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Processes 2017, 5(1), 8;

Sensitivity-Based Economic NMPC with a Path-Following Approach

Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
Department of Computer Science, Czech Technical University in Prague, 12000 Praha 2, Czech Republic
Author to whom correspondence should be addressed.
Academic Editor: Dominique Bonvin
Received: 26 November 2016 / Revised: 3 February 2017 / Accepted: 13 February 2017 / Published: 27 February 2017
(This article belongs to the Special Issue Real-Time Optimization)
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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. View Full-Text
Keywords: fast economic NMPC; NLP sensitivity; path-following algorithm; nonlinear programming; dynamic optimization fast economic NMPC; NLP sensitivity; path-following algorithm; nonlinear programming; dynamic optimization

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Suwartadi, E.; Kungurtsev, V.; Jäschke, J. Sensitivity-Based Economic NMPC with a Path-Following Approach. Processes 2017, 5, 8.

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