Showing papers by "Jinhu Lu published in 2020"

Distributed Consensus of Layered Multi-Agent Systems Subject to Attacks on Edges

Abstract: This paper addresses the consensus problem for two-layered MASs (multi-agent systems) subject to attacks on communication edges. Unlike most existing two-layered MAS models, the considered MASs are allowed to have heterogeneous inner communication topologies between the different layers. By using the linear transformation technique, some sufficient criteria are first given to achieve consensus among the leaders within the leader layer where the condition that the interaction topology has directed spanning trees with a fixed root required in most existing works has been removed. Furthermore, based on the relative outputs, observer-based controllers are designed to achieve node-to-node consensus among the two layers. By developing a new kind of multiple Lyapunov function (MLF) based on a bisection search method, some criteria are established under which the node-to-node consensus error will converge into a bounded set asymptotically. It is interestingly found that a less conservative average dwell time (ADT) constraint is obtained for achieving node-to-node consensus by utilizing the proposed MLF compared with that yielded by the traditional MLF constructed from nonsingular $M$ matrix theory. Moreover, we show that consensus can be achieved asymptotically in the layered MASs with the same inner topology subject to synchronous attacks under some suitable conditions. Finally, the effectiveness of the criteria is verified by simulation on networking linear oscillators.

Fixed-Time Synchronization of Complex Dynamical Networks: A Novel and Economical Mechanism.

Abstract: Fixed-time synchronization of complex networks is investigated in this article. First, a completely novel lemma is introduced to prove the fixed-time stability of the equilibrium of a general ordinary differential system, which is less conservative and has a simpler form than those in the existing literature. Then, sufficient conditions are presented to realize synchronization of a complex network (with a target system) within a settling time via three different kinds of simple controllers. In general, controllers designed to achieve fixed-time stability consist of three terms and are discontinuous. However, in our mechanisms, the controllers only contain two terms or even one term and are continuous. Thus, our controllers are simpler and of more practical applicability. Finally, three examples are provided to illustrate the correctness and effectiveness of our results.

Fixed-Time Synchronization in the pth Moment for Time-Varying Delay Stochastic Multilayer Networks

Abstract: Since the speed that the pth moment of an output signal norm tends to 0 can be used to measure the quality of the output signal, the stability of the pth moment of complex networks has been widely concerned. This article studies fixed-time synchronization in the pth moment for time-varying delay stochastic multilayer networks, where fixed-time synchronization of multilayer networks is realized by using nonlinear feedback or delay feedback, and the concrete expression of fixed settling time is evaluated. Compared with some existing fixed-time control methods, the optimal combination relationship between the exponential power of fixed-time controllers and the pth moment is given, and the designed controllers are continuous function. Finally, the effectiveness of the method is verified by numerical simulations.

Synchronization of the Networked System With Continuous and Impulsive Hybrid Communications

Abstract: Many networked systems display some kind of dynamics behaving in a style with both continuous and impulsive communications. The cooperation behaviors of these networked systems with continuous connected or impulsive connected or both connected topologies of communications are important to understand. This paper is devoted to the synchronization of the networked system with continuous and impulsive hybrid communications, where each topology of communication mode is not connected in every moment. Two kind of structures, i.e., fixed structure and switching structures, are taken into consideration. A general concept of directed spanning tree (DST) is proposed to describe the connectivity of the networked system with hybrid communication modes. The suitable Lyapunov functions are constructed to analyze the synchronization stability. It is showed that for fixed topology having a jointly DST, the networked system with continuous and impulsive hybrid communication modes will achieve asymptotic synchronization if the feedback gain matrix and the average impulsive interval are properly selected. The results are then extended to the switching case where the graph has a frequently jointly DST. Some simple examples are then given to illustrate the derived synchronization criteria.

Topology Identification in Two-Layer Complex Dynamical Networks

Abstract: This study focuses on topology identification in two-layer networks with peer-to-peer unidirectional couplings, where one layer (the response layer) receives information from the other layer (the drive layer). The goal is to construct a theoretical framework for identifying the topology of the response layer based on the dynamics observed in both layers. In particular, an auxiliary layer is constructed. Based on the LaSalle-type invariance principle, simple control inputs and updating laws are designed to enable nodes in the auxiliary layer to reach complete synchronization with their counterparts in the response layer. Simultaneously, the topology of the response layer is adaptively identified. Numerical simulations are conducted to illustrate the effectiveness of the method. The impact of the inter-layer information transmission speed on the identification performance is further investigated. It is revealed that neither too slow or too fast information transmission favors efficient identification, and there exists an optimal level of transmission speed. The duplex framework can model many real-world systems, such as communication-rumor spreading networks. Therefore, the method proposed in this study can spark attention and provide basic insights into further theoretical research and practical application of multilayer networks, including detecting spreading routes and locating sources of rumors or pseudo news.

