Adaptive synchronization of coupled harmonic oscillators under switching topology

This paper investigates the adaptive synchronization for coupled harmonic oscillators with switching topology. Edge-based adaptive control protocols are proposed for both leaderless and leader-following synchronization for coupled harmonic oscillators with switching topology. Using Lyapunov stability theory, by parting the topology graph into connected components (containing at least two connected vertices) and isolated vertex components (containing a isolated vertex), full distributed adaptive synchronization conditions are obtained, which can guarantee that the synchronization conditions do not require any global information except a mild connection assumption. Finally numerical simulations are presented to illustrate the theoretical findings.     

Adaptive synchronization of complex dynamic networks with switching parameters subject to state constraints in power system

This paper deals with the synchronization control of power complex networks with switching parameters. In the meantime, the node state constraints are considered during the synchronization process. Admittedly, synchronization problem encountered in power complex networks is becoming progressively important due to the increasing connection and disconnection operations resulting from sustainable energy and controllable load. Hereon, the network model considering switching parameters of each node is established to describe the topology variation of power systems that may be confronted in practical terms. Then, by utilizing the adaptive backstepping technique with a barrier Lyapunov function (BLF), a novel synchronization controller is constructed recursively which accomplishes the nodes full states tracking within the predefined transient behavior. Owing to the characteristic of BLF, the designed controller as well as its adaptive law could guarantee both the constrained state of each node restricted by a prescribed range and the synchronization performance. Meanwhile, the bounded output of the system could track the desired trajectory. Finally, scenario simulations are performed to demonstrate the effectiveness and superiority of the proposed method.     

协同创新网络演化模型及仿真研究——基于类DNA翻译过程

在综合协同创新网络基本属性和独有特质的基础上,通过刻画外界刺激转变过程和网络异质主体行为,构建出以异质性主体为网络要素、以刺激-反应机制为演化机理的协同创新网络类DNA翻译过程模型。运用系统仿真方法,分析了从协同创新网络的拓扑结构变动规律,知识元素和网络拓扑结构的耦合演化关系,网络异质主体的角色作用及主体开放程度与知识流动效率的关系等多个角度渐次深入地分析了协同创新网络演化过程中网络结构变动特点及其与知识元素、主体要素间深层次地交互作用关系。结果表明：协同创新网络发展过程中会多次涌现小世界特性,但程度、原因不尽相同;技术知识和网络结构存在协同演化现象;强势企业始终是协同创新网络的主导;主体开放程度对技术知识流动效率提升幅度有很大影响。强化企业在技术创新中的主体地位,并增强异质性主体间的开放程度,是提高协同创新绩效的有力保障。     

Adaptive synchronization of complex networks with general distributed update laws for coupling weights

This paper discusses adaptive synchronization control for complex networks interacted in an undirected weighted graph, and aims to provide a novel and general approach for the design of distributed update laws for adaptively adjusting coupling weights. The proposed updating laws are very general in the sense that they encompass most weight update laws reported in the literature as special cases, and also provide new insights in the analysis of network system evolution and graph weight convergence. We show a rigorous proof for the synchronization stability of the overall complex network to a synchronized state, and demonstrate the convergence of adaptive weights for each edge to some bounded constants. A detailed comparison with available results is provided to elaborate the new features and advantages of the proposed adaptive strategies as compared with conventional adaptive laws. The effectiveness of the proposed approach is also validated by several typical simulations.     

Distributed cooperative learning for a group of uncertain systems via output feedback and neural networks

This paper studies the problem of adaptive neural network (NN) output-feedback control for a group of uncertain nonlinear multi-agent systems (MASs) from the viewpoint of cooperative learning. It is assumed that all MASs have identical unknown nonlinear dynamic models but carry out different periodic control tasks, i.e., each agent system has its own periodic reference trajectory. By establishing a network topology among systems, we propose a new consensus-based distributed cooperative learning (DCL) law for the unknown weights of radial basis function (RBF) neural networks appearing in output-feedback control laws. The main advantage of such a learning scheme is that all estimated weights converge to a small neighborhood of the optimal value over the union of all system estimated state orbits. Thus, the learned NN weights have better generalization ability than those obtained by traditional NN learning laws. Our control approach also guarantees the convergence of tracking errors and the stability of closed-loop system. Under the assumption that the network topology is undirected and connected, we give a strict proof by verifying the cooperative persisting excitation condition of RBF regression vectors. This condition is defined in our recent work and plays a key role in analyzing the convergence of adaptive parameters. Finally, two simulation examples are provided to verify the effectiveness and advantages of the control scheme proposed in this paper.     

