Data-Driven Leader-Follower Output Synchronization for Networked Non-Linear Multi-Agent Systems with Switching Topology and Time-Varying Delays

This paper studies the output synchronization problem for a class of networked non-linear multi-agent systems with switching topology and time-varying delays. To synchronize the outputs, a leader is introduced whose connectivity to the followers varies with time, and a novel data-driven consensus protocol based on model free adaptive control is proposed, where the reference input of each follower is designed to be the time-varying average of the neighboring agents’ outputs. Both the case when the leader is with a prescribed reference input and the case otherwise are considered. The proposed protocol allows for time-varying delays, switching topology, and does not use the agent structure or the dynamics information implicitly or explicitly. Sufficient conditions are derived to guarantee the closed-loop stability, and conditions for consensus convergence are obtained, where only a joint spanning tree is required. Numerical simulations and practical experiments are conducted to demonstrate the effectiveness of the proposed protocol.     

Consensus algorithm for multiple quadrotor systems under fixed and switching topologies

The distributed leadless consensus problem for multiple quadrotor systems under fixed and switching topologies is investigated. The objective is to design protocols achieving consensus for networked quadrotors＇ positions and attitudes. Because the model of a quadrotor is a strong high-order nonlinear coupling system, the approach of feedback linearization is employed to transform the model into a group of four linear subsystems among which there is no coupling. Then, a consensus algorithm is proposed which consists of a local feedback controller and interactions from the finite neighbors under fixed undirected topologies. Especially, the problem of choosing the parameters in the consensus algo-rithm is also addressed, enlightened by the results of the robust control theory. Furthermore, it is proved that the proposed algo-rithm also guarantees the consensus under undirected switching topologies. Simulation results show the effectiveness of the pro- posed algorithm.     

Asymptotic and stable proper ties of general stochastic functional differential equations

The asymptotic and stable properties of general stochastic functional differential equations are investigated by the multiple Lyapunov function method, which admits non-negative up-per bounds for the stochastic derivatives of the Lyapunov functions, a theorem for asymptotic properties of the LaSal e-type described by limit sets of the solutions of the equations is obtained. Based on the asymptotic properties to the limit set, a theorem of asymptotic stability of the stochastic functional differential equations is also established, which enables us to construct the Lyapunov functions more easily in application. Particularly, the wel-known classical theorem on stochastic stability is a special case of our result, the operator LV is not required to be negative which is more general to fulfil and the stochastic perturbation plays an important role in it. These show clearly the improvement of the traditional method to find the Lyapunov functions. A numerical simulation example is given to il ustrate the usage of the method.     

Novel multi-objective optimization algorithm

Many multi-objective evolutionary algorithms （MOEAs） can converge to the Pareto optimal front and work well on two or three objectives, but they deteriorate when faced with manyobjective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values （instead of the Paretodominance relation to select candidate solutions）, have been regarded as promising schemes that yield more satisfactory results than well-known algorithms, such as non-dominated sort- ing genetic algorithm （NSGA-II） and strength Pareto evolutionary algorithm （SPEA2）. However, they can suffer from having a slow convergence speed. This paper proposes a new indicatorbased multi-objective optimization algorithm, namely, the multi- objective shuffled frog leaping algorithm based on the ε indicator （ε-MOSFLA）. This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capability of global search and quick convergence as an evolutionary strategy and a simple and effective E-indicator as a fitness assignment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show that ε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as well as the speed of convergence.     

Coordination Model Research Based on Command Decision Joint Intension

Command Decision Simulation （CDS） is not only an important and difficult issue in the field of simulation, but also a technique issue to be tackled in the course of collective Modeling and Simulation in Joint Operations Experiment Center. CDS can give support to all kinds of simulation. Using computer to realize the Blue side＇s command deci- sion automatic execution in the operation training simulation system can improve training effect. Using computer to realize the lower level command unit＇s automatic command deci- sion can help to reduce the number of controllers and release them from heavy work. It can also enlarge simulation scale and increase the freedom of the simulation entity especially the command entity, thus enhance the authenticity and credibility of the simulation. Ac- cording to the command decision structure and the Joint Intention Theory, the thesis puts forward the method of making use of Coordination Matrix （CM） to realizing cooperation among the entities in CDS and constructs the calculation sketch of Task Execution, Task Monitoring and Task Replanning. At the same time, the thesis brings forward the CDS structure vision based on Partial Global Planning（PGP）. As CM is an indispensable part of combat simulation, the theory and the method of CDS will become an important part for the theory of Combat Simulation.     

