Supervisory predictive control and on-line set-point optimization
The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm...
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| Veröffentlicht in: | International Journal of Applied Mathematics and Computer Science Jg. 20; H. 3; S. 483 - 495 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Zielona Góra
Versita
01.09.2010
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
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| ISSN: | 1641-876X, 2083-8492 |
| Online-Zugang: | Volltext |
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| Abstract | The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers. |
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| AbstractList | The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers. Supervisory predictive control and on-line set-point optimization The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers. |
| Author | Tatjewski, Piotr |
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| CitedBy_id | crossref_primary_10_1002_asjc_703 crossref_primary_10_1016_j_mineng_2011_08_003 crossref_primary_10_1109_TVT_2017_2745101 crossref_primary_10_1109_TCSVT_2013_2270400 crossref_primary_10_1016_j_compchemeng_2020_107065 crossref_primary_10_1515_amcs_2017_0021 crossref_primary_10_1016_j_compchemeng_2017_04_001 crossref_primary_10_1016_j_conengprac_2021_104859 crossref_primary_10_1109_TII_2020_3005207 crossref_primary_10_3390_a13040097 crossref_primary_10_1016_j_isatra_2014_09_018 crossref_primary_10_1515_acsc_2017_0035 |
| Cites_doi | 10.1002/aic.690450611 10.1007/978-1-4471-0113-0 10.1142/p372 10.1016/j.arcontrol.2008.03.003 10.1016/S0005-1098(01)00174-1 10.1016/S0967-0661(02)00186-7 10.1109/TAC.2004.838489 10.1016/S0005-1098(96)00255-5 10.1007/978-1-4471-3398-8 10.2478/v10006-010-001-y 10.1016/S0005-1098(02)00250-9 10.1002/rnc.889 10.1016/S0967-0661(02)00033-3 10.1115/1.3645868 10.1016/j.compchemeng.2003.08.002 10.1016/S0005-1098(99)00214-9 10.1021/ie034308l 10.1109/9.661611 10.1007/BFb0109870 10.2478/v10006-008-0047-2 10.2478/v10006/009-0012-8 10.1109/9.704989 10.1002/aic.690460513 10.1109/TAC.2002.808470 10.1016/S0959-1524(00)00023-8 10.1080/00207727908941614 |
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