Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method.

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step n and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

Reinforcement learning-based consensus control for MASs with intermittent constraints.

In this article, an adaptive optimal consensus control problem is studied for multiagent systems in the strict-feedback structure with intermittent constraints (the constraints appear intermittently). More specifically, by designing a novel switch-like function and an improved coordinate transformation, the constrained states are converted into unconstrained states, and the problem of intermittent constraints is resolved without requiring "feasibility conditions". In addition, using the composite learning algorithm and neural networks to construct the identifier, a simplified identifier-actor-critic-based reinforcement learning strategy is proposed to obtain the approximate optimal controller under the framework of backstepping. Meanwhile, with the aid of the nonlinear dynamic surface control technique, the issue of "explosion of complexity" in backstepping is removed, and the requirements for filter parameters are loosened. Based on Lyapunov stability theory, it is demonstrated that all signals in the closed-loop system are bounded. Finally, two simulation examples are used to verify the effectiveness of the proposed method.

Bouncing dynamics of droplets on nanopillar-arrayed surfaces: the effect of impact position.

The impact behaviors of droplets on nanostructure-arrayed surfaces are ubiquitous in nature and engineering applications. And the influence of the impact position of a droplet on its bouncing dynamics is of great significance since there is inevitable randomness in the impact position of droplets on the surface of nanostructured arrays, but the difference in the dynamics process caused by this randomness has not been recognized. Here, by using molecular dynamics simulations, the effect of impact position on the bouncing dynamics of a water droplet on nanopillar-arrayed surfaces is systematically investigated. The simulation results highlight that the impact position plays an important role in droplet dynamics after impact, especially at the retraction stage, and the effect of impact position on the bouncing behavior is highly sensitive to the impact velocity. Importantly, from the point of energy conversion, the droplet deformation and contact state jointly determine whether the droplet can bounce back or not, which reveals the mechanism of impact position effects on the bouncing behavior of the droplet. Interestingly, the effect of impact position would be weakened with an increase in the size ratio of the droplet diameter to nanopillar spacing, and this effect becomes negligible when the size ratio is greater than 5.2. These findings demonstrate the key role played by the impact position and may provide new insights into the practical application of nanostructure-arrayed surfaces.

Security Analysis for Dynamic State Estimation of Power Systems With Measurement Delays.

This article is centered on the cybersecurity research of dynamic state estimation for power systems with measurement delays. Relying on mixed measurements from phasor measurement units (PMUs) and remote terminal units (RTUs), a delayed measurement model is constructed. A modified state estimator based on the Kalman filter (KF) is designed, which can obtain the optimal estimated states under measurement delays. Moreover, the measurement data transmitted from the sensor to the estimator are vulnerable to cyberattacks. Especially, false data-injection (FDI) attacks are frequently encountered in the power system state estimation (PSSE) process. In the case of measurement delays, an FDI attack strategy is designed to interfere with the state estimator and evade detection by the chi-square detector. By utilizing the attacked estimated information and the uncorrupted measurement information, two measurement residual vectors are designed. According to these two residual vectors, a chi-square-based attack detection method is proposed, which has the ability to detect the attack without being affected by the delayed measurements. The proposed KF algorithm and attack detection method are implemented on an IEEE 14-bus system and they are confirmed to be effective and feasible.

Observer-Based Neural Control of N -Link Flexible-Joint Robots.

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Prescription patterns of sodium and calcium polystyrene sulfonate in patients with hyperkalemia and chronic kidney disease receiving RAAS inhibitors.

