Direct neural adaptive control applied to synchronous generator

Application of neural networks to control a synchronous generator based on a direct adaptive control scheme is investigated in this paper. Use of a neural network to model the dynamic system is avoided by making use of the sign of the Jacobian of the plant. This substantially reduces the complexity and the computation time of the control algorithm. The controller is trained on-line using the back-propagation algorithm which gives an adaptive attribute to it. Moreover, the controller does not need the state information and only employs the plant outputs. Simulation results are presented to complement the theoretical discussion.