Giant magnetostrictive actuators(GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model...Giant magnetostrictive actuators(GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations,including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function(E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 μm and decreases the root-mean-square error in displacement to within0.5 μm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data;it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.展开更多
在同向双螺杆挤出机中通过熔融接枝反应制备了EPM g GMA ,将其与PBT在转矩流变仪中熔融共混可以获得增韧的PBT工程塑料 .实验中EPM g GMA接枝率的测定采用红外工作曲线法 ,选用CCl4 做溶剂以避免溶剂对样品吸收峰的干扰 .随着EPM g GMA...在同向双螺杆挤出机中通过熔融接枝反应制备了EPM g GMA ,将其与PBT在转矩流变仪中熔融共混可以获得增韧的PBT工程塑料 .实验中EPM g GMA接枝率的测定采用红外工作曲线法 ,选用CCl4 做溶剂以避免溶剂对样品吸收峰的干扰 .随着EPM g GMA接枝率的增加 ,PBT EPM g GMA的缺口冲击强度相应提高 ,共混物中EPM g GMA的粒径尺寸减小 ,当EPM g GMA的接枝率为 4 7mL 1 0 0gEPM时 ,EPM g GMA的粒径尺寸可达 0 5 μm ,PBT EPM g GMA的缺口冲击强度达到 5 1 6kJ m2 ,是纯PBT的 3展开更多
基金This work was supported by the Basic Technological Research Projects(Metrology)(Grant No.JSJL2020206B001).
文摘Giant magnetostrictive actuators(GMAs) are a widely used type of micro-nano actuator, and they are greatly significant in the field of precision engineering. The accuracy of a GMA often depends on its hysteresis model. However, existing models have some limitations,including the difficulty of identifying their parameters and the tradeoff between the quantity of modeling data required and the level of precision achieved. To solve these problems, in this paper, we propose a Preisach inverse model based on equal-density segmentation of the weight function(E-Preisach). The weight function used to calculate the displacement is first discretized. Then, to obtain a finer weight distribution, the discretized geometric units are uniformly divided by area. This can further minimize the output displacement span, and it produces a higher-precision hysteresis model. The process of parameter identification is made easier by this approach, which also resolves the difficulty of obtaining high precision using a small amount of modeling data. The Preisach and the E-Preisach inverse models were investigated and compared using experiments. At frequencies of 1 and 5 Hz, it was found that the E-Preisach inverse model decreases the maximum error of the feedforward compensation open-loop control to within 1 μm and decreases the root-mean-square error in displacement to within0.5 μm without the need to increase the number of measured hysteresis loops. As a result, the E-Preisach inverse model streamlines the structure of the model and requires fewer parameters for modeling. This provides a high-precision modeling method using a small amount of modeling data;it will have applications in precision engineering fields such as active vibration damping and ultra-precision machining.