Stochastic Consensus Control Integrated With Performance Improvement: A Consensus Region-Based Approach

Abstract: The randomly switching communication topologies are of particular interest for researchers in the field of consensus control since the communication connection among multiple agents can randomly change with time and space. Following this pattern, this paper models the communication topology with the Markovian randomly switching graphs, since the Markovian chain process is usually used to model the network with Rayleigh fading channel. In this stochastic framework, this paper focuses on the consensus control and performance improvement problems of grouped agents with linear or linearized nominal dynamics. To solve this problem, a class of state-feedback controllers is proposed under the assumption that all the state measurements are available. These controllers are intrinsically distributed since they solely require the relative information between neighboring agents. Moreover, the notion of $\mathcal {H}_{\infty }$ ( $\mathcal {H}_2$ ) stochastic consensus region integrated with transient performance is defined to measure the robustness of the proposed controllers with respect to the communication topologies. This paper theoretically shows that these controllers yield unbounded consensus region, which implies that they are well-designed. Furthermore, these controllers can improve the $\mathcal {H}_{\infty }$ , $\mathcal {H}_2$ consensus, and transient performances of agents subject to external and initial-state disturbances. At the end of this paper, numerical simulations and experiments are performed to verify the theoretical results.

Intralayer Synchronization of Multiplex Dynamical Networks via Pinning Impulsive Control.

Abstract: These days, the synchronization of multiplex networks is an emerging and important research topic. Grounded framework and theory about synchronization and control on multiplex networks are yet to come. This article studies the intralayer synchronization on a multiplex network (i.e., a set of networks connected through interlayer edges), via the pinning impulsive control method. The topologies of different layers are independent of each other, and the individual dynamics of nodes in different layers are different as well. Supra-Laplacian matrices are adopted to represent the topological structures of multiplex networks. Two cases are considered according to impulsive sequences of multiplex networks: 1) pinning controllers are applied to all the layers simultaneously at the instants of a common impulse sequence and 2) pinning controllers are applied to each layer at the instants of distinct impulse sequences. Using the Lyapunov stability theory and the impulsive control theory, several intralayer synchronization criteria for multiplex networks are obtained, in terms of the supra-Laplacian matrix of network topology, self-dynamics of nodes, impulsive intervals, and the pinning control effect. Furthermore, the algorithms for implementing pinning schemes at every impulsive instant are proposed to support the obtained criteria. Finally, numerical examples are presented to demonstrate the effectiveness and correctness of the proposed schemes.

Leader-Following Pinning Synchronization of Multiagent Systems With Impulsive Interlayer Coupling

Abstract: This article is mainly concerned with leader-following pinning synchronization of multiagent systems in a multiplex network setting, where the interlayer couplings are impulsive. The goal is to enforce all the followers to asymptotically synchronize with the leader via impulsive pinning control. Firstly, based on the Lyapunov stability theory of impulsive differential equations, some general synchronization criteria are derived. The synchronization index is then defined. We explore how the synchronization index is effected by major network parameters, including the impulse strength, the impulse interval, the pinning gain and the number of layers. In particular, with equivalent impulse intervals, when both the impulse strength and the pinning gain are constant, it is found that the least number of pinned nodes needed for achieving complete synchronization (CS) decreases with decreasing impulse intervals, whereas it firstly decreases and then increases with increasing impulse strength and pinning gain. Furthermore, the synchronization region enlarges as the impulse interval decreases, while narrows as the number of layers increases. Numerical simulations verify the correctness of these results.

Adaptive PI Control for Synchronization of Complex Networks With Stochastic Coupling and Nonlinear Dynamics

Abstract: In the paper, synchronization of nonlinear dynamical complex networks with stochastic coupling under adaptive PI control is considered. In light of the relative states information of the network nodes, node- and edge-based adaptive PI synchronization protocols are proposed and several sufficient conditions are derived. By employing stochastic analysis techniques combined with a version of the LaSalle’s invariant principles applied to stochastic process and Lyapunov function approaches, synchronization of complex networks with nonlinear dynamics and stochastic coupling reaches in mean square via these two types of adaptive PI control protocols are proved. The proposed adaptive PI control protocols for synchronization of complex dynamical networks are useful for future realistic engineering systems design. Numerical examples are finally simulated to validate the theoretical analysis.

