發(fā)布時(shí)間：2018-02-25 19:17

  本文關(guān)鍵詞： 群集系統(tǒng) 分群行為 自組織 分群控制 信息耦合度 選擇性交互 隱式信息傳播 成對(duì)交互 速度估計(jì) 時(shí)延 群集機(jī)器人　出處：《西北工業(yè)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：群集系統(tǒng)的分群行為,表征為一個(gè)聚合的母群分裂成若干子群的現(xiàn)象。作為群集系統(tǒng)的固有運(yùn)動(dòng)模式,分群行為不僅彌補(bǔ)了單一組群運(yùn)動(dòng)難以對(duì)群集分裂現(xiàn)象進(jìn)行有效表征的缺陷,而且賦予了群集更多的運(yùn)動(dòng)靈活性和行為多樣性,是群集對(duì)環(huán)境適應(yīng)能力的典型表現(xiàn)。因而,開展群集系統(tǒng)分群控制的研究,對(duì)于揭示群集"分裂-融合"行為的內(nèi)在機(jī)制,深化對(duì)自組織群集運(yùn)動(dòng)機(jī)理的認(rèn)知,以及促進(jìn)分群行為的工程應(yīng)用,均具有重要的理論價(jià)值和實(shí)踐意義。本文以國(guó)家自然科學(xué)基金項(xiàng)目(No.51179156)為依托,針對(duì)一類不依賴于指派、協(xié)商等智能規(guī)劃?rùn)C(jī)制的典型自組織分群運(yùn)動(dòng)行為,在深入揭示其產(chǎn)生機(jī)理的基礎(chǔ)上,對(duì)基于信息耦合度(Information coupling degree,ICD)的自組織分群控制方法及相關(guān)問題展開研究。論文的主要工作和研究成果如下:(1)群集系統(tǒng)自組織分群運(yùn)動(dòng)機(jī)理與建模研究以生物群集的分群行為作為參考,從刺激信息在群內(nèi)定向傳播的角度出發(fā),研究了分群運(yùn)動(dòng)中群集內(nèi)部信息的傳播機(jī)制,指出分群行為是多元外部刺激信息在群內(nèi)傳播所引起的一種個(gè)體運(yùn)動(dòng)分化現(xiàn)象,并發(fā)現(xiàn)個(gè)體間關(guān)聯(lián)強(qiáng)度的差異是導(dǎo)致分群行為的根本原因�；谠撜J(rèn)識(shí),定義了對(duì)個(gè)體間關(guān)聯(lián)強(qiáng)度進(jìn)行表征的指標(biāo)—信息耦合度,并提出了一種基于信息耦合度的分群控制模型框架,為后續(xù)分群控制算法的設(shè)計(jì)奠定了基礎(chǔ)。(2)基于信息耦合度的群集系統(tǒng)分群控制方法研究借鑒生物群集個(gè)體間的交互作用關(guān)系,綜合利用個(gè)體位置和速度信息,建立了信息耦合度模型。然后,從刺激信息在群內(nèi)傳播最大化的角度出發(fā),提出了一種基于"max-ICD"的分群控制策略。進(jìn)而,將自組織"成對(duì)交互"規(guī)則融入傳統(tǒng)的"分離/組隊(duì)/聚合"群集協(xié)同規(guī)則中,設(shè)計(jì)了基于信息耦合度的分群控制算法,實(shí)現(xiàn)了外部刺激下群集系統(tǒng)的自組織分群行為。理論分析和仿真實(shí)驗(yàn)均驗(yàn)證了所提出的分群控制算法的有效性。(3)無速度測(cè)量下群集系統(tǒng)分群控制方法研究針對(duì)無速度測(cè)量下群集系統(tǒng)的自組織分群?jiǎn)栴},提出了一種僅利用位置信息的分群控制方法。首先,通過構(gòu)建分布式觀測(cè)器,實(shí)現(xiàn)了利用個(gè)體間的相對(duì)位置信息對(duì)相對(duì)速度信息的實(shí)時(shí)估計(jì)。進(jìn)而,以基于信息耦合度的分群控制理論框架為基礎(chǔ),結(jié)合速度信息未知的情形,利用個(gè)體間的相對(duì)位置信息和估計(jì)的相對(duì)速度信息,設(shè)計(jì)了無需速度測(cè)量的分群控制算法,實(shí)現(xiàn)了群集系統(tǒng)的自組織分群運(yùn)動(dòng)。理論分析和仿真實(shí)驗(yàn)均驗(yàn)證了所提出的無速度測(cè)量下自組織分群控制算法的有效性。(4)具有時(shí)延的群集系統(tǒng)分群控制方法研究針對(duì)分群運(yùn)動(dòng)中個(gè)體信息獲取存在時(shí)延的問題,首先對(duì)前面提出的基于信息耦合度的分群控制算法的時(shí)延局限性展開討論,發(fā)現(xiàn)時(shí)延的存在會(huì)顯著降低分群算法的性能。進(jìn)而,分定常時(shí)延和時(shí)變時(shí)延兩種情況,對(duì)時(shí)延下群集系統(tǒng)的分群控制方法展開研究。對(duì)于定常時(shí)延情形,采用Lyapunov-Razumikhin定理得出了群集實(shí)現(xiàn)穩(wěn)定分群運(yùn)動(dòng)的時(shí)延上界及相關(guān)收斂條件。對(duì)于時(shí)變時(shí)延情形,利用Lyaponov-Krasovskii泛函得出了線性矩陣不等式形式的時(shí)延上界及相關(guān)收斂條件。理論分析和仿真實(shí)驗(yàn)均驗(yàn)證了所提出的時(shí)延下群集系統(tǒng)分群控制算法的有效性。(5)群集機(jī)器人系統(tǒng)分群控制實(shí)驗(yàn)驗(yàn)證研究以E-puck群集機(jī)器人系統(tǒng)為實(shí)驗(yàn)平臺(tái),對(duì)基于信息耦合度的自組織分群控制方法展開實(shí)驗(yàn)驗(yàn)證工作。針對(duì)E-puck機(jī)器人的特點(diǎn),首先建立其運(yùn)動(dòng)學(xué)模型,并結(jié)合所量測(cè)的鄰居距離-方位信息,以基于信息耦合度的分群控制思想為基礎(chǔ),設(shè)計(jì)了面向群集機(jī)器人分群應(yīng)用的控制算法,最后在Webots機(jī)器人仿真平臺(tái)上開展的分群實(shí)驗(yàn)驗(yàn)證了所提算法的可行性和有效性。
[Abstract]:The clustering behavior of the cluster system, represented as a polymerization mother groups are divided into several subgroups. The phenomenon as an inherent motion mode of cluster system, cluster behavior not only overcomes the defects of single group movement difficult to carry out effective characterization of cluster splitting phenomenon, but also gives more flexibility and cluster motion behavior is diversity. The typical performance of cluster to adapt to the environment. Therefore, to carry out research on cluster clustering control system, to reveal the internal mechanism of cluster "split - Fusion" behavior, deepening of self organizing cluster motion mechanism of cognition, and the engineering application to promote clustering behavior, has important theoretical value and practical significance. Based on the National Natural Science Fund Project (No.51179156) as the basis, for a class does not depend on the typical assignment, consultation and other intelligent planning mechanism of self-organizing clustering behavior, in revealing its Based on the generation mechanism, based on information coupling (Information coupling degree, ICD) from the research methods and related issues of clustering in control. The main work and research results are as follows: (1) clustering behavior of self-organizing clustering mechanism and Modeling Research on biological cluster cluster system as the reference, from the stimulus information based on directional propagation