改进的粒子群优化控制算法及其仿真研究_英文_谭显坤.pdf
外文翻译--改进的粒子群优化控制算法及其仿真研究【中文4900字】【中英文文献译文】
收藏
资源目录
压缩包内文档预览：
编号：30435128
类型：共享资源
大小：404.15KB
格式：ZIP
上传时间：2019-12-12
上传人：好资料QQ****51605
认证信息
个人认证
孙**（实名认证）
江苏
IP属地：江苏
12
积分
- 关 键 词：
-
中文4900字
中英文文献译文
外文
翻译
改进
粒子
优化
控制
算法
及其
仿真
研究
中文
4900
中英文
文献
译文
- 资源描述：
-
外文翻译--改进的粒子群优化控制算法及其仿真研究【中文4900字】【中英文文献译文】,中文4900字,中英文文献译文,外文,翻译,改进,粒子,优化,控制,算法,及其,仿真,研究,中文,4900,中英文,文献,译文
- 内容简介：
-
本科毕业设计论文外文文献翻译 专业名称 自动化 学生姓名 胡晋 指导教师 王佩 毕业时间 2014.06 改进的粒子群优化控制算法及其仿真研究 谭显坤中国重庆交通大学 应用技术学院,重庆,400074 摘要：控制系统的性能是由控制器的控制参数确定的。粒子群优化控制算法中存在的参数选择难题，如基本PSO算法易于陷入早熟收敛现象引起的局部最优解，导致不可能收敛于全局最优解，搜索精度不高以及收敛速度慢。针对以上问题，提出了一种改进的粒子群优化控制算法。讨论了具有遗传思想的粒子群优化算法，研究了改进的PSO控制算法，借助仿真实验对所设计的控制算法作了比较研究，仿真试验结果的响应曲线显示，其动静态特性优于传统方法的响应特性，验证了所提出改进控制算法的合理性与可行性。研究结果表明，所提出的改进PSO控制算法对控制器参数整定更加有效。关键词：粒子群优化算法；遗传思想；参数整定；改进的PSO控制算法1 简介粒子群算法和遗传算法都是进化算法1，进化算法是以生物进化和遗传等理论为基础来解决优化问题的，而且每一种进化算法都有其特点。粒子群算法有很多优势，比如，收敛率快，调整参数少，简单且易实现，编码比其他算法少等等。因此它应用范围十分广泛，例如目标函数的优化，生物系统建模，神经网络训练，噪音系统的控制等。在机械控制领域，为了提高速度，精确度和可靠性，先进的智能控制技术需获得广泛且普遍的适应性，而粒子群算法总是用于智能控制的控制器的结构参数调整2。遗传算法是一种寻找方法，这种方法是基于生物进化原则，并且在整体最优和随机优化策略更有优势3-4。到目前为止，遗传算法已经是一个成熟的分析方法，并且它广泛应用于许多领域例如优化组合，信号处理，机器改进，人造生命和自适应控制等等。粒子群算法和遗传算法有许多不同5，可举例说明，首先，粒子群算法有更好地记忆特性，以及保留具有优势解决方法的粒子的特点。遗传算法只是利用现有一代通过交叉和突变产生的新的个体,延续下一代。其次，对于PSO算法，它的原则很简单，控制参数少，减少了电脑负载。第三，PSO的进化方程,和它的位置,以及质点速度是量化并且模式化的，更重要的是它的可操作性比较强。PSO和GA都可以通过仿真和生物世界法则实验描述，并且具有一定的随机寻找技术，拥有整体最优化特性和隐式并行性。与此同时，而对复杂的控制目标，它也显示了早熟收敛和低收敛的不利一面。由于这个原因，本文探索了一种粒子群算法的改进算法，这一改进算法是基于遗传思想，文中所提及的都通过了仿真实验的验证。2 基于遗传思想的粒子群算法 2.1 遗传算法的调查遗传算法6最基本的思想是依靠编码技术，使其对染色体（一串二进制数字）起作用，模仿由染色体组成的群体的进化过程，并且遗传算法是一种随机搜索的算法。遗传算法形成了一个新的字符串群体，通过选择，交叉和变异任意的组织染色体，以解决并改革染色体，遗传算法选择更健康的基因去完成基因的繁殖。下面简要介绍算法的基本概念。1） 基因编码，由基因串决定的生物形状。在遗传算法中，通过二进制系统对每一个优化目标进行解码形成一个基因编码串，即就是个体，也称为染色体。2） 群体，群体是一套个体，即是用于解决问题的一套方法7。3） 交叉，在自然界中的繁殖时完全通过染色体交叉和基因算法的操作实现的。在交叉算子程序中，存在随机信息交换，和制造新基因和新个体的目标。4） 突变，变异操作通常需要执行变异概率不是对应点的突变，即一些基因的染色体的变化从1到0或从0到1，从而得到新的个体。变异操作可以促进后代个体的多样性，并且扩大了解决空间。变异操作是由基因算法本身固有的寻找能力来决定，并且和交叉操作仪器行动完成全部和本地区的优化解决寻找。5） 适应，存活下的生物体取决于对生物的适应程度。适应性反映了适应者生存的自然进化规律和自然选择。在最优化的问题中，每一个解决方案的相应方程值显示了解决问题的趋势是好还是坏。这一进化意义被称作适应性运算。6） 选择，也被称为繁殖或复制操作。高度适应的个体将提供一个确信的装置以便准备进行交叉和突变操作。一般会在高适应性和高性能个体根源上选择杰出的染色体，因此杰出的染色体可以广泛分布在大范围的下一代群体上，从而可以快速解决问题。这三种不同操作，交叉，突变和变异,其中交叉和突变操作被用于产生新个体以便完成复制操作,选择操作用于完成复制操作。2.2 基因算法的基本步骤存在于生物体内部染色体问题的基因控制解决方案，这一方法可以根据个体的高适应性进行选择。通过交叉和突变操作运算，这样形成新的群体，以便推测改进一套寻找算法为了下一代，因此最后汇聚到一个个体上的是最大适应环境能力，找到最优化设计。基因算法参与五个必须的过程，参数编码，最初群体设置，适宜方程设定，基因算法和控制参数。这五个关键因素，从基因算法的核心内容中形成。基本算法如下；81） 选择编码参数和决定适应方程。2） 确定遗传策略和初始化群体包括群体大小。具体是：选择，交叉和突变的方位，交叉概率的参数，以及突变概率的参数等等。3） 在解码后估算个体位串在群体中的适应性。4） 根据选择遗传策略使遗传算法作用于当前群体，包括选择。选择进料，从目前的群体中选择一些高适应性个体使下一代直接遗传其特性。交叉，可以让每一个个体在随机选择运算配对后，进入匹配集合，并且接受交叉概率从而通过基因伴侣彼此相互交换产生新个体。突变，通过突变并且利用突变概率，在交叉之后可以使得新个体携带特殊群体的基因。5） 结束条件推断。如果满足条件终止，那么就停止计算，并且输出结果。适应性最大的个体被认为是最优结果。否则进入下一步。3 进化的粒子算法在广泛空间内，寻找和交叉突变能力可以提高寻找整体最优方案的效率。也就是说，遗传算法可以帮助改进粒子算法。在迭代程序中，适应性最好的颗粒个体中的1/3通过选择算法直接进入下一代，然后让1/3下代中的任意两个新形成粒子进行交叉，最后使这部分中的1/3进行突变，接着进行局部选择。通过基因交叉算法运算，可以增加局部的多样性，并且得到具有优势粒子，其携带最适生存特性的特点，加快粒子集合速度。通过粒子部分的突变运算可以放大搜索区域，避免局部运算的早熟现象。在提高PSO算法方面，采取非线性递减模式，这是根据PSO算法的惯性重量，其可以提高原始算法的集合性能。3.1 惯性重量的非线性递减惯性重量是通过全局和局部搜索算法定义的。如果值很大，那么质点先前一时刻的速度也遭受到很大的影响。这样至少可以避免局部极值，并且全局搜索能力也可以变得很强。如果值很小，那么质点的前一刻速度也会受到影响变小，并且局部搜索能力会变强，这样将会更适合集合算法。公式（1）展示了一个部分递减非线性函数的惯性重量9，并且它的集中速度要优于线性惯性重量系数，而且获得更好地解决方式。（1）在此， ， ， ，代表了最大迭代次数，最大和最小惯性重量， 是目前当代迭代次数。