{"title":"A Robust Adaptive Control with Extended State Observer for a Piezo-actuated Nano-positioner","authors":"Pengfei Xia, Wei Wei, Zaiwen Liu, Min Zuo","doi":"10.1109/DDCLS.2019.8908943","DOIUrl":null,"url":null,"abstract":"Positioning control of a nano-positioner driven by a piezoelectric actuator is discussed. Robust adaptive control with extended state observer is presented for the trajectory tracking control. Radial basis function neural network (RBFNN) is utilized to estimate the unknown nonlinearities. Extended state observer (ESO) is also taken to observe the total disturbance, which includes external disturbances and hysteresis. Both the RBFNN and the ESO are utilized to reduce the dependence on model information. A nano-positioner model is established. Simulations confirm the robust adaptive control with ESO is effective in improving positioning accuracy.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"64 1","pages":"1003-1007"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DDCLS.2019.8908943","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Positioning control of a nano-positioner driven by a piezoelectric actuator is discussed. Robust adaptive control with extended state observer is presented for the trajectory tracking control. Radial basis function neural network (RBFNN) is utilized to estimate the unknown nonlinearities. Extended state observer (ESO) is also taken to observe the total disturbance, which includes external disturbances and hysteresis. Both the RBFNN and the ESO are utilized to reduce the dependence on model information. A nano-positioner model is established. Simulations confirm the robust adaptive control with ESO is effective in improving positioning accuracy.