1. bookVolume 11 (2018): Issue 2 (October 2018)
Journal Details
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Journal
eISSN
1339-3065
First Published
10 Dec 2012
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2 times per year
Languages
English
Open Access

Fast nonlinear model predictive control of a chemical reactor: a random shooting approach

Published Online: 19 Dec 2018
Volume & Issue: Volume 11 (2018) - Issue 2 (October 2018)
Page range: 175 - 181
Journal Details
License
Format
Journal
eISSN
1339-3065
First Published
10 Dec 2012
Publication timeframe
2 times per year
Languages
English
Abstract

This paper presents a fast way of implementing nonlinear model predictive control (NMPC) using the random shooting approach. Instead of calculating the optimal control sequence by solving the NMPC problem as a nonlinear programming (NLP) problem, which is time consuming, a sub-optimal, but feasible, sequence of control inputs is determined randomly. To minimize the induced sub-optimality, numerous random control sequences are selected and the one that yields the smallest cost is selected. By means of a motivating case study we demonstrate that the random shooting-based approach is superior, from a computational point of view, to state-of-the-art NLP solvers, and features a low level of sub-optimality. The case study involves a continuous stirred tank reactor where a fast multi-component chemical reaction takes place.

Keywords

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