Predictive control for sector bounded nonlinear model and its application to solid oxide fuel cell systems

In this paper, a nonlinear model predictive control method is presented for solid oxide fuel cell systems. For realistic modeling, a sector bounded nonlinear model with input constraint is considered. As a performance index, we consider one horizon cost function that is represented by the weighted sum of state, control input and nonlinear function. By minimizing the upper bound of the cost function, the optimized control input sequences are obtained. For cost monotonicity, a terminal inequality condition is derived in terms of a finite number of linear matrix inequalities (LMIs) by using augmented vector feedback law which consists of state and sector bounded nonlinear function. In order to show the effectiveness of the proposed method, the sector bounded nonlinear model is derived for the solid oxide fuel cell systems and the system performance is verified by applying the proposed MPC algorithm to the systems.