{"title":"基于失谐迭代连续循环的多环PI控制","authors":"Shubham Khandelwal, K. Detroja","doi":"10.1109/ANZCC47194.2019.8945741","DOIUrl":null,"url":null,"abstract":"Encountering multivariable systems in process industries is quite common. Along with effectiveness and robustness, simplicity and easy scalability are the utmost requirements expected in a control system design. In this regard, we propose the Detuning Iterative Continuous Cycling (DICC) method for decentralized PI control of multi-input multi-output (MIMO) processes. The proposed DICC design utilizes the idea of continuous cycling for obtaining the ultimate parameters for the effective open-loop transfer functions (EOTFs). While for systems the controller settings are easily derived for the EOTFs, controller tuning for higher dimensional systems is challenging due to complicated EOTF dynamics. Therefore, the effective transfer function (ETF) description of the large scale MIMO system is used for obtaining the ultimate parameters during the closed loop continuous cycling test. Thereafter for obtaining multi-loop PI controller settings, the derived ultimate parameters for the EOTFs/ETFs are subjected to appropriate detuning adjustments. The wide applicability, effectiveness, simplicity and easy scalability of the proposed DICC method has been demonstrated by considering various $2 \\times 2, 3 \\times 3$ and $4 \\times 4$ dimensional MIMO systems. Further, robustness of the proposed design has also been tested by introducing a plant-model mismatch of ± 10% during the closed-loop simulations.","PeriodicalId":322243,"journal":{"name":"2019 Australian & New Zealand Control Conference (ANZCC)","volume":"731 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detuning Iterative Continuous Cycling based Multi-loop PI control for multivariable processes\",\"authors\":\"Shubham Khandelwal, K. Detroja\",\"doi\":\"10.1109/ANZCC47194.2019.8945741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Encountering multivariable systems in process industries is quite common. Along with effectiveness and robustness, simplicity and easy scalability are the utmost requirements expected in a control system design. In this regard, we propose the Detuning Iterative Continuous Cycling (DICC) method for decentralized PI control of multi-input multi-output (MIMO) processes. The proposed DICC design utilizes the idea of continuous cycling for obtaining the ultimate parameters for the effective open-loop transfer functions (EOTFs). While for systems the controller settings are easily derived for the EOTFs, controller tuning for higher dimensional systems is challenging due to complicated EOTF dynamics. Therefore, the effective transfer function (ETF) description of the large scale MIMO system is used for obtaining the ultimate parameters during the closed loop continuous cycling test. Thereafter for obtaining multi-loop PI controller settings, the derived ultimate parameters for the EOTFs/ETFs are subjected to appropriate detuning adjustments. The wide applicability, effectiveness, simplicity and easy scalability of the proposed DICC method has been demonstrated by considering various $2 \\\\times 2, 3 \\\\times 3$ and $4 \\\\times 4$ dimensional MIMO systems. Further, robustness of the proposed design has also been tested by introducing a plant-model mismatch of ± 10% during the closed-loop simulations.\",\"PeriodicalId\":322243,\"journal\":{\"name\":\"2019 Australian & New Zealand Control Conference (ANZCC)\",\"volume\":\"731 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Australian & New Zealand Control Conference (ANZCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANZCC47194.2019.8945741\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Australian & New Zealand Control Conference (ANZCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANZCC47194.2019.8945741","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detuning Iterative Continuous Cycling based Multi-loop PI control for multivariable processes
Encountering multivariable systems in process industries is quite common. Along with effectiveness and robustness, simplicity and easy scalability are the utmost requirements expected in a control system design. In this regard, we propose the Detuning Iterative Continuous Cycling (DICC) method for decentralized PI control of multi-input multi-output (MIMO) processes. The proposed DICC design utilizes the idea of continuous cycling for obtaining the ultimate parameters for the effective open-loop transfer functions (EOTFs). While for systems the controller settings are easily derived for the EOTFs, controller tuning for higher dimensional systems is challenging due to complicated EOTF dynamics. Therefore, the effective transfer function (ETF) description of the large scale MIMO system is used for obtaining the ultimate parameters during the closed loop continuous cycling test. Thereafter for obtaining multi-loop PI controller settings, the derived ultimate parameters for the EOTFs/ETFs are subjected to appropriate detuning adjustments. The wide applicability, effectiveness, simplicity and easy scalability of the proposed DICC method has been demonstrated by considering various $2 \times 2, 3 \times 3$ and $4 \times 4$ dimensional MIMO systems. Further, robustness of the proposed design has also been tested by introducing a plant-model mismatch of ± 10% during the closed-loop simulations.