由式（1），可以看出迭代重量系数在最开始的迭代中是最大的，在迭代过程中非线性递减，在迭代最后时可达到最小值。因此可以获得全局和局部搜索的平衡能力。 3.2 基因算法的操作认识1）选择算法的认识，适应部分选择算法，首先计算所有在群体中适应的个体总数，然后计算在整个适应总数中适应个体的比例。为了选择最好性能的1/3粒子，让选择运算直接进入下一代。以此来保持质点数量的能力。最好革命，可以更好地选出最佳的直接解决方案。2）交叉算法的认识，首先在输入的一个设备的1/3选择出粒子，关注每一个粒子随机交叉的概率在设备中，然后让交叉算法在任意两者中进行。在此必须值得注意的是应该确保产生的下一代质点具有相同数量以此确保质点数量不会改变。更新位置公式和速度，是由于新粒子是用公式（2）和式（3）表示的。第二，再次评价新产生一代的适应性，并且和上一代父母粒子的适应性进行比较。如果下一代的适应性优于上一代，那么用下一代代替上一代，否则保持上一代不变进入下一代。 （2）（3） 在此代表矢量代置，表示维数，和代表位置向量和空间内的速度向量，用于交叉质点的选择，并且和表示空间位置和速度向量，新一代质点在交叉后的向量。rand（）是一个随机向量，在搜索空间内一般为0，1 范围。3）突变运算认识，首次选择一个重新初始化的方法在这次论文中，以使质点初次突变以此来确定选择质点的维数，并且可以避免出现早熟现象。其次可以再新子代和父母代进行适应性比较，这一进程和交叉运算的步骤2是相似的。 3.3 算法步骤提高PSO算法步骤如下：1） 初始化质点集设置m大小数量，搜索空间维数n，在 维空间随机制造m个质点，形成矩阵，每个质点随机产生速度值，形成质点的制度改变矩阵，设置学习因子和，惯性重量系数和 ，编码模型，最大初始化数字次数和最后设置初始化数次数，在现在一代成为1.2） 更新初始化重量系数根据（1）式,评价每个质点的数量和计算适应性 根据客观方程。3） 实行基因运算操作。首先计算每个质点个体的适应性，根据步骤2，然后在1/3中选出最佳部分，直接进入下一代部分。其次，对他们进行交叉操作，为了创造1/3的质点的下一代，并且位置公式和速度交叉，速度交叉的代表是公式（2）和（3）.最后为了扩大参数搜索区域和调处区域集合，因此随机初始化运算。4） 更新每个粒子的个体极值在群体中，比较目前 和他们个体极值 。如果 超过 ，那么就更新个体极值让 代替 。5） 更新群体中的全局极值，比较全部群体，即全局 中的所有在历史上新产生的个体 。如果 个体的适应值超过，那么就用代替全局极值 。6） 通过式（2）和（3）更新质点的速度和位置，得出新的群体 。7） 判断是否满足终值条件（通常检验是否满足误差精度或最大迭代次数）。如果不满足则退回步骤2，否则停止寻找并且输出结果。图1展示了改进PSO算法的惯性因子非线性减少和遗传算法融合的步骤。 图1 改进粒子算法的步骤4. 仿真与分析4.1 控制算法的设计有两种不同的仿真控制算法可以进行便利比较，他们分笔试改进PID控制算法和人为仿真智能控制算法。1） 改进PID算法 （4） 在此E时特征误差临界值在特征模型中，并且控制参数需要分别协调一致，U，和 。U可以通过实验获得， ， 和 可以通过论文前面所推理的方式求得。2）HSIC基于控制算法 （5）在此， 是代表误差的极限值，并且他们改变随着典型特征值变化，还有个别控制参数 ， ，和 。在此U可以从实验中获得，其它的可以根据论文前面所述的方法求得。为了得到更好地控制过程和动态特性，一般改进PSO算法的方程选择下式（6）。（6）在此 是超越系统， 和 分别是重量值.4.2参数确定根据控制算法，采用改进粒子群算法整定控制参数，并且确定若干粒子参数范围是50，大部分迭代次数是500，学习因子，惯性重量系数和，基因交叉系数是0.7，并且突变操作采用初始化模式。在Matlab环境下，大约可根据Simset和Sim的M文件，能够优化再控制算法内的所有参数，图2和图3动态描述了仿真迭代的过程。这两幅图精确展示了控制算法1和控制算法2的客观精度。优化参数的结果表示如下：，这是控制算法1的结果。，以上是控制算法2的结果。图2控制算法1的迭代曲线图图3 控制算法2的迭代曲线图4.3仿真及其分析由程序模型10可得公式（7）（7）在Matlab环境下进行仿真实验，采取改进粒子群算法来优化整定控制参数，图4展示了系统的反应曲线。该图可以被认作是过快响应时间和大振荡产生的曲线，并且反映了超调现象，但是对于控制算法2的曲线而言，控制算法2的曲线显得平滑和稳定的多，并且没有超调现象。这也反映了控制算法2优于控制算法1。图4 通过PID和HSIC的反应曲线图5总结针对在相同结构的模型中，设计的这两种算法，即改进PID控制算法和多模式给予HSIC的控制算法。依靠改进的PSO算法，整定和优化了控制参数的这两种控制算法。在Matlab环境下，仿真结果显示通过基于改进PSO算法的参数整定控制系统其控制效果更好。参考文献：1Van den Bergh ,Enegelbrecht A. Using neighborhood with the guaranteed convergence PSO Z.2003 IEEE swarm intelligence symposium .USA s.n. 2003 :235-242.2LEI Kai-fa. Particle Swarm Optimization and Its Application Research D.Chongqing: Southwest University ,2006:23-72.3WANG Hao .Comparative Study on Four Sorts of Intelligent Alogorithm J.Fire Control and Command Control ,2008(33):71-75.4WANG Yuhong ,LI Wei .A simulated annealing algorithm for training empirical potential function of protein folding J .CHEN RES CHINESE U,2005,21(1):73-77.5 DUAN Haibin ,WANG Daobo ,YU Xiufen .Research on Some Novel Bionic Optimization Algorithms J.Computer Simulation ,2007 ,24 (3):169-172.6LUO Chunsong .Improved PSO algorithm and its application in control parameter sketch D. Changsha: Human University ,2009.7QIU Zhiping ,ZHANG Yuxing . Parametric optimization design of aircraft based on hybrid parallel multi-objective tabu search algorithmJ .Chinese Journal of Aeronautics,2010 ,23 (4) :430-437.8LI Minjiang .Basic Theory and Its Application of Genetic Algorithm M.Beijing: Science Press,2002.9YU Miao ,ZHU Lixi, DONG Xiao-min et al. Human simulated intelligent control of