{"title":"基于快速梯度法和增广拉格朗日乘子的线性定常系统的快速预测控制","authors":"M. Kögel, R. Findeisen","doi":"10.1109/CCA.2011.6044410","DOIUrl":null,"url":null,"abstract":"We present an algorithm based on the fast gradient method and augmented Lagrange multipliers for model predictive control of linear, discrete-time, time-invariant, systems with constraints. In particular, the algorithm solves the underlying quadratic program in the so-called condensed form and takes advantage of the problem structure. At the end, we illustrate the performance of the algorithm, which is competitive with tailored interior-point methods, by an example.","PeriodicalId":208713,"journal":{"name":"2011 IEEE International Conference on Control Applications (CCA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Fast predictive control of linear, time-invariant systems using an algorithm based on the fast gradient method and augmented Lagrange multipliers\",\"authors\":\"M. Kögel, R. Findeisen\",\"doi\":\"10.1109/CCA.2011.6044410\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present an algorithm based on the fast gradient method and augmented Lagrange multipliers for model predictive control of linear, discrete-time, time-invariant, systems with constraints. In particular, the algorithm solves the underlying quadratic program in the so-called condensed form and takes advantage of the problem structure. At the end, we illustrate the performance of the algorithm, which is competitive with tailored interior-point methods, by an example.\",\"PeriodicalId\":208713,\"journal\":{\"name\":\"2011 IEEE International Conference on Control Applications (CCA)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE International Conference on Control Applications (CCA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCA.2011.6044410\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Control Applications (CCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCA.2011.6044410","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fast predictive control of linear, time-invariant systems using an algorithm based on the fast gradient method and augmented Lagrange multipliers
We present an algorithm based on the fast gradient method and augmented Lagrange multipliers for model predictive control of linear, discrete-time, time-invariant, systems with constraints. In particular, the algorithm solves the underlying quadratic program in the so-called condensed form and takes advantage of the problem structure. At the end, we illustrate the performance of the algorithm, which is competitive with tailored interior-point methods, by an example.