Sensitivity-Based Economic NMPC with a Path-Following Approach
Autor: | Vyacheslav Kungurtsev, Johannes Jäschke, Eka Suwartadi |
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Jazyk: | angličtina |
Rok vydání: | 2017 |
Předmět: |
0209 industrial biotechnology
Mathematical optimization NLP sensitivity Bioengineering 02 engineering and technology lcsh:Chemical technology Nonlinear programming fast economic NMPC lcsh:Chemistry 020901 industrial engineering & automation Quadratic equation 020401 chemical engineering Fractionating column Control theory nonlinear programming dynamic optimization Chemical Engineering (miscellaneous) lcsh:TP1-1185 Sensitivity (control systems) 0204 chemical engineering TRACE (psycholinguistics) Mathematics Sequence Process Chemistry and Technology path-following algorithm Function (mathematics) Model predictive control lcsh:QD1-999 |
Zdroj: | Processes Processes; Volume 5; Issue 1; Pages: 8 Processes, Vol 5, Iss 1, p 8 (2017) |
Popis: | We present a sensitivity-based predictor-corrector path-following algorithm for fast nonlinear model predictive control (NMPC) and demonstrate it on a large case study with an economic cost function. The path-following method is applied within the advanced-step NMPC framework to obtain fast and accurate approximate solutions of the NMPC problem. In our approach, we solve a sequence of quadratic programs to trace the optimal NMPC solution along a parameter change. A distinguishing feature of the path-following algorithm in this paper is that the strongly-active inequality constraints are included as equality constraints in the quadratic programs, while the weakly-active constraints are left as inequalities. This leads to close tracking of the optimal solution. The approach is applied to an economic NMPC case study consisting of a process with a reactor, a distillation column and a recycler. We compare the path-following NMPC solution with an ideal NMPC solution, which is obtained by solving the full nonlinear programming problem. Our simulations show that the proposed algorithm effectively traces the exact solution. (c) 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
Databáze: | OpenAIRE |
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