buffer system based on vibration insulation of magnetic current J .Journal of Central South University : Nature Science Edition ,2009 ,40(9):58-62.10ZHAO Jinxian ,ZHANG Zhijiang .Application of Fuzzy Adaptive PID Control in Sewage Treatment Dissolved Oxygen Control J.Machin Building & Automation ,2011,40(4);164-164.Oct 2012机床与液压Hydromechatronics EngineeringVol 40No 19Received: 2012 08 22* TAN Xiankun E- mail: txkcx11163 comDOI: 10 3969/j issn 10013881 2012 19 005Improved Control Algorithm Based on Particle SwarmOptimization and Its Simulation ResearchTAN Xiankun*Polytechnic School，Chongqing Jiaotong University， Chongqing 400074， ChinaAbstract:The performance of control system is determined by the control parameter of controllerAimed at the puzzle of parameter selection for particle swarm optimization ( PSO)control algo-rithm that the phenomenon of premature convergence made the basic PSO algorithms have beeneasy to get in local optimal solution，and resulted in impossible convergence to global extremum，as well as being not so high in search precision and slower in convergence speed， the paper pro-posed a sort of improved control algorithm based on particle swarm optimization In the paper，itdiscussed the particle swarm optimization algorithms with genetic thought ( GAPSO) ，researchedon improved algorithm of PSO，and made the comparative study for proposed control algorithmby means of simulation experiment The response curve of simulation result demonstrated that itwould be better in comparison with conventional method in dynamic and steady performance， andverified the reasonability and feasibility of the improved control algorithm The research resultshows that the improved algorithm of PSO proposed by the paper is more effective for controllerparameter tuningKey words:particle swarm optimization， genetic thought，parameter tuning， improved control al-gorithm of PSO1 IntroductionParticle swarm optimization ( PSO) 1and thegenetic algorithm( GA)all are the evolutionary algo-rithm based on the theory of biology evolutionism andgenetics etc for solving optimization problem，andeach of them has own characteristic PSO algorithmhas lots of advantages such as being faster in conver-gence rate，less in parameter tuning，simple and eas-ier in implementation，little in encoding comparedwith other algorithm and so on，and therefore it iswidely used in many fields such as optimization of ob-jective function，modeling of biologic system，train-ing of neural network，control of fuzzy system and soon In engineering control fields，in order to enhancethe speediness，accuracy and stability，the technolo-gy of advanced intelligent control has been obtainedwide generalized application，and PSO algorithm hasalways been used in controller of intelligent controlstructure so as to make the parameter tuning 2 The genetic algorithm is a sort of search methodbased on principles of biology evolution，and it hasbetter ability of global optimization and optimizationstrategy of randomization 3 4 Up to now，GA al-gorithm has been become a more mature analysismethod，and it is widely used in many fields such ascombination optimization，signal processing，machinelearning，artificial life and adaptive control and soon Compared PSO with GA algorithm，there