Background: Sodium and calcium polystyrene sulfonate (SPS/CPS) cation-exchange resins have had long-standing clinical use for hyperkalemia in patients with chronic kidney disease (CKD). However, uncertainty exists regarding the real-world usage of SPS/CPS for acute and chronic management of hyperkalemia. We evaluated the prescription patterns of SPS/CPS and their impact on renin-angiotensin-aldosterone system inhibitor (RAASi) treatment in patients with CKD Stages G3-G5 after an episode of de novo hyperkalemia. Methods: We conducted a retrospective cohort study using population-level administrative databases in Manitoba, Canada, which included adults with CKD and a RAASi prescription who had an episode of de novo hyperkalemia (≥5.5 mmol/L) between January 2007 and December 2017. Results: A total of 10 009 individuals were included in our study cohort. Among the study population, 4% received an SPS/CPS prescription within 30 days of their hyperkalemia episode. Of those, 22% received a 1-day supply of SPS/CPS and 7% received a prescription for more than 30 days. There were 8145 patients using RAASi at baseline who survived 90 days after their first hyperkalemia episode. Of those, 1447 (18%) discontinued their RAAS inhibitor and 339 (5%) received a prescription of SPS/CPS. Also, the proportion of patients who discontinued their RAASi was similar among those who did and did not receive a prescription of SPS/CPS. Conclusion: In patients with CKD receiving RAASi therapy, there is a low frequency of SPS/CPS prescription after an episode of hyperkalemia. RAASi discontinuation or downtitration is the most used pharmacologic approach for the management of hyperkalemia, a strategy that deprives patients of the cardiac and renal protective benefits of RAASi. New options for the management of hyperkalemia in this population are needed.

Distributed Finite-Time Containment Control for Nonlinear Multiagent Systems With Mismatched Disturbances.

This article proposes a finite-time adaptive containment control scheme for a class of uncertain nonlinear multiagent systems subject to mismatched disturbances and actuator failures. The dynamic surface control technique and adding a power integrator technique are modified to develop the distributed finite-time adaptive containment algorithm, which shows lower computational complexity. In order to overcome the difficulty from the mismatched uncertainties, the disturbance observers are constructed based on the backstepping technique. Moreover, the uncertain actuator faults, including loss of effectiveness model and lock-in-place model, are considered and compensated by the proposed adaptive control scheme in this article. According to the Lyapunov stability theory, it is demonstrated that the containment errors are practically finite-time stable in the presence of actuator faults. Finally, a simulation example is conducted to show the effectiveness of the proposed theoretical results.

Synchronization of Complex Dynamical Networks Subject to Noisy Sampling Interval and Packet Loss.

This article focuses on the sampled-data synchronization issue for a class of complex dynamical networks (CDNs) subject to noisy sampling intervals and successive packet losses. The sampling intervals are subject to noisy perturbations, and categorical distribution is used to characterize the sampling errors of noisy sampling intervals. By means of the input delay approach, the CDN under consideration is first converted into a delay system with delayed input subject to dual randomness and probability distribution characteristic. To verify the probability distribution characteristic of the delayed input, a novel characterization method is proposed, which is not the same as that of some existing literature. Based on this, a unified framework is then established. By recurring to the techniques of stochastic analysis, a probability-distribution-dependent controller is designed to guarantee the mean-square exponential synchronization of the error dynamical network. Subsequently, a special model is considered where only the lower and upper bounds of delayed input are utilized. Finally, to verify the analysis results and testify the effectiveness and superiority of the designed synchronization algorithm, a numerical example and an example using Chua's circuit are given.

Event-Triggered Output-Feedback Control for Large-Scale Systems With Unknown Hysteresis.

This article focuses on the event-triggered-based adaptive neural-network (NN) control problem for nonlinear large-scale systems (LSSs) in the presence of full-state constraints and unknown hysteresis. The characteristic of radial basis function NNs is utilized to construct a state observer and address the algebraic loop problem. To reduce the communication burden and the signal transmission frequency, the event-triggered mechanism and the encoding-decoding strategy are proposed with the help of a backstepping control technique. To encode and decode the event-triggering control signal, a one-bit signal transmission strategy is adopted to consume less communication bandwidth. Then, by estimating the unknown constants in the differential equation of unknown hysteresis, the effect caused by unknown backlash-like hysteresis is compensated for nonlinear LSSs. Moreover, the violation of full-state constraints is prevented based on the barrier Lyapunov functions and all signals of the closed-loop system are proven to be semiglobally ultimately uniformly bounded. Finally, two simulation examples are given to illustrate the effectiveness of the developed strategy.

Optimal Filtered and Smoothed Estimators for Discrete-Time Linear Systems With Multiple Packet Dropouts Under Markovian Communication Constraints.

