Leo Emil Sokoler, G. Frison, Kristian Edlund, Anders Skajaa, J. B. Jørgensen
{"title":"A riccati based homogeneous and self-dual interior-point method for linear economic model predictive control","authors":"Leo Emil Sokoler, G. Frison, Kristian Edlund, Anders Skajaa, J. B. Jørgensen","doi":"10.1109/CCA.2013.6662814","DOIUrl":null,"url":null,"abstract":"In this paper, we develop an efficient interior-point method (IPM) for the linear programs arising in economic model predictive control of linear systems. The novelty of our algorithm is that it combines a homogeneous and self-dual model, and a specialized Riccati iteration procedure. We test the algorithm in a conceptual study of power systems management. Simulations show that in comparison to state of the art software implementation of IPMs, our method is significantly faster and scales in a favourable way.","PeriodicalId":379739,"journal":{"name":"2013 IEEE International Conference on Control Applications (CCA)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Control Applications (CCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.2013.6662814","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
In this paper, we develop an efficient interior-point method (IPM) for the linear programs arising in economic model predictive control of linear systems. The novelty of our algorithm is that it combines a homogeneous and self-dual model, and a specialized Riccati iteration procedure. We test the algorithm in a conceptual study of power systems management. Simulations show that in comparison to state of the art software implementation of IPMs, our method is significantly faster and scales in a favourable way.