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, Radlinského 9, 812 37 Bratislava, Slovakia
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: nonlinear model predictive control, random shooting, continuous stirred tank reactor
Full paper in Portable Document Format: acs_0316.pdf
Acta Chimica Slovaca, Vol. 11, No. 2, 2018, pp. 175—181, DOI: 10.2478/acs-2018-0025