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

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...

Full description

Saved in:
Bibliographic Details
Published inActa Chimica Slovaca Vol. 11; no. 2; pp. 175 - 181
Main Authors Bakaráč, Peter, Kvasnica, Michal
Format Journal Article
LanguageEnglish
Published 01.10.2018
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:1337-978X
1337-978X
DOI:10.2478/acs-2018-0025