Fast sensitivity-based economic model predictive control for degenerate systems
| Autor: | Vyacheslav Kungurtsev, Eka Suwartadi, Johannes Jäschke |
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| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Optimization problem Linear programming Computer science Solver System of linear equations Industrial and Manufacturing Engineering Computer Science Applications Nonlinear programming Model predictive control Control and Systems Engineering Modeling and Simulation Linear independence Quadratic programming |
| Zdroj: | Journal of Process Control. 88:54-62 |
| ISSN: | 0959-1524 |
| DOI: | 10.1016/j.jprocont.2020.02.006 |
| Popis: | We present a sensitivity-based nonlinear model predictive control (NMPC) algorithm and demonstrate it on a case study with an economic cost function. In contrast to existing sensitivity-based approaches that make strong assumptions on the underlying optimization problem (e.g. the linear independence constraint qualification implying unique multiplier), our method is designed to handle problems satisfying a weaker constraint qualification, namely the Mangasarian-Fromovitz constraint qualification (MFCQ). Our nonlinear programming (NLP) sensitivity update consists of three steps. The first step is a corrector step in which a system of linear equations is solved. Then a predictor step is computed by a quadratic program (QP). Finally, a linear program (LP) is solved to select the multipliers that give the correct sensitivity information. A path-following scheme containing these steps is embedded in the advanced-step NMPC (asNMPC) framework. We demonstrate our method on a large-scale case example consisting of a reactor and distillation process. We show that LICQ does not hold and the path-following method is able to accurately approximate the ideal solutions generated by an NLP solver. |
| Databáze: | OpenAIRE |
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