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基于粒子群优化算法的收敛速度快简单易实现的特点和免疫算法的免疫记忆、免疫自我调节和多峰值收敛的特点,本文设计出免疫粒子群算法,并将其应用于PID控制器中。仿真结果表明,免疫粒子群优化算法适用于增量式PID控制,并且基于免疫粒子群优化算法的增量式PID控制的跟踪效果和抗干扰能力比粒子群优化算法的PID控制和基于免疫算法的增量式PID控制跟踪效果和抗干扰能力都要好。 相似文献
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以某电厂600MW超临界机组为例,利用现场实际运行数据,基于标准粒子群算法,建立了中间点(分离器出口)焓值与水煤比之间的传递函数,结果表明,辨识模型很好的反应了实际运行曲线,是超临界机组给水控制决策和控制系统制定及设计的前提保证;同时标准粒子群辨识方法使系统辨识更加快速与准确. 相似文献
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为解决城市供水系统进行变频改造实践中出现的一系列技术问题,设计了一种基于PIC与模糊控制技术的小区供水控制系统,详细讨论了系统的组成、工作原理、运行方式、模糊控制策略的设计思想。这种系统较好地克服了传统PID控制中稳定性差、参数调整困难的问题,提高了供水质量。该系统具有运行稳定、可靠性高、节能效果明显等特点,有很高的推广价值。 相似文献
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针对供水企业降低水泵的能耗和维护成本,设计了一种基于DSP变频自动恒压供水系统。采用TMS320F240 DSP和一台160kW大功率变频器控制三台大功率水泵电机实现对某水厂的变频自动恒压供水控制;详细分析了其硬件组成和DSP编程原理。实验运行表明,此系统运行稳定可靠、稳压精度高、节能效果明显,具有很好的经济效益。 相似文献
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粒子群算法是一种仿生进化算法,源于对鸟群觅食行为的模拟,由于其计算简单、快速,被广泛应用。但是,基本粒子群算法在求解的过程中存在着全局搜索能力和局部求精能力两个性能指标之间的矛盾,算法容易陷入局部极值,进化后期的收敛速度慢。针对上述问题,提出了基于混沌变异算子的粒子群算法,可以使粒子摆脱局部极值,继续优化,加快收敛速度。将基于混沌变异的粒子群算法与模糊算法相结合,用于控制倒立摆系统的平衡。仿真实验表明,混沌变异粒子群算法优化了倒立摆系统模糊控制器的设计,改善了控制效果。 相似文献
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基于粒子群算法的模糊控制在倒立摆中的应用 总被引:2,自引:1,他引:1
粒子群算法是一种仿生进化算法,源于对鸟群觅食行为的模拟,由于其计算简单、快速,被广泛应用.但是,基本粒子群算法在求解的过程中存在着全局搜索能力和局部求精能力两个性能指标之间的矛盾,算法容易陷入局部极值,进化后期的收敛速度慢.针对上述问题,提出了基于混沌变异算子的粒子群算法,可以使粒子摆脱局部极值,继续优化,加快收敛速度.将基于混沌变异的粒子群算法与模糊算法相结合,用于控制倒立摆系统的平衡.仿真实验表明,混沌变异粒子群算法优化了倒立摆系统模糊控制器的设计,改善了控制效果. 相似文献
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针对采用硬限制的方法设计的迎角保护控制器存在操纵空行程,难以保证满意的操纵控制效果,提出了一种免疫粒子群算法的迎角保护控制器设计方法。首先根据软保护的原理设计了一种迎角/C*指令前馈-无差控制的迎角保护控制器,系统性能指标选择合适的参考模型,并设定合理的目标函数,最后引入免疫粒子群优化算法进行了控制器参数的自动寻优。仿真结果表明,基于免疫粒子群算法所设计的迎角保护控制器,能有效地选取最优的控制器参数,使迎角和过载均响应在最大保护迎角和过载限制内,达到了良好的控制效果。 相似文献
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关于水轮发电机控制系统优化问题,水轮发电机组的控制技术对于水轮发电机组稳定工作非常重要。针对保证供电质量,改善调节系统的非线性时滞特性,利用具有局部搜索能力的粒子群算法对水轮发电机组进行模糊PID控制可以确保控制的稳定性。首先,根据水轮发电机组的控制原理和模糊PID控制器的基本结构,提出具有局部搜索能力的改进粒子群算法,利用模糊PID控制器以及采用了改进粒子群算法的模糊PID控制器,用MATLAB软件对水轮发电机组进行优化控制仿真,仿真结果表明采用改进粒子群算法的模糊PID控制器具有最优的控制效果。 相似文献
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为提高抽油机在群控系统操作下的能耗特征分析精度,减少功率损耗,提出一种基于联合调度的抽油机群控系统能耗特征分析方法.设计直流供电配置下的抽油机群控系统架构,利用整数线性规划构建联合调度模型.利用粒子群算法调整系统网络偏差,采用进程代数语言形式化描述系统,把构件接口级别当作能耗特性定义,使用CSP模型完成抽油机群控系统操... 相似文献
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钟斌 《计算机测量与控制》2017,25(12):228-231
节能控制能够有效降低能耗,对保护环境等方面具有重要影响;但目前大多数电子节能控制器都是通过采用单片机技术和双向晶闸管过零触发交流调压电路对电子节能控制器进行设计;通过介绍电子的负荷特点和节能原理,分析电子节能控制器的硬件组成电路,并对电子节能控制器的主要软件程序的流程图进行设计,完成电子节能控制器设计;但这种方法节能控制效果较低,难以保证电子节能控制器性能,为此,提出一种基于模糊PID控制的嵌入式电子节能控制器设计与实现方法;首先通过对嵌入式电子节能控制器的处理器、电源电路、复位电路、系统时钟电路、JTAG接口电路、D/A转换电路、功放电路、双极性电源电路以及嵌入式电子节能控制器硬件PCB板器件布局等的设计,完成嵌入式电子节能控制器硬件设计;在此基础上,选用模糊PID控制方法对嵌入式电子节能控制器进行设计;通过分析模糊PID控制原理,介绍加入自调节因子的模糊PID控制的算法设计,以此确定输入输出隶属度函数,再利用模糊推理和模糊规则,得到电子节能控制器的模糊控制过程,从而完成嵌入式电子节能控制器的设计;实验证明,所提方法能够有效提高嵌入式电子节能控制器的节能控制效果,具有良好的使用价值。 相似文献
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为了解决电力系统的节能优化问题,本文在传统的PSO节能控制方法的基础上,提出了一种多重自适应的粒子群优化算法,应用分散控制系统设计与实现了一种新的电力节能优化控制系统。数值仿真的结果说明了使用所提出的粒子群算法的基于DCS的电力节能优化控制系统在电力调度最佳节点的搜索精确度要高于相同条件下的一般的电力控制系统。使用所提算法的电力节能优化控制系统,能有效地对电力能耗进行优化,且具有较高的实用性。 相似文献
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A hybrid of genetic algorithm and particle swarm optimization for recurrent network design 总被引:15,自引:0,他引:15
Chia-Feng Juang 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2004,34(2):997-1006
An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority. 相似文献
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某电厂300 MW单元机组控制系统采用西门子TELEPERMME DCS集散控制系统,在对其锅炉给水系统阀门切换的改造过程中,利用模糊控制理论,根据实际运行状况及运行资料,通过大量的实验分析,设计出切实可行的给水阀门切换逻辑,使燃煤锅炉给水系统给水阀门实现自动切换,解决了大容量单元机组燃煤锅炉实现全程给水自动控制所必须解决的一个关键问题。本系统的投运不仅节约了改造、更换给水系统所需的大量资金,由于机组实现自动启动,大大缩短了启动时间,使机组启动过程中消耗的燃料量明显下降,经济效益十分显著;同时也给出了模糊控制技术在控制工程中实际应用的一种思路。 相似文献