angles within the group, the internal information communication mechanism of cluster grouping movement, pointed out that the clustering behavior is multiple external stimuli spread a differentiation phenomenon caused by the movement of the individual within the group, and found the difference correlation strength between individuals is the root cause of clustering behavior. Based on the understanding of the definition the characterization of the strength of the correlation between individual index information coupling degree, and proposes an information coupling clustering control model based on the framework for further clustering control Laid the foundation of the algorithm design. (2) cluster clustering control method based on the biological cluster interaction relationship between individual information based on coupling degree, the comprehensive utilization of position and speed information of individuals, to establish the information model of coupling degree. Then, starting from the stimulation of information dissemination in the maximum angle, put forward a based on the "max-ICD" grouping control strategy. Then, the self-organization "interactions" rules into the traditional "separation / team / polymerization" cluster coordination rules, designed the information coupling degree of clustering algorithm based on self organizing clustering behavior is realized under external stimulation of the cluster system. Theoretical analysis and simulation experiments verify the validity of the proposed control algorithm of clustering. (3) without velocity measurement under cluster self-organization groups system clustering control method for speed measurement of the cluster system is proposed. A method using only the location information of the cluster control method. Firstly, through the construction of distributed observer, the relative position between the individual information real-time estimation of the relative speed of information. Then, the information coupling clustering framework based on control theory as the basis, combined with the speed of the information is unknown, the relative position of relative velocity information estimation and information between individuals, designed without clustering control algorithm of velocity measurement, realizes the self organization clustering movement cluster system. Theoretical analysis and simulation experiments show that the speed measurement proposed since the effectiveness of clustering control algorithm. (4) with clustering control method for cluster system the individual information clustering movement access latency problems delay, firstly some delay limitation information coupling control algorithm based on cluster expansion The discussion, found delays will significantly reduce the performance of clustering algorithm. Then, divided into constant delay and time-varying delay two cases, to study the time delay of the cluster system clustering control method. For constant time, using the Lyapunov-Razumikhin theorem that the delay bound cluster realize stable cluster movement and related convergence conditions for time-varying delay, delay bound by the Lyaponov-Krasovskii functional form of linear matrix inequalities and convergence conditions are obtained. The theoretical analysis and experimental results show the effectiveness of the cluster clustering algorithm of the control system under the proposed delay. (5) the cluster grouping control robot system and experimental studies of the E-puck cluster system as the experimental robot based on the information platform, the coupling degree of self organization clustering control method experiment. According to the characteristics of E-puck robot, Firstly, the kinematic model, and combined with the measured neighbor distance azimuth information, the information coupling degree of clustering based on control theory, design oriented cluster control algorithm of robot application clustering, clustering experiment finally carried out in simulation platform of the Webots robot verified the feasibility and effectiveness of the algorithm.

【學(xué)位授予單位】：西北工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP13

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