Application of mixed integer nonlinear programming for system identification
This work describes a method of deadtime approximation in dynamic systems, particularly in the context of nonlinear model predictive control based on mechanistic models where the differentiability of the equations must be ensured. The resulting system identification system is solved using the BBMCSF...
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| creator | Fernandes, Natércia C. P. Fernandes, Florbela P. Romanenko, Andrey |
| description | This work describes a method of deadtime approximation in dynamic systems, particularly in the context of nonlinear model predictive control based on mechanistic models where the differentiability of the equations must be ensured. The resulting system identification system is solved using the BBMCSFilter (Branch and Bound based on a Multistart Coordinate Search Filter) global optimization algorithm to determine the order and the parameters of the resulting model, taking into account not only the model-plant mismatch but also the model complexity and the resulting computation time. The application of the method is illustrated with a simulated example of a chemical process unit. |
| doi_str_mv | 10.1063/5.0026410 |
| format | Conference Proceeding |
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| fulltext | fulltext |
| identifier | ISSN: 0094-243X |
| ispartof | AIP conference proceedings, 2020, Vol.2293 (1) |
| issn | 0094-243X 1551-7616 |
| language | eng |
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| source | AIP Journals Complete |
| subjects | Algorithms Approximation Global optimization Mixed integer Nonlinear control Nonlinear programming Predictive control System identification |
| title | Application of mixed integer nonlinear programming for system identification |
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