智能群体系统集群行为的动力学建模与分析及其仿真研究
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摘要
集群是一种普遍存在的自然现象，是对现实世界中的群居性生物群体和人工群体所呈现出的有序集群行为的一种高度抽象。群由数目庞大的相互作用的个体组成，其本质特征是其个体具有较少智能或没有智能，但由于个体之间的交互作用而自底向上涌现出集群智能，体现了生物进化及智能体间分布式协调合作机制等的内在原因。智能群体系统本质上是一种复杂适应系统。
集群智能控制研究的是智能群体系统的运动行为的软控制。而由多个智能个体耦合而成的多智能体系统经由相互的分布式协调合作机制可实现对复杂系统的控制。多智能体系统的控制问题与智能群体系统的集群运动控制问题之间有着天然的内在联系。多智能体分布式协调合作控制问题的目标是使多智能体达到一致，这与集群智能控制系统的研究目标相符合。近年来，一致性问题的研究重点在于研究一致性机制在具体的智能群体工程应用中的性质，如同步、编队、集群、蜂拥、聚集等相关的一致性问题。
本文针对智能群体系统运动行为的软控制，通过建立反映群居性生物群体集群行为的涌现机制与一致性问题之间的映射关系为纽带，将从前者中抽取的群体运动规律作为智能群体系统的动力学建模和协调行为的软控制工具，然后结合一致性机制应用中由迁移、蜂拥行为延伸出的避碰、避障、编队、目标跟踪、覆盖搜索等主要环节的需求，进行软控制策略设计及其仿真研究。围绕这些内容所进行的主要工作如下：
（1）借鉴群居性生物的感知能力和交互能力，建立了集群系统运动行为分析的基本框架，给出了集群行为动态特性与一致性问题之间的映射关系，从而揭示了智能群体系统的协调控制与一致性应用机制之间的内在联系。
（2）生物群体的集群行为中蕴涵着潜在的内部运行机理。为便于群体系统运动规律的分析，文中采用空间法建模中的拉格朗日框架和欧拉框架，分别构建了三种不同类型的智能群体动力学模型。同时，针对不同的感知环境，就有限感知、全局感知、各向同性、各向异性、随机干扰、有无时延等情形，分别对这三类模型进行了细化和描述。并在对有限感知和指数型随机两类拉格朗日模型处于不同情形下的控制律进行分析的基础上，就其集群行为进行了仿真测试，验证了这两类模型的正确性和有效性。
（3）从控制系统稳定性的分析着手，从理论上证明了有限感知、指数型随机和互惠扩散三类集群系统模型的聚集性与稳定性。并针对智能群体系统协调行为的软控制中涉及到的避障、编队、抑噪、目标跟踪、覆盖搜索等一致性应用问题，就有限感知、指数型随机这两类集群系统模型分别进行了检验其软控制性能的仿真测试。仿真结果表明，这两类智能群体系统模型均具有良好的稳定性。
（4）针对面向盐湖采卤及盐田监测的仿生机器鱼系统实际应用中涉及到的协调合作作业问题，构建了机器鱼群体的控制体系结构的基本框架，并就系统的控制方案设计、研究目标、技术路线等问题进行了讨论，论证了所建立的航迹导航策略与协调作业机制的可行性。
Swarming is the ubiquitous phenomenon in nature. This phenomenon is thehighly abstracted of the orderly collective behavior, which is emerging from socialbiological population and man-made population in the real word. Flock is made up bythe interaction individuals with enormous number. The intrinsic character of flock isthat the intelligent individual has little or no intelligence at all. This kind ofintelligence it does is spontaneously emerging from bottom to top via the interactionsamong individuals. It shows the internal reason of biologic evolution, the mechanismof distributed coordination and cooperation among intelligent agents, and so on.Intelligent swarm system is essentially a kind of complex adaptive system.
Collective intelligent control study the motion behavior’s soft controlling ofintelligent swarm system. Meantime, the multi-agent system with a lot of intelligentagents via the mechanism of distributional coordination and cooperation can realizethe control on complicated system. The control problem of multi-agent system linkswith the swarming motion control problem of intelligent swarm systems in natureinherent. The objective of the control problem of multi-agent’s distributionalcoordination and cooperation is multi-agent coincident with the consensus, which iswell coincide with the research objective of collective intelligent control system. Inrecent years, the studying emphasis in consensus problem is studying the concreteproperties of the consensus mechanism’s intelligent swarm engineering applications.Some relative problems are analyzed here respectively, such as synchronization,formation problem, swarming and flocking, rendezvous problem, and so on.
