Transients in a load have a significant impact on the performance and durability of a solid oxide fuel cell (SOFC) integrated into a micro gas turbine (MGT) hybrid power system. One of the main reasons is that the SOFC operating temperature and turbine inlet temperature change drastically due to the load change. Therefore, in order to guarantee the temperature to operate within a specified range, an adaptive proportional-integral-derivative (PID) decoupling control strategy based on a dynamic radial basis function (RBF) neural network is presented to control the temperature of a natural gas fueled, tubular SOFC/MGT hybrid with internal reforming in this paper. Using the self-learning ability of the dynamic RBF neural network, the proportional, integral, and differential factor of the PID controller are tuned on-line. The simulation results show that it is feasible to build the adaptive PID decoupling controller for temperature control of the SOFC/MGT hybrid system.