Numerical Integration for Nonlinear Model Predictive Control of a Fuel Cell System

Proceedings of the ... American Control Conference/Proceedings of the American Control Conference, pp. 2633–2639

Abstract

A two-stage nonlinear model predictive controller for the task of power tracking with a fuel cell system is presented. In the first stage, an economic optimization problem is solved for optimal steady states and inputs, which are tracked in the subsequent stage. For the dynamic tracking using MPC, a nonlinear, stiff ordinary differential equation must be discretized. A numerical study of different integration schemes reveals that the first order implicit Runge-Kutta integration scheme is appropriate for discretization. Compared to a high accuracy numerical integration and the standard explicit Runge-Kutta method of order four, the computation time on embedded hardware can be reduced by more than 30% and 10% respectively without loss in closed-loop cost.

Authors 2

1. Lukas Schmitt Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Automatic Control,Aachen,Germany,52074

2. Dirk Abel Aachen Institute of Automatic Control (IRT)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Automatic Control,Aachen,Germany,52074