多智能体系统的有限时间跟踪控制

基于领导-跟随者模型研究了多智能体系统的有限时间跟踪控制问题。针对领导者状态是时变的情况,提出了一类非线性有限时间跟踪控制算法,在固定网络拓扑结构下,利用Lyapunov有限时间稳定性理论和矩阵理论分析,得到了该算法使得系统中跟随者状态在有限时间内与领导者状态达到一致的充分条件;在切换网络拓扑结构下,提出了一类有限时间跟踪控制算法,在领导者的状态是时不变的情况下,给出了该算法使得系统实现有限时间跟踪控制的充分条件。仿真实例验证了所提出两类算法的有效性。     

Switching variability index based multiple strategy CFAR detector

A switching variability index （SVl） constant false alarm rate （CFAR） detector is proposed for improving the detection performance of VI-CFAR detectors in multiple targets backgrounds. When the presence of non-homogeneity in CFAR reference windows is indicated by a VI-CFAR detector, a switching- CFAR detector is introduced to optimize the performance of the VI-CFAR detector in homogeneous, multiple targets and clutter edge backgrounds. The structure and parameters selection method of the SVI-CFAR detector is presented. Comparisons with classic CFAR detectors and recently proposed detectors are also given. Theoretical analysis and simulation results show that SVICFAR detector maintains the good performance of the VI-CFAR detector in homogeneous and clutter edge backgrounds, while greatly improving the capacity of anti-multi targets.     

Distributed Consensus-Based <Emphasis Type="Italic">K</Emphasis>-Means Algorithm in Switching Multi-Agent Networks

This paper discusses a distributed design for clustering based on the K-means algorithm in a switching multi-agent network, for the case when data are decentralized stored and unavailable to all agents. The authors propose a consensus-based algorithm in distributed case, that is, the double-clock consensus-based K-means algorithm (DCKA). With mild connectivity conditions, the authors show convergence of DCKA to guarantee a distributed solution to the clustering problem, even though the network topology is time-varying. Moreover, the authors provide experimental results on various clustering datasets to illustrate the effectiveness of the fully distributed algorithm DCKA, whose performance may be better than that of the centralized K-means algorithm.     

具有不同输入时延的二阶多智能体系统一致性

研究了具有不同时变输入时延的二阶连续多智能体系统的一致性问题。首先,通过变量转换,将系统的收敛性问题转化为误差系统的稳定问题;然后,通过对系统进行变换,将二阶系统稳定性问题转换为等价系统的稳定性问题。通过构造李雅普诺夫函数,基于线性矩阵不等式(LMI)的方法,给出在无向固定拓扑条件下,系统达到一致的充分条件。最后,仿真实例证明了结果的有效性。     

Consensus problem of second-order multi-agent systems with time-varying communication delay and switching topology

Leader-following stationary consensus problem is investigated for the second-order multi-agent systems with timevarying communication delay and switching topology.Based on Lyapunov-Krasovskii functional and Lyapunov-Razumikhin functions respectively,consensus criterions in the form of linear matrix inequality (LMI) are obtained for the system with time-varying communication delays under static interconnection topology converging to the leader’s states.Moreover,the delay-dependent consensus criterion in the form of LMI is also obtained for the system with time-invariant communication delay and switching topologies by constructing Lyapunov-Krasovskii functional.Numerical simulations present the correctness of the results.     

On control of strong consensus for networked agents with noisy observations

The discrete-time first-order multi-agent networks with communication noises are under consideration.Based on the noisy observations,the consensus control is given for networks with both fixed and time-varying topologies.The states of agents in the resulting closed-loop network are updated by a stochastic approximation(SA) algorithm,and the consensus analysis for networks turns to be the convergence analysis for SA.For networks with fixed topologies,the proposed consensus control leads to consensus of agents with probability one if the graph associated with the network is connected.In the case of time-varying topologies,the similar results are derived if the graph is jointly connected in a fixed time period.Compared with existing results,the networks considered here are in a more general setting under weaker assumptions and the strong consensus is established by a simpler proof.     

Distributed Nonsmooth Convex Optimization over Markovian Switching Random Networks with Two Step-Sizes

This paper investigates the distributed convex optimization problem over a multi-agent system with Markovian switching communication networks. The objective function is the sum of each agent's local nonsmooth objective function, which cannot be known by other agents. The communication network is assumed to switch over a set of weight-balanced directed graphs with a Markovian property. The authors propose a consensus sub-gradient algorithm with two time-scale step-sizes to handle the Markovian switching topologies and the absence of global gradient information. With proper selection of step-sizes, the authors prove the almost sure convergence of all agents' local estimates to the same optimal solution when the union graph of the Markovian network' states is strongly connected and the Markovian chain is irreducible. The convergence rate analysis is also given for specific cases.Simulations are given to demonstrate the results.     

