基于智能优化算法的Jiles-Atherton磁滞模型参数计算研究

发布时间：2018-03-14 08:47

  本文选题：智能优化算法　切入点：磁滞回线　出处：《浙江师范大学》2015年硕士论文　论文类型：学位论文

【摘要】：磁滞非线性现象常见于物理系统和电磁装置中,对设备的安全运行及系统能否稳定运行起着重要的作用。近年来,随着新型材料的发展,例如磁电复合材料,由于其独特的物理性质,在各种各样的微型器件及整体化装置,如微传感器、微电子机械系统设备以及高密度信息存储器中都有潜在的应用；磁性形状记忆合金(MSMA)作为一种新型的智能材料,在驱动器制造方面具有良好的应用前景；另外,磁热疗是一种很有前途的癌症治疗技术,为癌症患者带来了新的希望。上述这些新型材料及技术的应用都涉及到了磁滞非线性现象,因此新型智能材料的设计和分析与众多高新技术的研发在很大程度上依赖于磁滞建模及其参数计算的准确性,了解和分析磁滞特性具有重要意义。建立磁滞模型后,一般会包含很多待确定的参数,参数的取值不同代表着不同的物理状态。模型设计的有效性在很大程度上取决于参数提取的准确性,无论一个磁滞模型的理论有多完美,如果没有行之有效的参数计算方法,其可操作性也就无法得到保证。目前从物理和数学两种角度出发,研究者已提出了多种描述磁滞现象的模型,较常见的有：Bouc-Wen磁滞模型、Preisach磁滞模型、Jiles-Atherton(JA)磁滞模型。在众多的磁滞模型中,JA模型的物理意义清晰、参数较少、仅包含一个一阶常微分方程,但是模型参数识别的复杂和困难性一直以来困扰着人们。许多传统的优化方法曾用来计算JA模型参数,但是这些方法容易受初始值选择的影响,其结果是算法的收敛性往往得不到保证、易陷入局部极小且计算量通常也会很大。更令人担忧的是,很多优化方法,尤其是基于求导寻优的优化方法,面对模型方程的不连续、离散、单峰与多峰等数学性质也越来越显得“力不从心”。最近发展起来的基于群体智能的新型优化技术在各种领域的参数计算中受到了很大的关注。智能优化算法是基于仿生学的随机优化算法,典型的方法有Eberhart与Kennedy提出的粒子群优化(Particle Swarm Optimization, PSO)算法、遗传算法(Genetic Algorithm, GA)、差分进化算法(Differential Evolutionary,DE)和Dorigo提出的蚁群算法等。这些方法被广泛应用于科学研究和实际问题求解中,并取得了传统优化方法无法取代的成效。智能优化类算法本身具有很强的适用性,且对目标函数的连续性无任何要求、对初始解的选取不敏感,因此把智能优化算法作为求解复杂优化问题的候选算法是非常具有现实意义的。本文所要解决的主要问题包括：(1)、在充分熟悉国内外研究现状、深刻理解并掌握智能优化算法及MATLAB/Simulink动态仿真集成环境的基础上,基于物质磁化机理从物理角度出发提出了一种粒子群优化算法(PSO)结合MATLAB/Simulink动态仿真集成环境的Jiles-Atherton(JA)磁滞回线模型参数计算方法。并分别以无噪及加噪的仿真数据,对三组参数值不同的JA模型进行数值实验。(2)、另外本文构建了基于Simulink模块的JA模型方程,通过求解模型方程得到了准确的B-H磁滞回线。并将JA模型的Simulink模块方程与算法实现了无缝融合,为算法优化的顺利运行提供了保障。(3)、算法中控制变量的取值不同会对优化精度、计算时间及收敛性有很大的影响,因此针对不同的问题,如何选择最优的参数配置是本文需要关注的一个问题。(4)、最后将计算结果与遗传算法做了比较,经分析发现对于复杂的Jiles-Atherton非线性磁滞模型参数计算,PSO算法表现出很好的鲁棒性,而且无论是计算精度、还是收敛性都要好于遗传算法。可见,在材料磁滞特性的研究上,针对模型参数识别的复杂和困难性,粒子群优化算法结合MATLAB/Simulink动态仿真集成环境是一种有效可行的研究方法。
[Abstract]:The nonlinear hysteresis phenomenon is common in physical systems and electromagnetic devices, for the safe operation of equipment and system plays an important role in the stable operation. In recent years, with the development of new materials, such as magnetoelectric composite materials, because of its unique physical properties, micro devices and integrated in a variety of devices, such as micro sensors. The potential applications of micro electro mechanical system equipment and high density information storage; magnetic shape memory alloy (MSMA) as a new type of smart materials, has a good application prospect in the drive manufacturing; in addition, magnetic hyperthermia is a promising cancer treatment technology, has brought new hope for cancer patients. The application of these new materials and techniques are related to the phenomenon of nonlinear hysteresis, so the design and analysis of a new type of intelligent material with many high-tech R & D in it A large extent dependent on the accuracy of Hysteresis Modeling and parameter calculation, is of great significance to understand and analyze the hysteresis characteristics. A hysteresis model, usually contain many parameters to be determined, different parameters represent different physical states. The validity of the model design depends largely on the accuracy of parameter extraction, no matter a hysteresis model theory is perfect, if there is no effective parameter calculation, the operability is not guaranteed. Starting from the two perspectives of physics and mathematics at present, researchers have proposed a variety of models to describe the hysteresis phenomenon, the more common are: Bouc-Wen hysteresis