  This paper discusses the systematic design of an adaptive feedback
linearizing neurocontroller for a high-order model of the synchronous
machine/infinite bus power system. The power system is first modelled as an
input-output nonlinear discrete-time system approximated by two neural
networks. The approach allows a simple linear pole-placement controller (which
is itself not a neural network) to be designed. The control law is specified
such that the controller adaptively calculates an appropriate feedback
linearizing control law at each sampling instant by utilizing plant parameter
estimates provided by the neural system model. The control system also adapts
itself on-line. This avoids the requirement for exact knowledge of the power
system dynamics and full state measurement as well as other difficulties
associated with implementing analytical input-output feedback linearizing
control for a complex power system model. Furthermore, a departure is made from
the `ad hoc' manner in which many neural controllers have been designed for
power systems; the approach used here has foundations in control theoretic
concepts of adaptive feedback linearization and pole-placement control design.
  Simulation results demonstrate the performance of this controller for a
representative example of a single-machine/infinite bus power system
configuration under various operational conditions.