Consensus control for leader-following multi-agent systems with measurement noises

<正> This work is concerned with consensus control for a class of leader-following multi-agentsystems (MASs).The information that each agent received is corrupted by measurement noises.Toreduce the impact of noises on consensus,time-varying consensus gains are adopted,based on whichconsensus protocols are designed.By using the tools of stochastic analysis and algebraic graph theory,asufficient condition is obtained for the protocol to ensure strong mean square consensus under the fixedtopologies.This condition is shown to be necessary and sufficient in the noise-free case.Furthermore,by using a common Lyapunov function,the result is extended to the switching topology case.     

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.     

CONTINUOUS AUXILIARY COVARIATE IN ADDITIVE HAZARDS REGRESSION FOR SURVIVAL DATA

This paper considers the additive hazards iliary covariate information to improve the efficiency regression analysis by utilizing continuous aux- of the statistical inference when the primary covariate is ascertained only for a randomly selected subsample. The authors construct a martingale based estimating equation for the regression parameter and establish the asymptotic consistency and normality of the resultant estimators. Simulation study shows that the proposed method can greatly improve the efficiency compared with the estimator which discards the auxiliary covariate information in a variety of settings. A real example is also provided as an illustration.     

Adaptive Event-Triggering Consensus for Multi-Agent Systems with Linear Time-Varying Dynamics

In this paper, the authors study the fully distributed event-triggering consensus problem for multi-agent systems with linear time-varying dynamics, where each agent is described by a linear time-varying system. An adaptive event-triggering protocol is proposed for time-varying multi-agent systems under directed graph. Based on the Gramian matrix of linear time-varying systems, the design of control gain is done and sufficient conditions ensuring the consensus of linear time-varying multi-agent systems are obtained. It is shown that the coupling strength is closely related to the triggering condition. When it comes to undirected graph, it is shown that the coupling strength is independent on the triggering condition and thus the design procedure is of more freedom than the directed case. In addition, it is also proved that Zeno behaviours can be excluded in the proposed protocols. A numerical example is presented to demonstrate the effectiveness of the theoretical results.

     

EXISTENCE AND ANALYTICAL APPROXIMATIONS OF LIMIT CYCLES IN A THREE-DIMENSIONAL NONLINEAR AUTONOMOUS FEEDBACK CONTROL SYSTEM

This paper is concerned with the existence and the analytical approximations of limit cycles in a three-dimensional nonlinear autonomous feedback control system. Based on three-dimensional Hopf bifurcation theorem, the existence of limit cycles is first proved. Then the homotopy analysis method （HAM） is applied to obtain the analytical approximations of the limit cycle and its frequency. In deriving the higher-order approximations, the authors utilized the idea of a perturbation procedure proposed for limit cycles＇ approximation in van der Pol equation. By comparing with the numerical integration solutions, it is shown that the accuracy of the analytical results obtained in this paper is very high, even when the amplitude of the limit cycle is large.     

Comprehensive multivariate grey incidence degree based on principal component analysis

To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis （PCA） are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.     

Immune multi-agent model using vaccine for cooperative air-defense system of systems for surface warship formation based on danger theory

Aiming at the problem on cooperative air-defense of surface warship formation, this paper maps the cooperative airdefense system of systems （SoS） for surface warship formation （CASoSSWF） to the biological immune system （BIS） according to the similarity of the defense mechanism and characteristics between the CASoSSWF and the BIS, and then designs the models of components and the architecture for a monitoring agent, a regulating agent, a killer agent, a pre-warning agent and a communicating agent by making use of the theories and methods of the artificial immune system, the multi-agent system （MAS）, the vaccine and the danger theory （DT）. Moreover a new immune multi-agent model using vaccine based on DT （IMMUVBDT） for the cooperative air-defense SoS is advanced. The immune response and immune mechanism of the CASoSSWF are analyzed. The model has a capability of memory, evolution, commendable dynamic environment adaptability and self-learning, and embodies adequately the cooperative air-defense mechanism for the CASoSSWF. Therefore it shows a novel idea for the CASoSSWF which can provide conception models for a surface warship formation operation simulation system.     

Performance evaluation from stochastic statecharts repre-sentation of flexible reactive systems：a simulation approach

This paper focuses on the performance analysis of flexible reactive systems. The performance analysis consists of two phases： first system modeling, second performance evalua-tion. The paper models the flexible reactive system by the stochas-tic statecharts method, and uses the simulation method to evalu-ate the performance. To make use of the feature of event-triggered state transitions in the statecharts, a new method of simulation is proposed based on the techniques of the discrete-event system simulation. The new method solves the problem of computer imple-mentation of stochastic events, probabilistic transition, concurrent states, paral el actions, and broadcast communication mechanism in the stochastic statecharts. An example of a flexible manufactur-ing system is presented. The simulation result of the example is consistent with the analytical result, which shows the feasibility of the proposed new simulation method.