This paper presents an adaptive neural network
controller (ANNC) that is used to control the speed of a
separately excited de motor deriving a centrifugal pump load
and fed from photovoltaic (PV) generator through dc-dc buck-boost converter. The controller is also used to track the
maximum power point (MPP) of the PV generator by
controlling the converter duty ratio. Such kind of controllers
must have two objective functions to perform these two tasks,
but in this research the objective function related to the MPP is
converted to a constrained for the second objective function by
making some approximation in the system equations. An
adaptive neural network identifier (ANNI), which emulates the
dynamic behavior of the motor system, plays an important role
in computing the system Jacobian and hence updating the
weights and biases of the ANNC. The weights and biases of both networks are updated on line using BP algorithm with adaptive learning rate. The computation of the adaptive learning rate is based on the value of the speed error through an empirical formula to get faster response with less oscillation and minimum overshoot. The transient response of the motor speed, current and voltage for a step change in the reference speed and the insolation are presented.