<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Department of Information Engineering and Process Control Institute of Information Engineering, Automation, and Mathematics FCFT STU in Bratislava

Acta Chimica Slovaca

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This paper presents a fast way of implementing nonlinear model predictive control (NMPC) using the random shooting approach. Instead of calculating the optimal...

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

Online veröffentlicht: 19. Dez. 2018

Seitenbereich: 175 - 181

DOI: https://doi.org/10.2478/acs-2018-0025

Schlüsselwörter
nonlinear model predictive control, random shooting, continuous stirred tank reactor

© 2018 Peter Bakaráč et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 19. Dez. 2018

Seitenbereich: 175 - 181

DOI: https://doi.org/10.2478/acs-2018-0025

Schlüsselwörternonlinear model predictive control, random shooting, continuous stirred tank reactor

© 2018 Peter Bakaráč et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
nonlinear model predictive control, random shooting, continuous stirred tank reactor