Solid oxide fuel cells (SOFCs) are high temperature fuel cells with a strong potential for stationary power house applications. However, considerable challenges need to be overcome to connect these cells to the power grid. The fluctuating grid demand has to be met without sacrificing the cell efficiency and causing structural/material damage to the system. This requirement coupled with fast and highly nonlinear transients of the transport variables results in a challenging control problem. This paper is on synthesizing a controller that can address some of these challenges. For using in the model predictive controller (MPC), input output models are identified from the data generated by a detailed dynamic model. A traditional SISO control and a novel MIMO control are considered here. In the SISO control problem, power is the controlled variable (CV) and H-2 flow is the manipulated variable (MV). In the MIMO control problem, power and the utilization factor (UF) of the fuel are the CVs while voltage and the flow of H-2 are the MVs. The identification study shows that the nonlinear NAARX models with properly chosen cross terms can improve the model performance significantly in a MIMO problem. The results from the control study indicate that a well-tuned proportional integral derivative (PM) controller is sufficient for the single input single output (SISO) power control of a tubular SOFC. It also shows that the mutiple input multiple output (MIMO) control of power and the UF is highly interactive and necessitates a nonlinear model predictive controller (NMPC). Without using any additional hardware such as an ultracapacitor or battery pack, the designed NMPC could satisfy a step change in load with acceptable overshoot in power and the UF. A well-tuned PID controller is found to perform poorly for the MIMO problem. On the basis of these findings, future work will focus on the development of nonlinear predictive control approaches for stack-level control of tubular solid oxide fuel cells.