rolling noise is a key factor in the management and reduction of the noise. Despite numerous studies carried out on the rolling noise prediction in the available literature, the role of track components on the rolling noise has not been taken into account. That is, the power dissipation within the track superstructures has not been given sufficient consideration. This is addressed in this research by investigating the influences of the track components on the level of railway rolling noise. For this purpose, a new noise prediction approach is developed in this research using finite element method and the statistical energy analysis. This approach has an advantage of considering power dissipation along the track components compared with the previous approaches. The results obtained from the new approach were compared with field measurements carried out in this research. It is shown that the proposed finite element–statistical energy analysis approach is effective in accurate predictions of the ballasted railway track noise.