Direct adaptive regulation using dynamic neural networks: Application to dc motors speed control Original Research Article

A direct nonlinear adaptive state regulator is derived, based on dynamic neural networks, and it is successfully applied to control the speed of a nonlinearized dc motor. One interesting feature of the proposed control algorithm is that it covers the situation where the magnetic flux continuously varies, as it is the case in the loss minimization problem.