{"title":"Nash-optimization enhanced distributed model predictive control for VAV air conditioning system","authors":"Jianyu Wang, Qinchang Ren","doi":"10.1109/ICSSE.2014.6887935","DOIUrl":null,"url":null,"abstract":"This paper presents an efficient distributed model predictive control scheme based on Nash optimality for a single-duct VAV air conditioning system. An internal model has been built by analyzing the working mechanism and the dynamics of the system and the whole system is decomposed into four sub-systems based on distributed predictive control strategy. MPC solves a constrained convex quadratic Nash optimization by defining weighting factors and constraint limits for each local MPC. Simulation results demonstrate that the performance of the Nash-optimization enhanced distributed MPC is better than that of the fully decentralized MPC, and is close to that of the centralized MPC.","PeriodicalId":166215,"journal":{"name":"2014 IEEE International Conference on System Science and Engineering (ICSSE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on System Science and Engineering (ICSSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSE.2014.6887935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
This paper presents an efficient distributed model predictive control scheme based on Nash optimality for a single-duct VAV air conditioning system. An internal model has been built by analyzing the working mechanism and the dynamics of the system and the whole system is decomposed into four sub-systems based on distributed predictive control strategy. MPC solves a constrained convex quadratic Nash optimization by defining weighting factors and constraint limits for each local MPC. Simulation results demonstrate that the performance of the Nash-optimization enhanced distributed MPC is better than that of the fully decentralized MPC, and is close to that of the centralized MPC.
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