Aiming at the motion behavior’s soft controlling of intelligent swarm systems, themapping relation is built up to reflect the relations between the emergency mechanismof social biological population’s collective behavior and consensus problem in thispaper. The group’s motion law is be drawn from the former by taking the mappingrelations as bond, and use it as a tool to be used in dynamic modeling and soft controlon coordination behavior of intelligent swarm systems. And then unifies the mainlinks’ require of the consensus mechanism’s applications, the soft control strategydesign and simulation are studied. In which, the main links refer to such motionbehavior as collision-avoidance, obstacle-avoidance, formation configuration,trajectory tracking, coverage search, and so on. The main links are extended frommigration or flocking behavior. Being focused on this topic, the main work in thispaper is as following:
(1) Drawing lessons from the perception and interaction capabilities of socialbiological, the basic frame of the motion behavior analysis of swarming system isestablished, the mapping relation is given to reflect the relations between the dynamiccharacteristic of collective behavior and consensus problem. It is revealed that there is an internal relation between coordination control of intelligent swarm systems andconsensus application mechanism.
(2) The collective behavior of biological population which implicates thepotential interior operational principle. For the convenience of the analysis on motionlaw of swarm systems, three kinds of the dynamic models of different types onintelligent swarm systems are constructed respectively based on the two differentframeworks which are Lagrangian framework and Eulerian framework, and which aresubordinate to the spatial approach is adopted while modeling in this paper.Meanwhile, the detailed analysis and description of these three kinds of models hasbeen done respectively which is aiming at the difference of the perceptionenvironment’s condition and considering under different situation such as finiteperception, global perception, isotropy, anisotropy, stochastic disturbance, withtime-delay, without time-delay, and so on. And then the collective behavior issimulated and tested with the two kinds of Lagrangian models which are the rangelimit perceived swarms model and exponential type stochastic swarms model basedon the analyses of the control rules for the two kinds of Lagrangian models underdifferent situation in this paper. The correctness and validity of the two kinds ofLagrangian models has been verified by simulation.
(3) Theoretically, it has been proved that the aggregation and stability on the threekinds of swarm systems models which are the range limit perceived swarms modeland the exponential type stochastic swarms model and the cooperative diffusionswarms model from control system stability analysis, and in addition to this, thesimulation and testing on the soft control performance of the two kinds of swarmsmodels which are the range limit perceived swarms model and exponential typestochastic swarms model is done which is aiming at the consensus applicationsproblems during the soft control on coordination behavior of intelligent swarmsystems such as obstacle-avoidance, formation configuration, noise suppression,trajectory tracking, coverage search, and so on. The simulation result confirms the twokinds of intelligent swarm systems models which all posses better stability.
(4) Aiming at the particular requirement of coordination and cooperationproblems on the brine extraction and monitoring process with multiple biomimeticrobotic fishes system as well as some basic problems in salt field, the basic frameworkof the control architecture of multiple bionic robot fishes system is constructed. Someproblems bearing relationship with it have been discussed, such as control schemedesign, research objective, technology route, and so on. The feasibility demonstrationis done yet simultaneously on the track navigation strategy and coordinative operationmechanisms which have been established in this paper.
集群智能控制研究的是智能群体系统的运动行为的软控制。而由多个智能个体耦合而成的多智能体系统经由相互的分布式协调合作机制可实现对复杂系统的控制。多智能体系统的控制问题与智能群体系统的集群运动控制问题之间有着天然的内在联系。多智能体分布式协调合作控制问题的目标是使多智能体达到一致，这与集群智能控制系统的研究目标相符合。近年来，一致性问题的研究重点在于研究一致性机制在具体的智能群体工程应用中的性质，如同步、编队、集群、蜂拥、聚集等相关的一致性问题。
本文针对智能群体系统运动行为的软控制，通过建立反映群居性生物群体集群行为的涌现机制与一致性问题之间的映射关系为纽带，将从前者中抽取的群体运动规律作为智能群体系统的动力学建模和协调行为的软控制工具，然后结合一致性机制应用中由迁移、蜂拥行为延伸出的避碰、避障、编队、目标跟踪、覆盖搜索等主要环节的需求，进行软控制策略设计及其仿真研究。围绕这些内容所进行的主要工作如下：
（1）借鉴群居性生物的感知能力和交互能力，建立了集群系统运动行为分析的基本框架，给出了集群行为动态特性与一致性问题之间的映射关系，从而揭示了智能群体系统的协调控制与一致性应用机制之间的内在联系。
（2）生物群体的集群行为中蕴涵着潜在的内部运行机理。为便于群体系统运动规律的分析，文中采用空间法建模中的拉格朗日框架和欧拉框架，分别构建了三种不同类型的智能群体动力学模型。同时，针对不同的感知环境，就有限感知、全局感知、各向同性、各向异性、随机干扰、有无时延等情形，分别对这三类模型进行了细化和描述。并在对有限感知和指数型随机两类拉格朗日模型处于不同情形下的控制律进行分析的基础上，就其集群行为进行了仿真测试，验证了这两类模型的正确性和有效性。
（3）从控制系统稳定性的分析着手，从理论上证明了有限感知、指数型随机和互惠扩散三类集群系统模型的聚集性与稳定性。并针对智能群体系统协调行为的软控制中涉及到的避障、编队、抑噪、目标跟踪、覆盖搜索等一致性应用问题，就有限感知、指数型随机这两类集群系统模型分别进行了检验其软控制性能的仿真测试。仿真结果表明，这两类智能群体系统模型均具有良好的稳定性。
（4）针对面向盐湖采卤及盐田监测的仿生机器鱼系统实际应用中涉及到的协调合作作业问题，构建了机器鱼群体的控制体系结构的基本框架，并就系统的控制方案设计、研究目标、技术路线等问题进行了讨论，论证了所建立的航迹导航策略与协调作业机制的可行性。
Swarming is the ubiquitous phenomenon in nature. This phenomenon is thehighly abstracted of the orderly collective behavior, which is emerging from socialbiological population and man-made population in the real word. Flock is made up bythe interaction individuals with enormous number. The intrinsic character of flock isthat the intelligent individual has little or no intelligence at all. This kind ofintelligence it does is spontaneously emerging from bottom to top via the interactionsamong individuals. It shows the internal reason of biologic evolution, the mechanismof distributed coordination and cooperation among intelligent agents, and so on.Intelligent swarm system is essentially a kind of complex adaptive system.