Matrix-weighted consensus for multi-agent systems with sampled-data

This paper researches the consensus issue for multi-agent systems on matrix-weighted directed fixed and undirected switching network topologies by sampled data control method which saves resources and is more practical. Using the sampled information, the distributed control laws are designed under two network topologies, respectively. Under directed fixed network topology, the consensus conditions based on the sampling period and the eigenvalues of Laplacian matrix are deduced by matrix theory and analysis theory. Under undirected switching network topology, by using Lyapunov stability theory, the consensus conditions based on the sampling period and switched network topologies are built. Lastly, two simulation examples are offered to verify the validity of the obtained results.     

Stabilization of a class of nonlinear parametrized systems by a flexible switching adaptive backstepping

In this paper, we consider global adaptive feedback control of nonlinear systems with unknown parameters entering nonlinearly. Such unknown parameters are also not required to lie in a known compact set. Unlike previous results, our proposed adaptive controller is a new double dynamical switching-type controller in which the controller parameter is tuned in a flexible switching manner via a monotonically decreasing switching logic and the controller combines the traditional adaptive theorem with the switching scheme perfectly. Global stability results of the closed-loop system have been proved.     

Synchronization within synchronization: transients and intermittency in ecological networks

Transients are fundamental to ecological systems with significant implications to management, conservation and biological control. We uncover a type of transient synchronization behavior in spatial ecological networks whose local dynamics are of the chaotic, predator–prey type. In the parameter regime where there is phase synchronization among all the patches, complete synchronization (i.e. synchronization in both phase and amplitude) can arise in certain pairs of patches as determined by the network symmetry—henceforth the phenomenon of  ‘synchronization within synchronization.’ Distinct patterns of complete synchronization coexist but, due to intrinsic instability or noise, each pattern is a transient and there is random, intermittent switching among the patterns in the course of time evolution. The probability distribution of the transient time is found to follow an algebraic scaling law with a divergent average transient lifetime. Based on symmetry considerations, we develop a stability analysis to understand these phenomena. The general principle of symmetry can also be exploited to explain previously discovered, counterintuitive synchronization behaviors in ecological networks.     

Leaderless consensus control of nonlinear multi-agent systems under directed topologies subject to input saturation using adaptive event-triggered mechanism

This paper deals with the leaderless consensus controller design for nonlinear multi-agent systems (MASs) subject to the input saturation nonlinearity by using an event-triggered (ET) mechanism. An adaptive ET scheme has been established with variable threshold parameter for attaining an efficient control bandwidth. Linear parameter varying (LPV) formulation and region of stability investigation for dealing with the inherent nonlinearity and input saturation, respectively, are focused in the study. A consensus controller design condition has been formulated to ensure the regional stability, to determine the consensus protocol gains, to choose the parameters of ET mechanism, and to select an appropriate adaptation law for ET control. Elimination of Zeno behavior, based on nonlinearity bounds, for the adaptive ET mechanism has been ensured through a rigorous analysis. In contrast to excising methods, a directed communication topology, adaptive ET mechanism, and removal of Zeno behavior as well as elimination of the windup effect of saturation have been considered in our work. A simulation study has been provided for six robotic agents and comparison results with the existing method are revealed.     

Sliding mode output-feedback control of discrete-time Markov jump systems using singular system method

The issue of adaptive sliding mode controller design via output knowledge is studied for discrete-time Markov jump systems in this paper by means of using singular system scheme. To force the system state onto the sliding motion, an appropriate switching surface depended on the system output is established. Meanwhile, the reachability of the sliding manifold is guaranteed by synthesizing the robust sliding mode controller and adaptive sliding mode controller for the accessible and inaccessible upper bounds of sliding patch, respectively. By using Lyapunov functional technique, sufficient criteria to guarantee the sliding motion to be stochastically admissible are proposed. Then the reachability conditions of the predesigned switching surface are developed. Finally, simulation results are provided to illustrate the effectiveness of the proposed approach.     

