This paper describes the use of Universa
l Learning Networks (ULNs) in the speed
control of a separately excited DC mo
tor drives fed from Photovoltaic (PV)
generators through intermediate power c
onverters. Two ULNs-based identification
and control are used. Their free parameters
are updated online concurrently by the
forward propagation algorithm. The identif
ier network is used to capture and
emulate the nonlinear mappings between the inputs and outputs of the motor
system. The controller network is used to c
ontrol the converter duty ratio so that the
motor speed can follow an arbitrarily reference signal. Moreover the overall system
can operate at the Maximum Power Point (MPP) of the PV source. The simulation
results showed a good performance for the
controller and the identifier during the
training mode and the conti
nuous running mode as well. |