Constrained robust model predictive control embedded with a new data-driven technique

Abstract: In the control field, the adaptive model predictive control (AMPC) has the capability of taking effective control actions on unknown-but-bounded time-independent or slowly time-varying systems coupling with constraints. In essence, AMPC estimates the uncertain parameters or uncertainty set online by utilising historian data to extract model information. The model estimation procedure imposes some specific conditions on data and these extra conditions have restricted its practical use. To overcome these problems, a new data-driven control methodology is presented that integrates the data-driven concept into robust model predictive control (RMPC) architecture for unknown-but-bounded time-independent or slowly time-varying plant. The key novelty is to employ historian data to derive control policy and make a prediction in replacement with the complicated procedure of utilising data to estimate model parameters. A data-driven RMPC algorithm is developed within the robust model predictive control framework with the fulfilment of recursive feasibility and stability. The authors display the highlights of the data-driven model predictive control controller through two simulation examples. The resulting controller is verified to reduce conservativeness and increase the closed-loop performance of the system.

Recovering Network Structures With Time-Varying Nodal Parameters

Abstract: Complex networks with time-varying nodal parameters are of considerable interest and significance in many areas of science and engineering. Reconstructing networks with unknown but continuously bounded time-varying nodal parameters from limited measured information is desirable and of significant interest for using and controlling these networks. Based on the Lasso method and the Taylor expansion approximation, we develop an efficient and feasible, completely data-driven approach to predicting the structures of networks with unknown but continuously bounded time-varying nodal parameters in the presence or absence of noise. In particular, the reconstruction framework is implemented on several different kinds of artificial, two-layer and real complex networks composed of various parameter-varying nodal dynamics. Through numerical simulations, we demonstrate that, networks structures can be fully reconstructed with limited available information and presence or absence of noise, though systemic parameters are continuously time-varying. In addition, our method is also applicable to structure identification of multilayer networks as well as networks with constant nodal parameters. We expect our method to be useful in addressing issues of significantly current concern in the information era, natural networks, and large-scale multilayer networks.

Synchronization Analysis on Two-Layer Networks of Fractional-Order Systems: IntraIayer and InterIayer Synchronization

Abstract: The paper focuses on a two-layer network composed of fractional-order oscillators, and investigates the mesoscale collective dynamical behaviors of the network, i.e., intra-layer synchronization and inter-layer synchronization. Specifically, the necessary constraint conditions are firstly presented for achieving intra-layer synchronization and inter-layer synchronization. With the constraints, based on the Lyapunov stability theory and the fractional-order system theory, sufficient conditions for inter-layer synchronization and intra-layer synchronization are obtained respectively. Interestingly, the conditions cover the basic synchronization criteria bridging the relationship of interacting between the topologies, inner coupling functions and coupling strengthes of the intra-layer and the inter-layer. Finally, numerical examples are provided to further verify the theoretical results.

An Extended Stability Analysis Method for Paralleled DC-DC Converters System With Considering the Periodic Disturbance Based on Floquet Theory

Abstract: Paralleled DC-DC converters system normally is operated with current-sharing control to achieve equal current distribution among the converters connected in parallel. However, the added current-sharing control might make the paralleled DC-DC converters system suffer from potential degradation of stability and dynamic performances. Hence, the stability analysis of paralleled DC-DC converters system is a hot issue. In order to analyze the stability of paralleled DC-DC converters system accurately and effectively, an extended stability analysis method based on Floquet theory is proposed for paralleled DC-DC converters system in this paper with considering the periodic disturbance. And, a paralleled buck converters system with average current-mode control is taken as an analysis object. The extended stability criterion of paralleled buck converters system based on Floquet theory is derived. Finally, the simulation and experimental results verify the correctness and effectiveness of the proposed stability criterion with considering the periodic disturbance. This paper provides a new choice for the stability analysis of paralleled DC-DC converters system.

Semiglobal Consensus of a Class of Heterogeneous Multi-Agent Systems With Saturation

Abstract: This article addresses the leader-following consensus of a class of multi-agent systems (MASs) subjected to saturation. Unlike previous literature, the followers are with heterogeneous dynamics. To solve this problem, we employ the low-gain feedback technique and the parameterized algebraic Riccati equations to design the controllers. For the fixed and switching network topologies, sufficient conditions are put in place to guarantee the semiglobal stability of the consensus error system. Numerical results are also provided to validate the effectiveness of the control design.

