Predictive control of solid oxide fuel cell based on an improved Takagi-Sugeno fuzzy model

Thermal management of a solid oxide fuel cell (SOFC) stack essentially involves control of the temperature within a specific range in order to maintain good performance of the stack. In this paper, a nonlinear temperature predictive control algorithm based on an improved Takagi-Sugeon (T-S) fuzzy model is presented. The improved T-S fuzzy model can be identified by the training data and becomes a predictive model. The branch-and-bound method and the greedy algorithm are employed to set a discrete optimization and an initial upper boundary, respectively. Simulation results show the advantages of the model predictive control (MPC) based on the identified and improved T-S fuzzy model for an SOFC stack. (C) 2009 Elsevier B.V. All rights reserved.