{"title":"基于神经网络辅助串级控制系统的AHU实时控制","authors":"Chengyi Guo, Q. Song, W. Cai","doi":"10.1109/RAMECH.2004.1438049","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel neural network assisted proportional-plus-integral (PI) control strategy to improve the supply air pressure control performance of variable air volume (VAV) system. The neural network is trained on-line with a normalized training algorithm, which eliminates the requirement of a bounded regression signal to the system. To ensure the convergence of the training algorithm, an adaptive dead-zone scheme is employed. Stability of the proposed control scheme is guaranteed based on the conic sector theory. To demonstrate the applicability of the proposed method, real-time tests were carried out on a pilot VAV air-conditioning system and good experimental results were obtained.","PeriodicalId":252964,"journal":{"name":"IEEE Conference on Robotics, Automation and Mechatronics, 2004.","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Real-time control of AHU based on a neural network assisted cascade control system\",\"authors\":\"Chengyi Guo, Q. Song, W. Cai\",\"doi\":\"10.1109/RAMECH.2004.1438049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a novel neural network assisted proportional-plus-integral (PI) control strategy to improve the supply air pressure control performance of variable air volume (VAV) system. The neural network is trained on-line with a normalized training algorithm, which eliminates the requirement of a bounded regression signal to the system. To ensure the convergence of the training algorithm, an adaptive dead-zone scheme is employed. Stability of the proposed control scheme is guaranteed based on the conic sector theory. To demonstrate the applicability of the proposed method, real-time tests were carried out on a pilot VAV air-conditioning system and good experimental results were obtained.\",\"PeriodicalId\":252964,\"journal\":{\"name\":\"IEEE Conference on Robotics, Automation and Mechatronics, 2004.\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Conference on Robotics, Automation and Mechatronics, 2004.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RAMECH.2004.1438049\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Conference on Robotics, Automation and Mechatronics, 2004.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RAMECH.2004.1438049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-time control of AHU based on a neural network assisted cascade control system
In this paper, we propose a novel neural network assisted proportional-plus-integral (PI) control strategy to improve the supply air pressure control performance of variable air volume (VAV) system. The neural network is trained on-line with a normalized training algorithm, which eliminates the requirement of a bounded regression signal to the system. To ensure the convergence of the training algorithm, an adaptive dead-zone scheme is employed. Stability of the proposed control scheme is guaranteed based on the conic sector theory. To demonstrate the applicability of the proposed method, real-time tests were carried out on a pilot VAV air-conditioning system and good experimental results were obtained.