{"title":"Control of a Thermal Airflow Process - Part II: Adaptive Self-Tuning Control","authors":"Sidney Viana","doi":"10.3895/IJCA.V5N1.5001","DOIUrl":null,"url":null,"abstract":"This article was motivated from a practical work on modeling and control of a time-delayed thermal airflow process using adaptive techniques. The work was divided into two parts: (I) the modeling of the process using system identification methods, with main concerns to the numerical robustness of the identification, and (II) the digital control of the process using adaptive self-tuning control, with main concerns to the adaptation of the controller to changes in the process dynamics. This article concerns the second part of the work. An adaptive self-tuning controller was implemented to improve the performance of the thermal airflow process. At each sampling interval, the process model is updated using an on-line identification strategy developed in the first part of the work. Based on the identification, the controller is self-tuned to compensate for eventual changes in the process parameters. Intentional disturbances were made in the process dynamics in order to evaluate the adaptation performance of the control system.","PeriodicalId":346963,"journal":{"name":"Journal of Applied Instrumentation and Control","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Instrumentation and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3895/IJCA.V5N1.5001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This article was motivated from a practical work on modeling and control of a time-delayed thermal airflow process using adaptive techniques. The work was divided into two parts: (I) the modeling of the process using system identification methods, with main concerns to the numerical robustness of the identification, and (II) the digital control of the process using adaptive self-tuning control, with main concerns to the adaptation of the controller to changes in the process dynamics. This article concerns the second part of the work. An adaptive self-tuning controller was implemented to improve the performance of the thermal airflow process. At each sampling interval, the process model is updated using an on-line identification strategy developed in the first part of the work. Based on the identification, the controller is self-tuned to compensate for eventual changes in the process parameters. Intentional disturbances were made in the process dynamics in order to evaluate the adaptation performance of the control system.