Collective intelligent control study the motion behavior’s soft controlling ofintelligent swarm system. Meantime, the multi-agent system with a lot of intelligentagents via the mechanism of distributional coordination and cooperation can realizethe control on complicated system. The control problem of multi-agent system linkswith the swarming motion control problem of intelligent swarm systems in natureinherent. The objective of the control problem of multi-agent’s distributionalcoordination and cooperation is multi-agent coincident with the consensus, which iswell coincide with the research objective of collective intelligent control system. Inrecent years, the studying emphasis in consensus problem is studying the concreteproperties of the consensus mechanism’s intelligent swarm engineering applications.Some relative problems are analyzed here respectively, such as synchronization,formation problem, swarming and flocking, rendezvous problem, and so on.
Aiming at the motion behavior’s soft controlling of intelligent swarm systems, themapping relation is built up to reflect the relations between the emergency mechanismof social biological population’s collective behavior and consensus problem in thispaper. The group’s motion law is be drawn from the former by taking the mappingrelations as bond, and use it as a tool to be used in dynamic modeling and soft controlon coordination behavior of intelligent swarm systems. And then unifies the mainlinks’ require of the consensus mechanism’s applications, the soft control strategydesign and simulation are studied. In which, the main links refer to such motionbehavior as collision-avoidance, obstacle-avoidance, formation configuration,trajectory tracking, coverage search, and so on. The main links are extended frommigration or flocking behavior. Being focused on this topic, the main work in thispaper is as following:
(1) Drawing lessons from the perception and interaction capabilities of socialbiological, the basic frame of the motion behavior analysis of swarming system isestablished, the mapping relation is given to reflect the relations between the dynamiccharacteristic of collective behavior and consensus problem. It is revealed that there is an internal relation between coordination control of intelligent swarm systems andconsensus application mechanism.
(2) The collective behavior of biological population which implicates thepotential interior operational principle. For the convenience of the analysis on motionlaw of swarm systems, three kinds of the dynamic models of different types onintelligent swarm systems are constructed respectively based on the two differentframeworks which are Lagrangian framework and Eulerian framework, and which aresubordinate to the spatial approach is adopted while modeling in this paper.Meanwhile, the detailed analysis and description of these three kinds of models hasbeen done respectively which is aiming at the difference of the perceptionenvironment’s condition and considering under different situation such as finiteperception, global perception, isotropy, anisotropy, stochastic disturbance, withtime-delay, without time-delay, and so on. And then the collective behavior issimulated and tested with the two kinds of Lagrangian models which are the rangelimit perceived swarms model and exponential type stochastic swarms model basedon the analyses of the control rules for the two kinds of Lagrangian models underdifferent situation in this paper. The correctness and validity of the two kinds ofLagrangian models has been verified by simulation.
(3) Theoretically, it has been proved that the aggregation and stability on the threekinds of swarm systems models which are the range limit perceived swarms modeland the exponential type stochastic swarms model and the cooperative diffusionswarms model from control system stability analysis, and in addition to this, thesimulation and testing on the soft control performance of the two kinds of swarmsmodels which are the range limit perceived swarms model and exponential typestochastic swarms model is done which is aiming at the consensus applicationsproblems during the soft control on coordination behavior of intelligent swarmsystems such as obstacle-avoidance, formation configuration, noise suppression,trajectory tracking, coverage search, and so on. The simulation result confirms the twokinds of intelligent swarm systems models which all posses better stability.
(4) Aiming at the particular requirement of coordination and cooperationproblems on the brine extraction and monitoring process with multiple biomimeticrobotic fishes system as well as some basic problems in salt field, the basic frameworkof the control architecture of multiple bionic robot fishes system is constructed. Someproblems bearing relationship with it have been discussed, such as control schemedesign, research objective, technology route, and so on. The feasibility demonstrationis done yet simultaneously on the track navigation strategy and coordinative operationmechanisms which have been established in this paper.
引文
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