This paper concentrates on the linear least mean square (LLMS) filtered and smoothed estimators for networked linear stochastic systems. Multiple packet losses, Markovian communication constraints, and superposed process noise are considered simultaneously. In order to reduce the channel load during communication, at every step, just one transmission node is permitted to send data packets. Hence, a Markovian communication protocol is utilized to arrange the packets of these transmission nodes. Moreover, multiple data packet dropouts occur during transmission due to an imperfect communication channel. Therefore, the global observation information cannot be obtained by the state estimator. The real state of Markov chain is assumed to be unknown to the estimator except the transition probability matrix. By means of the innovation analysis approach and orthogonal projection principle, we design Kalman-like estimators in a recursive form. Finally, through simulation experiments, we verify the effectiveness and superiority of the designed algorithm.

Thermal Conductivity of Two Types of 2D Carbon Allotropes: a Molecular Dynamics Study.

The thermal properties of the two novel 2D carbon allotropes with five-five-eight-membered rings are explored using molecular dynamics simulations. Our results reveal that the thermal conductivity increases monotonically with increasing size. The thermal conductivities of infinite sizes are obtained by linear relationships of the inverse length and inverse thermal conductivity. The converged thermal conductivity obtained by extrapolation in the reverse non-equilibrium molecular dynamics method is found to be in reasonable agreement with that in the equilibrium molecular dynamics method. The much lower thermal conductivity, compared with graphene, is attributed to the lower phonon group velocity and phonon mean free path. Temperature and strain effects on thermal conductivity are also explored. The thermal conductivity decreases with increasing temperature and it can also be tuned through strain engineering in a large range. The effect of strain on TC is well explained by spectra analysis of phonon vibration. This study provides physical insight into thermal properties of the two carbon allotropes under different conditions and offers design guidelines for applications of novel two-dimensional carbon allotropes related devices.

Liquid-Liquid Phase Transition in Nanoconfined Silicon Carbide.

We report theoretical evidence of a liquid-liquid phase transition (LLPT) in liquid silicon carbide under nanoslit confinement. The LLPT is characterized by layering transitions induced by confinement and pressure, accompanying the rapid change in density. During the layering transition, the proportional distribution of tetracoordinated and pentacoordinated structures exhibits remarkable change. The tricoordinated structures lead to the microphase separation between silicon (with the dominant tricoordinated, tetracoordinated, and pentacoordinated structures) and carbon (with the dominant tricoordinated structures) in the layer close to the walls. A strong layer separation between silicon atoms and carbon atoms is induced by strong wall-liquid forces. Importantly, the pressure confinement phase diagram with negative slopes for LLPT lines indicates that, under high pressure, the LLPT is mainly confinement-induced, but under low pressure, it becomes dominantly pressure-induced.

Electronic transport properties of ultra-thin Ni and Ni-C nanowires.

The structures and electronic transport properties of ultra-thin Ni and Ni-C nanowires obtained from carbon nanotube (CNT) templates are theoretically investigated. C atoms tend to locate at the central positions of nanowires and are surrounded by Ni atoms. Spin polarization at the Fermi level is not responsible for the spin filtration of these nanowires. Increasing C concentration can improve the resistance of nanowires by abating the number of electronic transmission channels and the coupling of electron orbitals between Ni atoms. Moreover, with the increase of diameter, the conductance of these nanowires increases as well. This study is helpful for guiding the synthesis of nanowires with desired applications.

Wettability and Coalescence of Cu Droplets Subjected to Two-Wall Confinement.

Controlling droplet dynamics via wettability or movement at the nanoscale is a significant goal of nanotechnology. By performing molecular dynamics simulations, we study the wettability and spontaneous coalescence of Cu droplets confined in two carbon walls. We first focus on one drop in the two-wall confinement to reveal confinement effects on wettability and detaching behavior of metallic droplets. Results show that Cu droplets finally display three states: non-detachment, semi-detachment and full detachment, depending on the height of confined space. The contact angle ranges from 125° to 177°, and the contact area radius ranges from 12 to ~80 Å. The moving time of the detached droplet in the full detachment state shows a linear relationship with the height of confined space. Further investigations into two drops subjected to confinement show that the droplets, initially distant from each other, spontaneously coalesce into a larger droplet by detachment. The coalescing time and final position of the merged droplet are precisely controlled by tailoring surface structures of the carbon walls, the height of the confined space or a combination of these approaches. These findings could provide an effective method to control the droplet dynamics by confinement.