Finite-time multi-switching synchronization behavior for multiple chaotic systems with network transmission mode

By considering network transmission mode, this paper addresses the finite-time multi-switching synchronization problem for two kinds of multiple chaotic systems. For multiple same-order chaotic systems, we construct the general switching rules and analyze the existence of switching cases. The presented schemes guarantee the states of each derive system to be finite-timely synchronized with the desired states of every respond system in the different transmission paths and switching sequences. For multiple different order chaotic systems, we analyze a special multi-switching hybrid synchronization behavior, where part of the states are completely synchronized and the others belong to combination synchronization. Moveover, the easily verifiable criterion is derived for such synchronization. Finally, numerical examples are given to show the effectiveness of the presented theoretical results.     

Model reference adaptive control for switched linear systems using switched multiple models control strategy

In this paper, the multiple model strategy is applied to the adaptive control of switched linear systems to improve the transient performance. The solvability of the adaptive stabilization problem of each subsystem is not required. Firstly, the two-layer switching mechanism is designed. The state-dependent switching law with dwell time constraint is designed in the outer-layer switching to guarantee the stability of the switched systems. During the interval of dwell time constraint, the parameter resetting adaptive laws are designed in the inner-layer switching to improve the transient performance. Secondly, the minimum dwell time constraint providing enough time for multiple model adaptive control strategy to work fully and maintaining the stability of the switched systems is found. Finally, the proposed switched multiple model adaptive control strategy guarantees that all the closed-loop system signals remain bounded and the state tracking error converges to zero.     

Neural network-based sensor fault estimation and active fault-tolerant control for uncertain nonlinear systems

In this work, we developed a novel active fault-tolerant control (FTC) design scheme for a class of nonlinear dynamic systems subjected simultaneously to modelling imperfections, parametric uncertainties and sensor faults. Modelling imperfections and parametric uncertainties are dealt with using an adaptive radial basis function neural network (RBFNN) that estimates the uncertain part of the system dynamics. For sensor fault estimation (FE), a nonlinear observer based on the estimated dynamics is designed. A scheme to estimate sensor faults in real-time using the nonlinear observer and an additional RBFNN is developed. The convergence properties of the RBFNN, used in the fault FE part, are improved by using a sliding surface function. For FTC design, a sliding surface is designed that incorporates the real-time sensor FE. The resulting sliding mode control (SMC) technique-based FTC law uses the estimated dynamics and real-time sensor FE. A double power-reaching law is adopted to design the switching part of the control law to improve the convergence and mitigate the chattering associated with the SMC. The FTC works well in the presence and absence of sensor faults without the requirement for controller reconfiguration. The stability of the proposed active FTC law is proved using the Lyapunov method. The developed scheme is implemented on a nonlinear simulation of an unmanned aerial vehicle (UAV). The results show good performance of the proposed unified FE and the FTC framework.     

Iterative learning control-based tracking synchronization for linearly coupled reaction-diffusion neural networks with time delay and iteration-varying switching topology

In this paper, the D-type iterative learning control (ILC) protocol based on the local neighbor information is designed to achieve tracking synchronization for linearly coupled reaction-diffusion neural networks in presence of time delay and iteration-varying switching topology under a repetitive environment. Firstly, based on non-collocated sensors and actuators network, the proposed D-type ILC update law can realize tracking synchronization by utilizing output tracking errors. Then, by virtue of the contraction mapping principle, the sufficient convergence conditions of tracking synchronization errors are presented under the fixed commutation topology. Subsequently, the synchronization conclusions are extended to the iteration-varying commutation topology scenario. Finally, two numerical examples are provided to verify the efficacy of the obtained results.     

Entry, exit and knowledge: evidence from a cluster in the info-communications industry

The process by which knowledge is created, accumulated and eventually destroyed appears crucial to many industrial dynamics patterns, since it shapes the profile of evolution of industries by favouring the entry of new companies, the co-existence of incumbents and new entrants and, eventually, their selective or joint exit over time. Though problematic, and all too often neglected, the connection between two nodes of interest, Industrial Dynamics on the one hand, and Knowledge Dynamics on the other hand, thus appears as a promising field of research. On the basis of a case study in the info-communications industry, we start by emphasizing that this field of research has direct importance at the empirical level. Knowledge dynamics can create specific models of evolution among firms at the local level, such as non-shakeout patterns within the cluster, which significantly differ from more global patterns of evolution in the info-communications industry, now generally oriented towards trends of decline and bust. We further argue in favour of the development of Knowledge-Based Industrial Dynamics, an approach that lies at the interface of industry and knowledge dynamics, and which can explain how a cluster may decrease the barriers to knowledge of clustered companies and, further, create a specific knowledge dynamics that is able to shape the industrial dynamics. Finally, we document how this process of knowledge dynamics was collectively implemented in our case study on the info-communications cluster and decompose the mechanisms that led to a local non-shakeout pattern of industrial dynamics. We conclude with some remarks on the policy implications.     