Optimizing Synchronizability of Multilayer Networks Based on the Graph Comparison Method

Abstract: This paper is aimed at optimizing the synchronizability of a complex network when the total of its edge weights is given and fixed. We try to allocate edge weights on a complex network to optimize the network's synchronizability from the perspective of spectral graph theory. Most of the existing analysis on multilayer networks assumes the weights of intralayer or interlayer edges to be identical. Such a restrictive assumption is not made in this work. Using the graph comparison based method, different edge weights are allocated according to topological features of networks, which is more reasonable and consistent with most physical complex networks. Furthermore, in order to find out the best edge-weight allocation scheme, we carried out numerical simulations on typical duplex networks and real-world networks. The simulation results show that our proposed edge-weight allocation schemes outperform the average, degree-based, and edge betweenness centrality allocations.

Design and Virtex-7-Based Implementation of Video Chaotic Secure Communications

Abstract: In this paper, a Virtex-7-based video chaotic secure communication scheme is investigated. First, the network sending and receiving controller Intellectual Property (IP) cores are designed. Next, t...

Crowd Counting for Static Images: A Survey of Methodology

Abstract: Crowd counting for static images is one of the typical application fields of image processing. it is essential to direct crowd analysis for public security purposes under the background of a rapidly growing social environment. Crowd counting for static images involves crucial challenges such as occlusions, scale variations, scene perspective distortions and diverse crowd distributions. It has now become an independent research hotspot. This paper investigates the literature of crowd counting tasks, summarizes the evolution of counting approaches, compares and analyzes traditional methods and current trends of CNN estimation-based counting, and then evaluates common datasets for crowd counting tasks. Conclusions concerning summarizations and prospects are made.

Recovering node parameters and topologies of uncertain non-linearly coupled complex networks

Abstract: Node dynamics and network topologies play vital roles in determining dynamical behaviours and features of complex networks. Thus it is of great theoretical significance and practical value to identify the node parameters and topologies of uncertain complex networks. This study presents adaptive anticipatory synchronisation-based approaches to identify the unknown node parameters and topologies of uncertain non-linearly coupled complex networks perturbed by stochastic noise. Moreover, during the identification process, the authors proposed scheme guarantees anticipatory synchronisation between the uncertain drive and constructed response network simultaneously. In particular, their methods can be used to recover node parameters and topologies of complex networks in several special cases, such as cases without node delay or coupling delay. Furthermore, numerical simulations are provided to verify the applicability and effectiveness of their methods for recovering node parameters and topologies of uncertain non-linearly coupled complex networks.

Rethinking Motivation of Deep Neural Architectures

Abstract: Nowadays, deep neural architectures have acquired great achievements in many domains, such as image processing and natural language processing. In this paper, we hope to provide new perspectives for the future exploration of novel artificial neural architectures via reviewing the proposal and development of existing architectures. We first roughly divide the influence domain of intrinsic motivations on some common deep neural architectures into three categories: information processing, information transmission and learning strategy. Furthermore, to illustrate how deep neural architectures are motivated and developed, motivation and architecture details of three deep neural networks, namely convolutional neural network (CNN), recurrent neural network (RNN) and generative adversarial network (GAN), are introduced respectively. Moreover, the evolution of these neural architectures are also elaborated in this paper. At last, this review is concluded and several promising research topics about deep neural architectures in the future are discussed.

An overview on GNSS carrier-phase time transfer research

Abstract: global navigation satellite system (GNSS) carrier phase observations are two orders of higher accuracy than pseudo-range observations, and they are less affected by multipath besides. As a result, the time transfer accuracy can reach 0.1 ns, and the frequency transfer stability can reach 1×10−15 with carrier phase (CP) method, therefore CP method is considered the most accurate and promising time transfer technology. The focus of this paper is to present a comprehensive summary of CP method, with specific attention directed toward day-boundary clock jump, ambiguity resolution (AR), multi-system time transfer and real-time time transfer. Day-boundary clock jump is essentially caused by pseudo-range noise. Several approaches were proposed to solve the problem, such as continuously processing strategy, sliding batch and bidirectional filtering methods which were compared in this study. Additionally, researches on AR in CP method were introduced. Many scholars attempted to fix the single-difference ambiguities to improve the time transfer result, however, owing to the uncalibrated phase delay (UPD) was not considered, the current studies on AR in CP method were still immature. Moreover, because four GNSS systems could be used for time-transfer currently, which was helpful to increase the accuracy and reliability, the researches on multi-system time transfer were reviewed. What’s more, real-time time transfer attracted more attention nowadays, the preliminary research results were presented.

