Generalized predictive control for fractional order dynamic model of solid oxide fuel cell output power

Abstract

An adaptive generalized predictive control (GPC) system is presented for the management of output power of solid oxide fuel cells (SOFCs). The dynamics of SOFC output power are characterized by a fractional order model, which is more accurate than an integer order model to depict the dynamics; the fractional order dynamic model is taken as the controlled plant of the GPC system. The GPC algorithm adopts a linear approximation method that uses a linear predictive model to approximate locally and dynamically the nonlinear dynamics of SOFC output power at each sampling period. Moreover, the parameters of the predictive model are identified online to overcome the time-varying dynamics of SOFC output power via introducing a forgetting factor recursive least squares (FFRLS) algorithm. Finally, according to the future power outputs predicted by the predictive model, an optimal current control sequence is obtained by solving a multistage cost function. The results demonstrate that the dynamic responses of the GPC system are quick and smooth, and the change of the current control sequence is slow and smooth. The quick and smooth dynamics are important for satisfying the rapid load following of SOFC generating systems and for prolonging the lifetime of SOFC stack.