aresome differences 5 For instance，firstly PSO algo-rithm owns better memory characteristics，and keptbetter solving particle GA algorithm only makes useof new population produced by current generation，and the new population produces through crossoverand mutation Secondly for PSO algorithm，it is sim-ple in principle，less in control parameter and re-duced in computing load Thirdly PSO gave the evo-lution equation，the position and speed of particle isin quantification and modeling，and comparatively itis stronger in operability Both PSO and GA repre-sent as the simulation and research of biological worldlaws，and it is a sort of stochastic search techniqueand owns the global optimization characteristic andimplicit parallelism And at the same time，in theface of complex control objective，it appears the dis-advantages of premature convergence and low conver-gence For this reason，the paper explored a sort ofimproved algorithm of particle swarm optimizationbased on genetic thought，and made the simulationresearch for the above mentioned2 Particle swarm optimization based on ge-netic thought2 1 Survey for genetic algorithmThe basic thought of genetic algorithm 6 isthat by means of encoding technology，it acts onchromosome ( binary digit string) ，and simulates theevolutionary process of population made up of chro-mosome，and is a sort of random search algorithmGenetic algorithm forms a new string populationthrough the selection， crossover and mutation of chro-mosome in randomness and organization so as tosolve，and makes evaluation for chromosome It se-lects the chromosome of better fitness to carry throughthe genetic reproduction The basic conception in thealgorithm is briefly introduced as the following 7 1)Gene encoding，the biologic shape is deter-mined by gene coding- strand In the genetic algo-rithm，by use of binary system code each decoding ofoptimization objective forms a gene coding- strand，namely the individual， and also called as the chromo-some2)Population，the population is a set of indi-vidual，namely it is a set of solution in solving prob-lem3)Crossover，the reproduction in living natureis completed through chromosome crossover，and theGA makes it be an operator In the crossover opera-tion process，there exists random information ex-change，and the aim is that it produces the new genecombination and new individual4) Mutation，the mutation operation usuallytakes mutation probability to execute NOT mutation ofcorresponding Bit，namely some gene value of chro-mosome changes from 1 to 0，or from 0 to 1，andthereby it gets the new individual The mutation op-erating operation can promotes the diversity of proge-ny individual，and expand the solution space Themutation operation decided the local search ability ofgenetic algorithm，and the combined action togetherwith crossover operator completes the global and localsearch of optimal solution5)Fitness，the survivability of biosome dependson fitness degree of environment The fitness reflec-ted the evolution law in nature of survival of the fittestand natural selection In the optimization problem，the corresponding function value of each solutionpresents the good or bad trend with solved problemAnd the evaluation value is called as the fitness of al-gorithm6)Selection，also