Constrained computationally efficient nonlinear predictive control of Solid Oxide Fuel Cell: Tuning, feasibility and performance.

Control of Solid Oxide Fuel Cells (SOFCs) is a challenging task since they are nonlinear dynamic systems and it is essential to precisely satisfy the existing technological constraints which must be imposed on the manipulated variable (the fuel flow) and on fuel utilisation. This paper details a constrained computationally efficient nonlinear Model Predictive Control (MPC) algorithm for the SOFC process. The predicted voltage and fuel utilisation trajectories are successively linearised on-line which leads to a simple to solve quadratic optimisation MPC problem. The emphasis is put on three aspects: (a) selection of tuning parameters, (b) feasibility of the constrained MPC optimisation problem, (c) good control quality and low computational burden. At first, tuning is thoroughly described. It is demonstrated that soft fuel utilisation constraints lead to feasible MPC optimisation. It is shown that control accuracy and constraints' satisfaction ability of the algorithm are very similar to those of the "ideal" MPC strategy with nonlinear on-line optimisation, but its computational burden is much lower. Finally, it is shown that the algorithm is much more precise than the simple MPC algorithm with successive on-line model linearisation and the classical MPC algorithm based on a parameter-constant linear model.