河湖水系连通演变过程及驱动因子分析

河湖水系连通是古今中外行之有效的治水举措,也是适应我国新时期水利发展需要的治水新方略。河湖水系连通是以水循环物理过程为基础,伴随化学、生物和人文过程的复杂过程,有其自身的演变规律。本文从生产方式的变革和人水相互关系出发,探讨了河湖水系连通的历史演变过程,划分出四个发展阶段,分析了不同发展阶段的河湖水系连通演变特征,辨识出在自然营力和人类活动影响下河湖水系连通的关键驱动因子,分析了不同驱动因子对河湖水系连通演变的作用机制。     

基于局域核心择优模型的B2B电商网络仿真研究

通过运用复杂网络对B2B电商网络进行建模仿真,发现B2B网络的运行规律,提出减少平台风险的措施。首先分析B2B电商网络的特征和演化动因,提出网络演化的动态规则、局域择优连接规则和自演化规则。接着基于演化规则,在BA模型的基础上,构建更契合于B2B电商网络的局域世界核心择优连接(Local world Core Preferred connection)演化模型。最后,采用NetLogo软件对LCP演化模型进行仿真实验。结果验证B2B电商网络具有无标度性,发现网络具有小世界性;以及网络对随机干扰具有较强的鲁棒性。     

网络联结、资源获取与组织学习互动影响战略绩效路径研究——基于长城汽车的纵向案例研究#br#

在资源有限条件下,网络联结的构建,能使企业从联结关系中获取资源,从而促进战略绩效提升,加强企业的竞争能力。但已有研究未能从相关理论角度,挖掘网络联结、资源获取与提升企业战略绩效路径以及内在机理。以资源基础观与组织学习理论视角,采用案例研究法,以长城汽车公司为研究对象,讨论“不同网络联结阶段的结构特征、同资源获取方式与组织学习形式之间的互动关系,提升企业战略绩效的路径与机理”。通过企业网络联结成长阶段和网络联结成熟阶段的对比分析,构建出企业不同网络联结阶段的“网络联结、资源获取与组织学习”的互动关系对企业战略绩效作用的理论框架。研究结论揭示了网络联结影响企业战略绩效的深层次原因,凸显了资源获取和组织学习与之共演化的中介作用,进而深入了解企业网络联结的异变机理与作用发挥效应,为企业整合网络联结、获取资源优势、加强组织学习、提高企业战略绩效提供指导。     

Event-triggered H∞ filtering control for a class of distributed parameter systems with Markovian switching topology

The H_∞ filtering problem for distributed parameter systems with stochastic switching topology is investigated in this paper based on event-triggered control scheme. The switching topology which subjects to a Markovian chain is considered in filter design because of the communication uncertainty of practical networks. An event-triggered mechanism as a sampling scheme is developed aiming at the benefit of reducing the computation load or saving the limited network resources. Based on some novel integral inequalities, the improved delayed method is proposed for the H_∞ filtering control problem with event-triggered scheme. Moreover, by employing stochastic stability theory, filters with Markovian jump parameters are designed to guarantee that the stochastically mean square stability and H_∞ performance of the underlying error system. Finally, in order to illustrate the applicability of the obtained results, numerical examples are presented.     

Fixed-time leader-following consensus of multiple uncertain nonholonomic systems: An adaptive distributed observer approach

This paper discusses the fixed-time leader-following consensus problem for multiple uncertain nonholonomic systems, which are widely used in engineering models. According to our literature review, either the system is assumed to be known, or the uncertainty only contains state information, which does not meet the actual requirements. For this reason, this paper investigates more general nonholonomic systems with uncertainties driven by inputs and states. First, a fixed-time adaptive distributed observer is proposed to estimate the leader’s state and structural parameters, which ensures that the estimation errors converge to zero within a fixed time. Second, two regulator equations based on the idea of cooperative output regulation are constructed, and a novel observer-based distributed switching control law is proposed. This control law overcomes the nonholonomic constraints and appropriately relaxes the assumptions of uncertain functions in the existing references. Finally, the simulation results verify the effectiveness of the proposed control scheme.