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 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.

Agile manoeuvres using model predictive control

“…In the subsequent sections, two principal ways allowing (3) to be solved quickly are reviewed. The fi rst method to solve a nonlinear MPC problem of (3) quickly is the so-called random shooting method, introduced in more details in Bakaráč and Kvasnica (2018). In principle, the method investigates a (possibly large) number of randomly generated control sequences {u 0 , …, u N-1 }.…”