Event-Based Formation Control for Linear Multi-Agent Systems Under Switching Topology

Abstract: This paper investigates linear multi-agent systems under switching topology. A novel event-triggered consensus formation controller is proposed. Sufficient conditions are presented to ensure the boundedness of formation error. Zeno behavior can also be avoided in the event-triggered mechanism. A numerical simulation based on hexagon formation of six robots is carried out to validate the results.

Improving the initialization speed for long-range NRTK in network solution mode

Abstract: Initialization speed is one of the most important factors in network real time kinematic (NRTK) performance. Owing to the low correlation among the error sources of reference stations, it is difficult to fix reference station ambiguities of long-range NRTK quickly. In traditional reference stations ambiguity resolution (AR) methods, baselines are usually solved independently which is called baseline solution (BS) mode in this study. Because the correlations among baselines are not taken into consideration in ambiguities estimation, the AR speed is slow. Generally, tens of minutes or longer time is required to initialize. We propose a network solution (NS) mode approach, in which the correlations among the double-difference ambiguities (DDAs) as well as double-difference ionospheric delays (DDIDs) of different baselines are considered in estimating float ambiguity solutions. Experimental results show that the float ambiguity solutions obtained are more accurate with an improved consistency. Thus, initialization speed is significantly increased by 18% in NS mode.

Leader-Following Consensus of Stochastic Dynamical Multi-Agent Systems Under PI Control

Abstract: With the development of swarm intelligence in last decades, consensus problem of multi-agent systems has been attracting much attention. To reveal the inherent mechanism of leader-following consensus in multi-agent systems with stochastic dynamics, PI control protocols are designed in this paper. Owing to the stochastic analysis techniques, algebraic graph theory, and constructing appropriate Lyapunov function, it is proved that the leader-following consensus of nonlinear stochastic dynamical multi-agent systems can be reached in mean square. Sufficient condition are deduced to select the PI control protocol parameters. Finally, the theoretical results are demonstrated through a simulation example.

Hierarchical Decomposition and Prescribed Performance Bound based Adaptive Control for Leader-Follower Formation of Uncertain Nonholonomic Mobile Robots

Abstract: In this paper, the cooperative formation control problem is investigated for multiple two-wheeled mobile robots with unknown parameters. By combining the hierarchical decomposition and prescribed performance bound (PPB) technique, distributed adaptive formation controllers are designed based on dynamic surface control. It is proved in the Lyapunov sense that all the closed-loop signals are semi-globally bounded and the tracking error converges to a compact set, which can be made arbitrarily small by adjusting the design parameters appropriately. Furthermore, formation maintenance, connectivity preservation and collision avoidance can be achieved with the proposed control scheme. Simulations are also given to verify the effectiveness of the theoretical results.

Prediction of drug-related phenotypes based on the constructed phenotype-gene-drug heterogeneous network

Abstract: Understanding the relationship between disease phenotypes and drugs is an important issue in network medicine. With the continuous enrichment of phenotypic and drug data, scientists have proposed various approaches to explore biological information from these data. Among the existing methods, network-based ones show their advantages. However, most of the considered networks were centered on disease networks, and rarely considered networks that were composed of three or more biological entities. By integrating disease phenotypes, genes and drugs data that were extracted from three databases, a heterogeneous network that containing relationships among the three biological entities was constructed. Based on the constructed networks, we develop a new method called Relationship based on the Shortest Path (RSP) to identify disease phenotypes and drug relationships. In the RSP, we first calculated the global correlation between the 22 phenotype classes and the 14 drug classes, then the shortest path length between each disease phenotype and each drug in the heterogeneous network was used to improve such global correlation, and finally, we obtained the local correlation coefficients between drugs and phenotypes. Different indicators were used to evaluate the method, the RSP method outperforms the ProphNet and the Shortest Path (SP) methods in inferring potential phenotypic targets for drugs. Our investigations have potential implications in network medicine.

Signal approximation with Pascal’s triangle and sampling

Abstract: This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle, which realizes a sampled–data driven approach between a continuous–time signal and its discrete–time representation. The roles of certain parameters, such as sampling time interval or model order, and signal characteristics, i.e., its curvature, on the approximation are investigated. Approximate errors in one and multiple–step predictions are analyzed. Furthermore, time–variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.