For reliable service, a power system must remain stable and capable of withstanding a wide range of disturbances especially for the large interconnected systems. In the last decade and a half and in particular after the famous blackout in N.Y. U.S.A. 1965, considerable research effort has gone in to the stability investigation of power systems. To deal with the requirements of real power systems, various stabilizing control techniques were being developed over the last decade. Conventional control engineering approaches are unable to effectively deal with system complexity, nonlinearities, parameters variations and uncertainties. This dissertation presents a non-linear control technique which relies on prediction of the large power system behaviour. One example of a large modern power system formed by interconnecting the power systems of various states is the South-Eastern Australian power network made up of the power systems of Queensland, New South Wales, Victoria and South Australia. The Model Predictive Control (MPC) for the total power system has been shown to be successful in addressing many large scale nonlinear control problems. However, for application to the high order problems of power systems and given the fast control response required, total MPC is still expensive and is structured for centralized control. This thesis develops a MPC algorithm to control the field currents of generators incorporating them in a decentralized overall control scheme. MPC decisions are based on optimizing the control action in accordance with the predictions of an identified power system model so that the desired response is obtained. Energy Function based design provides good control for direct influence items such as SVC (Static Var Compensators), FACTS (Flexible AC Transmission System) or series compensators and can be used to define the desired flux for generator. The approach in this thesis is to use the design flux for best system control as a reference for MPC. Given even a simple model of the relation between input control signal and the resulting machine flux, the MPC can be used to find the control sequence which will start the correct tracking. The continual recalculation of short time optimal control and then using only the initial control value provides a form of feedback control for the system in the desired tracking task but in a manner which retains the nonlinearity of the model.