{"title":"关于显式MPC复杂性的观察","authors":"F. Stoican, S. Mihai, B. Ciubotaru","doi":"10.1109/CDC45484.2021.9683256","DOIUrl":null,"url":null,"abstract":"This paper analyzes the structure of the constrained optimization problem induced by a typical Model Predictive Control (MPC) problem. The main idea is to exploit the particularities of the feasible domain (namely, that input/state/output constraints describe in fact zonotopic sets) to: i) efficiently describe the solution as a piecewise affine function with polyhedral support; ii) exploit the combinatorial properties of zonotopes to reduce the number of candidate active sets. The results are tested over a numerical example.","PeriodicalId":229089,"journal":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","volume":"418 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Observations on the complexity of the explicit MPC\",\"authors\":\"F. Stoican, S. Mihai, B. Ciubotaru\",\"doi\":\"10.1109/CDC45484.2021.9683256\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper analyzes the structure of the constrained optimization problem induced by a typical Model Predictive Control (MPC) problem. The main idea is to exploit the particularities of the feasible domain (namely, that input/state/output constraints describe in fact zonotopic sets) to: i) efficiently describe the solution as a piecewise affine function with polyhedral support; ii) exploit the combinatorial properties of zonotopes to reduce the number of candidate active sets. The results are tested over a numerical example.\",\"PeriodicalId\":229089,\"journal\":{\"name\":\"2021 60th IEEE Conference on Decision and Control (CDC)\",\"volume\":\"418 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 60th IEEE Conference on Decision and Control (CDC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC45484.2021.9683256\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 60th IEEE Conference on Decision and Control (CDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC45484.2021.9683256","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Observations on the complexity of the explicit MPC
This paper analyzes the structure of the constrained optimization problem induced by a typical Model Predictive Control (MPC) problem. The main idea is to exploit the particularities of the feasible domain (namely, that input/state/output constraints describe in fact zonotopic sets) to: i) efficiently describe the solution as a piecewise affine function with polyhedral support; ii) exploit the combinatorial properties of zonotopes to reduce the number of candidate active sets. The results are tested over a numerical example.