it is called as reproduction orcopy operation，and the individual of high fitnesswould be put in a certain set so as to get ready forcrossover and mutation operation It selects excellentchromosome of high fitness and performance as theparent individual，and therefore the excellent charac-teristic can widely distributed in the large scope ofnext generation population，thereby it can speedilysolve the problemThere are three sorts of crossover，mutation andselection operating operator，in which the crossoverand mutation operation are used for producing thenew individual so as to completes the reproductionoperation The selection operation is used for comple-ting the copy operating2 2 Basic step of genetic algorithmGA dominates the solution of problem to be asthe chromosome inner the biosome，and it makes theselection according to individual of high fitnessThrough operating operator of crossover and muta-tion，it forms the new population from generation togeneration so as to conjecture the improved set ofsearching optimization for next generation，thereby fi-nally it converges to an individual of most adaptiveenvironment，and finds the optimal solution The ge-netic algorithm involves five essential factors，param-eter encoding，setting of initial population，and de-sign in fitness function，genetic operation and controlparameter The above five key factors formed the corecontent of genetic algorithm The basic glow of algo-rithm is as the following 8 92TAN Xiankun: Improved Control Algorithm Based on Particle SwarmOptimization and Its Simulation Research1)Select encoding pattern and determine fitnessfunction2) Determine genetic strategy，and initializepopulation including population size，method of se-lection and crossover as well as mutation，parametersof crossover probability Pcand mutation probabilityPmand so on3)Compute the fitness of individual Bit string inthe population after decoding4)Make genetic operator act on current popula-tion according to the selected genetic strategy，inclu-ding that Selection，it charges in selecting some indi-viduals of high fitness directly to inherit into nextgeneration population from current population Cross-over，it makes each individual in the match set afterSelection operation randomly match into the pairs，and takes the crossover probability to carry throughthe exchange for partial gene of them so as to producenew individual Mutation，it makes specified popula-tion after crossover carry through the mutation withmutation probability5)End the condition judgement If it satisfiesthe end condition then it stops the computing，andoutput the individual of maximum fitness to be as theoptimal solution Otherwise it returns to step 3 Improved algorithm of PSOThe wide space search and crossover- mutation a-bility can high efficiently search the global optimalsolution By means of that，it can improve the PSOalgorithm In the iteration process，the particle indi-vidual of one third of the best fitness makes selectionoperation directly go into the next generation，andthen makes crossover between any two form particleof one third in next generation，and the final particleof one third forms through mutation operation for theselected particle By means of genetic crossover oper-ator operating，it