Multiple helical configuration and quantity threshold of graphene nanoribbons inside a single-walled carbon nanotube.

Molecular dynamics simulation has been carried out to explore the configuration and quantity threshold of multiple graphene nanoribbons (GNRs) in single-walled carbon nanotube (SWCNT). The simulation results showed that several GNRs tangled together to form a perfect spiral structure to maximize the π-π stacking area when filling inside SWCNT. The formation of multiple helical configuration is influenced by the combined effect of structure stability, initial arrangement and tube space, meanwhile its forming time is related to helical angle. The simulated threshold of GNRs in SWCNT decreases with GNR width but increases with SWCNT diameter, and two formulas have come up in this study to estimate the quantity threshold for GNRs. It has been found that multilayered graphite is hard to be stripped in SWCNT because the special helical configuration with incompletely separated GNRs is metastable. This work provides a possibility to control the configuration of GNR@SWCNT.

Interfacial structure and wetting properties of water droplets on graphene under a static electric field.

The behavior of water droplets located on graphene in the presence of various external electric fields (E-fields) is investigated using classical molecular dynamics (MD) simulations. We explore the effect of E-field on mass density distribution, water polarization as well as hydrogen bonds (H-bonds) to gain insight into the wetting properties of water droplets on graphene and their interfacial structure under uniform E-fields. The MD simulation results reveal that the equilibrium water droplets present a hemispherical, a conical and an ordered cylindrical shape with the increase of external E-field intensity. Accompanied by the shape variation of water droplets, the dipole orientation of water molecules experiences a remarkable change from a disordered state to an ordered state because of the polarization of water molecules induced by static E-field. The distinct two peaks in mass density and H-bond distribution profiles demonstrate that water has a layering structure in the interfacial region, which sensitively depends on the strong E-field (>0.8 V nm(-1)). In addition, when the external E-field is parallel to the substrate, the E-field would make the contact angle of the water droplets become small and increase its wettability. Our findings provide the possibility to control the structure and wetting properties of water on graphene by tuning the direction and intensity of external E-field which is of importance for relevant industrial processes on the solid surface.

Synergy and pinning effects in a monatomic liquid film in confined conditions.

The freezing behavior of a monatomic liquid film in confined conditions during rapid cooling was studied by molecular dynamics simulations. We illustrated the synergy and pinning effects of the local icosahedral order during freezing. Our results show that the icosahedron contributes to nucleation through the synergy with other short-range ordered structures and participates in crystal growth via assimilation, but the pinning effect should be overcome when crystals grow. Furthermore, a semi-ordered morphology with maze-like nano-patterns emerged due to the cooperation between the synergy effect and the pinning effect. Our findings shed light on the correlation between the local icosahedral order and the crystalline medium-range order, providing a better understanding of the rapid solidification.

Coalescence of water films on carbon-based substrates: the role of the interfacial properties and anisotropic surface topography.

Molecular dynamics (MD) simulations are carried out to study the coalescence of identical adjacent and nonadjacent water films on graphene (G), vertically or horizontally stacked carbon nanotube arrays (VCNTA and HCNTA respectively). We highlight the key importance of carbon-based substrates in the growth of the liquid bridge connecting the two water films. This simulation provides reliable evidence to confirm a linear increase of the liquid bridge height, which is sensitive to the surface properties and the geometric structure. In the case of nonadjacent water films, the meniscus liquid bridge occurs solely on the VCNTA, which is attributed to the spreading of water films driven by the capillary force. Our results provide an available method to tune the coalescence of adjacent or nonadjacent films with alteration of topographically patterned surfaces, which has important implications in the design of condensation, ink-jet printing and drop manipulation on a substrate.