Wiener structures for modeling and nonlinear predictive control of proton exchange membrane fuel cell

Abstract

The proton exchange membrane (PEM) fuel cell is a nonlinear dynamic system which cannot be precisely described and controlled using a linear model. This work has two objectives: (a) it discusses model selection for the PEM and (b) it develops two nonlinear computationally efficient model predictive control (MPC) algorithms for the PEM. Three Wiener model types of different orders of dynamics and complexity of the nonlinear steady-state block are compared. The model consisting of three dynamic blocks and a neural network with five hidden nodes is chosen. To obtain simple MPC quadratic optimization problems, a linear approximation of the model or a linear approximation of the predicted trajectory is repeatedly found. The first MPC scheme gives very good control accuracy, whereas the second MPC scheme leads to the same trajectories as those possible in the “ideal” MPC scheme with full online nonlinear optimization.