increased the diversity of particle，and took full advantage of particle characteristic inbest fitness so as to make better characteristic carrythrough the heredity，and it quickened the conver-gence speed of particle It enlarged the search areathrough mutation operation of partial particle，and a-voided the premature phenomenon of local optimiza-tion In the improved PSO algorithm，it adopts non-linear descending mode for the inertia weight in PSOalgorithm，and it can improve the convergence per-formance of the original algorithm3 1 Nonlinear descending of inertia weightThe inertia weight system w determined the abil-ity of global and local search in algorithm If the wvalue is larger then the influence of particle subjectedto previous speed of a moment also is larger It canavoid getting in least local extremum，and the globalsearch ability would be stronger If the w value isless then the influence of particle subjected to previ-ous speed of a moment also is less，and the search a-bility of local particle would be stronger，and there-fore it would be propitious to algorithm convergenceThe formula ( 1)shows a sort of descending functionof nonlinear inertia weight 9 ，and it has better con-vergence speed than linear inertia weight coefficient，and obtains better solving qualitywi=wstart w()endtit()max2+wstart w()end2tit()max+ wstart( 1)In which，tmax， wstart， wendis respectively the most iter-ation number of times，maximum and minimum of in-itial inertia weight，and tiis the current iterationnumber of generation From formula ( 1) ，it can beseen that the iteration weight coefficient has maximumat the beginning of iteration，and in the process of it-eration it presents a sort of nonlinear descending， andat the last time of iteration process it reaches to theminimum So it can obtain the balance ability ofglobal and local search3 2 Operating realization of genetic operator1)Realization of selection operation，adoptingproportion selection operating，it first computes thesummation of all individual fitness in the population，and then computes the proportion of individual fitnessin the whole fitness summation To select one thirdparticle of best performance makes selection operatingdirectly go into the next generation so as to keep theheredity of best evolution ability in particle popula-tion，and it can better search optimal solution quick-ly2)Realization of crossover operating，first it se-lects the particle of one third to put into a set，andpays a random crossover probability of each particlein the set，and then makes crossover operating be-tween any two Here it must pay attention to that itshould ensure to produce next generation particle ofthe same number so as to maintain the number not tochange of population particle The update formula of03Hydromechatronics Engineeringposition and speed for new particle is shown as re-spectively the formula ( 2)and ( 3) Secondly itmakes the fitness evaluation again for sub- particle ofnew generation produced，and makes the comparisonwith parent particle fitness If the fitness of sub- parti-cle is better than parent particle fitness then it wouldbe replaced，and otherwise it keeps parent particle togo into the next generationX1()t= rand( ) X1( )t+1 rand()( )X2( )tX2t( )= rand( ) X2( )t+1 