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首页> 外文期刊>International Journal of Intelligent Computing and Cybernetics >Identification of a dynamic model for shape memory alloy actuator using Hammerstein-Wiener gray box and mutable smart bee algorithm
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Identification of a dynamic model for shape memory alloy actuator using Hammerstein-Wiener gray box and mutable smart bee algorithm

机译：基于Hammerstein-Wiener灰箱和可变智能蜂算法的形状记忆合金执行器动力学模型辨识

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Purpose - The purpose of the current investigation is to design a robust and reliable computational framework to effectively identify the nonlinear behavior of shape memory alloy (SMA) actuators, as one of the most applicable types of actuators in engineering and industry. The motivation of proposing such an intelligent paradigm emanates in the pursuit of fulfilling the necessity of devising a simple yet effective identification system capable of modeling the hysteric dynamical respond of SMA actuators. Design/methodology/approach - To address the requirements of designing a pragmatic identification system, the authors integrate a set of fast yet reliable intelligent methodologies and provide a predictive tool capable of realizing the nonlinear hysteric behavior of SMA actuators in a computationally efficient fashion. First, the authors utilize the governing equations to design a gray box Hammerstein-Wiener identifier model. At the next step, they adopt a computationally efficient metaheuristic algorithm to elicit the optimum operating parameters of the gray box identifier. Findings - Applying the proposed hybrid identifier framework allows the authors to find out its advantages in modeling the behavior of SMA actuator. Through different experiments, the authors conclude that the proposed identifier can be used for identification of highly nonlinear dynamic behavior of SMA actuators. Furthermore, by extending the conclusions and expounding the obtained results, one can easily infer that such a hybrid method may be conveniently applied to model other engineering phenomena that possess dynamic nonlinear reactions. Based on the exerted experiments and implementing the method, the authors come to the conclusion that integrating the power of metaheuristic exploration/exploitation with gray box identifier results a predictive paradigm that much more computationally efficient as compared with black box identifiers such as neural networks. Additionally, the derived gray box method has a higher degree of preference over the black box identifiers, as it allows a manipulated expert to extract the knowledge of the system at hand. Originality/value - The originality of the research paper is twofold. From the practical (engineering) point of view, the authors built a prototype biased-spring SMA actuator and carried out several experiments to ascertain and validate the parameters of the model. From the computational point of view, the authors seek for designing a novel identifier that overcomes the main flaws associated with the performance of black-box identifiers that are the lack of a mean for extracting the governing knowledge of the system at hand, and high computational expense pertinent to the structure of black-box identifiers.
机译：目的-当前研究的目的是设计一个健壮且可靠的计算框架，以有效地识别形状记忆合金（SMA）执行器的非线性行为，该形状记忆合金执行器是工程和工业中最适用的执行器之一。提出这种智能范式的动机源于对实现设计一种简单而有效的识别系统的需求，该识别系统能够对SMA执行器的滞后动力响应进行建模。设计/方法/方法-为了满足设计实用的识别系统的要求，作者集成了一组快速而可靠的智能方法，并提供了一种预测工具，能够以计算有效的方式实现SMA执行器的非线性滞后行为。首先，作者利用控制方程式设计了一个灰盒Hammerstein-Wiener标识符模型。下一步，他们采用计算效率高的元启发式算法来得出灰盒标识符的最佳操作参数。结论-应用提出的混合标识符框架可以使作者发现其在SMA执行器行为建模中的优势。通过不同的实验，作者得出结论，提出的标识符可用于识别SMA执行器的高度非线性动态行为。此外，通过扩展结论并阐述所获得的结果，可以轻松推断出这种混合方法可以方便地应用于对具有动态非线性反应的其他工程现象进行建模。基于所进行的实验和实施方法，作者得出的结论是，将元启发式探索/开发的功能与灰盒标识符集成在一起，可以得到比黑盒标识符（如神经网络）更有效的预测范例。另外，派生的灰箱方法比黑箱标识符具有更高的优先级，因为它允许受过训练的专家提取手头系统的知识。原创性/价值-研究论文的原创性是双重的。从实用（工程）角度出发，作者构建了偏置弹簧SMA执行器原型，并进行了几次实验以确定并验证模型的参数。从计算的角度来看，作者寻求设计一种新颖的标识符，该标识符可克服与黑匣子标识符性能相关的主要缺陷，即缺乏提取当前系统管理知识的手段，并且计算量大与黑匣子标识符的结构有关的费用。

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