model, Preisach hysteresis model, Jiles-Atherton (JA) model. In many of the hysteresis hysteresis model, the JA model with clear physical meaning, less parameters, contains only a first-order differential equation, but the model parameters The identification of complex and difficult has been plaguing people. Many of the traditional optimization method was used to calculate the parameters of the JA model, but these methods are easily affected by the initial value of the impact of the choice, the result is the convergence of the algorithm are not guaranteed, easy to fall into the local minimum and the amount of calculation is usually large. More worrying yes, many optimization methods, especially the optimization method of derivation optimization based on face model equation is not continuous, discrete, unimodal and multimodal mathematical properties is becoming more and more powerless. Recently developed new optimization technique based on swarm intelligence computation parameters in various fields have been paid great attention in the intelligent optimization algorithm is a stochastic optimization algorithm based on bionics, the typical method of particle swarm optimization and Eberhart proposed by Kennedy (Particle Swarm Optimization PSO) algorithm, genetic algorithm (Ge Netic Algorithm, GA), differential evolution algorithm (Differential, Evolutionary, DE and Dorigo) of the ant colony algorithm. These methods are widely used in scientific research and solving practical problems, and made the traditional optimization methods can not replace the effectiveness of intelligent optimization algorithm. The class itself has very strong applicability, and continuous the objective function without any requirement, is not sensitive to the initial solution selection, so the intelligent optimization algorithm as a candidate algorithm for solving complex optimization problems is very realistic. Including the main problem to be solved in the paper: (1), in the fully familiar with the domestic and foreign research present situation, understand and master the basis of intelligence optimization and dynamic simulation of MATLAB/Simulink integrated environment, based on the material magnetization mechanism this paper puts forward a particle swarm optimization algorithm from the angle of Physics (PSO) combined with MATLAB/Simulink dynamic imitation It integrated environment Jiles-Atherton (JA) parameters of hysteresis loop model method. And the results of simulated data without noise and with noise, the three group of parameters in the JA model for numerical experiments. (2), this paper constructs a JA model equation based on Simulink module, by solving the model equations obtained B-H hysteresis loop accurately. And the Simulink module equation and the model of the JA algorithm to achieve a seamless integration, provide a guarantee for the smooth operation of the algorithm. (3), values of control variables in different algorithms to optimize accuracy, computing time and has great impact on convergence, therefore, for different problems, how to choose the optimal parameter configuration is a problem needing attention in this paper. (4), the results are compared with the genetic algorithm, the analysis shows that for Jiles-Atherton nonlinear hysteresis model of complicated calculation, PSO algorithm 鐜板嚭寰堝ソ鐨勯瞾妫掓，

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