rand()( )X1( )t( 2)V1()t=V1( )t+ V2( )tV1( )t+V2( )t V1( )tV2()t=V1( )t+ V2( )tV1( )t+V2( )t V2( )t( 3)In which，X represents the position vector in D di-mension space，X( t)and V( t)is respectively theposition vector and speed vector of the space used forselecting cross operating particle，and X ( t)and V( t)is respectively the space position and speed vec-tor of new generation particle after cross Rand ()isa random vector of search space over interval 0， 1 3)Realization of mutation operating It first se-lects an anew initializing method in this paper so as tomake initial mutation for partial dimension of selectedparticle，and it can avoid appear to get into the pre-mature convergence Secondly it makes the compari-son between fitness of new generation and parent par-ticle，and its process is similar to crossover operatingin step 23 3 Algorithm flowThe operation step of improved PSO algorithm isas the following1)Initialize particle swarmSetting population size m，search space dimen-sion n，producing m particles randomly in space Rn，forming population matrix X = x1， x2， ， xi， ，xm ，producing speed value of each particle random-ly，forming speed change matrix of particle V =v1， v2， ， vi， ， vm，setting learning factor c1andc2，inertia weight coefficient wstartand wend，encodingpattern，most iteration number of times tmax，and fi-nally setting iteration number of times in current gen-eration to be as 12)Update inertia weight coefficient according toformula ( 1) ，to make evaluation of population，andto compute fitness F( Xi)of each particle accordingto the objective function3)Execute genetic operator operating First tocompute fitness of each particle individual and makethe order according to step 2，and to select optimalparticle of one third directly to be as the next genera-tion particle Secondly to make cross operation forthem，to produce the particle of one third of nextgeneration，and the formula of position and speedcross is respectively the formula ( 2)and ( 3) Fi-nally to make random initializing operating for themso as to enlarge the parameter search area and jumpout the local convergence4)Update individual extremum of each particlein the population，and make comparison betweencurrent F( Xi)and itself individual extremum Pi IfF( Xi)excels Pithen it would update the individualextremum Piby F( Xi) 5)Update the global extremum of population，and make the comparison among all Piproduced new-ly by each particle in the whole population with glob-al Pgin history If there exists that Pifitness value ofparticle excels Pgthen the global extremum Pgwouldbe updated by Pi6)Update the speed and position of particle byformula ( 2)and ( 3) ，and produce the new popula-tion X( t +1) 7)Judge whether it satisfies the end condition( usually it is set as the error precision or most itera-tion generation of times) If it is not satisfied then itwould be returned to step 2，and otherwise it stopsthe search and outputs the resultFig 1 shows the flow of improved PSO algorithmof inertia factor nonlinear descending with fused ge-netic algorithmFig 1Flow of improved PSO algorithm13TAN Xiankun: Improved Control Algorithm Based on Particle SwarmOptimization and Its Simulation Research4 Simulation and its analysis4 1 Design of control