Online Identification and Control of a PV-Supplied DC Motor Using Universal Learning Networks

This paper describes the use of Universal Learning Networks (ULNs) in the speed control of a separately excited DC motor drives fed from Photovoltaic (PV) generators through intermediate power converters. Two ULNs-based identification and control are used. Their free parameters are updated online concurrently by the forward propagation algorithm. The identifier 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 control 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 continuous running mode as well.