A Learning Servo Motion Controller Based on Cerebellar Model Articulation Controller (CMAC) for Ultra Precision Machine Tools

9322808 Cetinkut该奖项旨在通过改进控制算法来提高超精密机床中低速运动的精度。该控制算法基于小脑模型关节控制器体系结构，并期望学习机器特性，如摩擦和间隙，这些特性在低速时对机器精度有显著影响。追求纳米级的定位精度。将在国家标准与技术研究所和受让人机构进行单点钻石车床的试验工作。高功率激光聚焦的金属和非金属反射镜、光盘和盒式磁带录像机的反射镜、非球面透镜的模具和硅片的平坦化等零部件都需要超精密加工。因此，这里所寻求的运动精度的实现将对这些制造应用产生有益的影响。

9322808 Cetinkunt The award is for research aimed at improving the accuracy of low speed motion in ultra-precision machine tools by improving control algorithms. The control algorithm is based on the cerebellar model articulation controller architecture and is expected to learn machine characteristics such as friction and backlash, which have a significant effect on machine accuracy at the low speeds. Nanometer level positioning accuracy is sought. Experimental work will be conducted at the National Institute of Standards and Technology and at the grantee institution on a single point diamond turning machine. Ultra-precision machining is needed for components such as metallic and non-metallic mirrors for high power laser focusing, mirrors for compact discs and video-cassette recorders, molds for aspherical lenses, and planarization of silicon wafers. Achievement of the motion accuracies sought here would therefore have a beneficial impact on these manufacturing applications.