algorithmHere it takes two sorts of control algorithm tomake the simulation for convenience of comparison，and they are respectively the improved PID control al-gorithm and human simulated intelligent control algo-rithm1)Improved PID algorithmu = sgn( e) U，e() Eu = Kpe + Kit0edt + Kdee ()E( 4)In which，E is the error feature threshold levelin the feature model，and the control parameter nee-ded tuning is respectively U，Kp，Kiand Kd WhereU can be gotten from experience，and Kp，Kiand Kdcan be gotten by means of parameter tuning methodproposed in this paper2)HSIC based control algorithmu = sgn( e) U，e E()1u = KP1e + KD1ee E1e E()2u = KP2e + KD2ee E2e E()1u = KP3e + KD3ee E2eE1e E3eE()2u = un1e E3e E()2( 5)In which，E， is respectively the threshold level oferror and its change rate in the feature model，andthe control parameter is respectively U，Kp1，KD2，KP2，Kd2，Kp3and KD3Where U can be gottenthrough the experience，the other can be gotten bymeans of parameter tuning method proposed in thispaper In order to get better control process and dy-namic characteristic，the objective function of im-proved PSO algorithm is selected as formula ( 6) J = w1t0(e( t)+ u2( t) ) dt + w2 ( 6)Where is the overshoot of system，and w1，w2is re-spectively the weight value4 2 Parameter tuningFor the above control algorithms，it adopts im-proved PSO algorithm to tune the control parameter，and makes setting be as number of the most particleswarm is 50，number of the most iteration times is500，learning factor c1= c2=1 2，inertia weight co-efficient wstart=1 2 and wend=0 4，genetic crossoverfactor is 0 7，and the mutation operating adopts ini-tializing pattern Under the environment of Matlab，by means of order of Simset and Sim in M file，it canoptimize the parameter of the above control algo-rithm，and the Fig 2 and Fig 3 demonstrates respec-tively the process of simulation iterationIt showsthat the control algorithm 2 excels the control algo-rithm 1 in objective precision The optimizing resultof tuned control parameter is respectively Kp=0 398 4， Ki= 0 001 7， Kd= 3 240 2 for control al-gorithm 1，and Kp1= 6 339 5， Kd1= 8 854， Kp2=6 706 5， Kd2= 0 148 14， Kp3= 26 468， Kd3=3 053 0 for control algorithm 2Fig 2Iteration curve of control algorithm 1Fig 3Iteration curve of control algorithm 24 3 Simulation and its analysisThe process model 10is shown as in formula( 7) G( s)=7 812 574s + 1e20s( 7)Under the environment of Matlab， the simulationexperiment adopts the above optimized control param-eter of improved PSO algorithm，the response curveof system is shown as in Fig 4 It can be seen fromthe response curve that the curve of control algorithm1 has faster response time and lager oscill
- 温馨提示:
1: 本站所有资源如无特殊说明，都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
2: 本站的文档不包含任何第三方提供的附件图纸等，如果需要附件，请联系上传者。文件的所有权益归上传用户所有。
3.本站RAR压缩包中若带图纸，网页内容里面会有图纸预览，若没有图纸预览就没有图纸。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 人人文库网仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对用户上传分享的文档内容本身不做任何修改或编辑，并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容，请与我们联系，我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
人人文库网所有资源均是用户自行上传分享，仅供网友学习交流，未经上传用户书面授权，请勿作他用。
|
2:不支持迅雷下载,请使用浏览器下载
3:不支持QQ浏览器下载,请用其他浏览器
4:下载后的文档和图纸-无水印
5:文档经过压缩，下载后原文更清晰
|
川公网安备: 51019002004831号