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基于哈默斯坦-维纳模型的两级形状记忆合金识别

Two-Stage Shape Memory Alloy Identification Based on the Hammerstein-Wiener Model.

作者信息

Copaci Dorin, Moreno Luis, Blanco Dolores

机构信息

Department of Systems Engineering and Automation, Carlos III University of Madrid, Leganes, Spain.

出版信息

Front Robot AI. 2019 Sep 4;6:83. doi: 10.3389/frobt.2019.00083. eCollection 2019.

Abstract

Thanks to characteristics, such as high force and light weight, a good biocompatibility, noiseless operation and simplicity, and relatively low-cost compared with other conventional actuators, actuators based on shape memory alloy are currently one of the most interesting research topics. They have been introduced in applications such robotics, medicine, automation, and so on. For a good actuator integration of these types of applications, proper control is needed, which seems to be a difficult task due to the hysteresis, dilatory response, and non-linear behavior. This work presents a new form of modeling of this type of actuator based on Hammerstein-Wiener model. This has been identified in two stages of the operation. When the activation temperature for the actuator is obtained by the Joule effect, electrically energy is transformed into thermal energy. In the second stage, the thermal energy is transformed into mechanical work. To fulfill this objective, experimental data [e.g., the input signal (pulse-width modulation), temperature signal, and position signal] from the two stages was obtained for a specific shape memory alloy wire and for specific environmental conditions. This data was used in the modeling process. The final model consists of a combination of the models from the two stages, which represent the behavior of the shape memory alloy actuator where the input signal is the pulse-width modulation signal and the output signal are the position of the actuator. Our results indicate that our model has a very similar response to the behavior of the real actuator. This model can be used to tune different control algorithms, simulate the entry system before manufacture and test on real devices.

摘要

由于具有诸如高力和轻重量、良好的生物相容性、无噪音运行和简单性以及与其他传统致动器相比成本相对较低等特性,基于形状记忆合金的致动器目前是最有趣的研究课题之一。它们已被引入机器人技术、医学、自动化等应用领域。为了在这些类型的应用中实现良好的致动器集成,需要进行适当的控制,由于滞后、延迟响应和非线性行为,这似乎是一项艰巨的任务。这项工作提出了一种基于哈默斯坦-维纳模型的这种类型致动器的新建模形式。这在操作的两个阶段中得到了确定。当通过焦耳效应获得致动器的激活温度时,电能转化为热能。在第二阶段,热能转化为机械功。为了实现这一目标,针对特定的形状记忆合金丝和特定的环境条件,获取了来自两个阶段的实验数据[例如输入信号(脉宽调制)、温度信号和位置信号]。这些数据被用于建模过程。最终模型由两个阶段的模型组合而成,它们代表了形状记忆合金致动器的行为,其中输入信号是脉宽调制信号,输出信号是致动器的位置。我们的结果表明,我们的模型与实际致动器的行为具有非常相似的响应。该模型可用于调整不同的控制算法、在制造前模拟输入系统以及在实际设备上进行测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b757/7805934/988f6a6dcdb2/frobt-06-00083-g0001.jpg

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本文引用的文献

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