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The energy consumption of train operation occupies a large proportion of the total consumption of railway transportation. In order to improve the operating energy utilization rate of trains, a multi-objective particle swarm optimization (MPSO) algorithm with energy consumption, punctuality and parking accuracy as the objective and safety as the constraint is built. To accelerate its the convergence process, the train operation progression is divided into several modes according to the train speed-distance curve. A human-computer interactive particle swarm optimization algorithm is proposed, which presents the optimized results after a certain number of iterations to the decision maker, and the satisfactory outcomes can be obtained after a limited number of adjustments. The multi-objective particle swarm optimization (MPSO) algorithm is used to optimize the train operation process. An algorithm based on the important relationship between the objective and the preference information of the given reference points is suggested to overcome the shortcomings of the existing algorithms. These methods significantly increase the computational complexity and convergence of the algorithm. An adaptive fuzzy logic system that can simultaneously utilize experience information and field data information is proposed to adjust the consequences of off-line optimization in real time, thereby eliminating the influence of uncertainty on train operation. After optimization and adjustment, the whole running time has been increased by 0.5 s, the energy consumption has been reduced by 12%, the parking accuracy has been increased by 8%, and the comprehensive performance has been enhanced. 相似文献
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对城市用水量的科学预测是城市供水管网规划与设计基础,可以给供水系统安排生产与优化调度提供科学依据。由于传统BP神经网络应用于城市用水量预测存在训练收敛速度过慢、预测精度较低等缺陷,本文提出基于改进粒子群优化BP神经网络的城市用水量预测方法。实验结果表明,该方法的训练收敛速度、预测精度明显优于传统BP神经网络、粒子群优化BP网络的方法,可以满足供水系统生产与调度的实际需要。 相似文献
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Due to low precision and premature tendency of traditional particle swarm optimization, the reactive power optimization control of electromechanical system based on fuzzy particle swarm optimization algorithm was designed. The premise is to meet the constraints of operation conditions. The active network loss was reduced and the static reactive power optimization mathematical model of electromechanical system was constructed by changing the voltage and reactive power distribution of system. Meanwhile, the voltage did not exceed the limit, and the discrete control variables were limited by the maximum allowable action times, so that the dynamic reactive power optimization mathematical model of electromechanical system was built by minimizing the sum of network loss in twenty-four hours of a day. The particle swarm algorithm was optimized by adaptive adjustment strategy, and then the particle position of particle swarm optimization algorithm was updated. Moreover, the static and dynamic reactive power optimization mathematical model of electromechanical system was solved. Finally, the reactive power optimization control of the electromechanical system is realized. Experimental results show that the proposed method has high convergence performance, so it is able to realize the precise control of reactive power optimization for electromechanical system and eliminate the voltage exceeding specified limits of electromechanical system. In this way, the node voltage can always be within the specified range. 相似文献
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