{"title":"Offset-free Energy-optimal Model Predictive Control for point-to-point motions","authors":"Xin Wang, J. Swevers","doi":"10.1109/ACC.2015.7170744","DOIUrl":null,"url":null,"abstract":"This paper discusses Offset-free Energy-optimal Model Predictive Control (offset-free EOMPC) which is a MPC algorithm to realize time-constrained energy-optimal point-to-point motion control with high positioning accuracy for linear time-invariant (LTI) systems. The offset-free EOMPC approach is developed based on our previous research - Energy-optimal Model Predictive Control (EOMPC) - which aims at performing energy-optimal point-to-point motions within a given motion time. A drawback of the EOMPC method is that it cannot achieve high positioning accuracy in the presence of unmodelled disturbances or model-plant mismatch. In order to cope with this problem, a `disturbance model' strategy is adopted: the system state is augmented with disturbance variables. Based on the `disturbance model', the disturbances are estimated and the effects of which are cancelled. Experimental validation of the offset-free EOMPC on a linear motor with coulomb friction and cogging disturbances has been implemented and the results show that time-constrained energy-optimal point-to-point motion with high positioning accuracy is achieved.","PeriodicalId":223665,"journal":{"name":"2015 American Control Conference (ACC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 American Control Conference (ACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACC.2015.7170744","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper discusses Offset-free Energy-optimal Model Predictive Control (offset-free EOMPC) which is a MPC algorithm to realize time-constrained energy-optimal point-to-point motion control with high positioning accuracy for linear time-invariant (LTI) systems. The offset-free EOMPC approach is developed based on our previous research - Energy-optimal Model Predictive Control (EOMPC) - which aims at performing energy-optimal point-to-point motions within a given motion time. A drawback of the EOMPC method is that it cannot achieve high positioning accuracy in the presence of unmodelled disturbances or model-plant mismatch. In order to cope with this problem, a `disturbance model' strategy is adopted: the system state is augmented with disturbance variables. Based on the `disturbance model', the disturbances are estimated and the effects of which are cancelled. Experimental validation of the offset-free EOMPC on a linear motor with coulomb friction and cogging disturbances has been implemented and the results show that time-constrained energy-optimal point-to-point motion with high positioning accuracy is achieved.