In this paper, a new robust control method and its application to a photovoltaic (PV) supplied, separately excited
DC motor loaded with a constant torque is discussed. The robust controller is designed against the load torque changes
by using the first and second ordered derivatives of the universal learning networks (ULNs). These derivatives are
calculated using the forward propagation algorithm, which is considered as an extended version of real time recurrent
learning (RTRL). In this application, two ULNs are used: The first is the ULN identifier trained offline to emulate the
dynamic performance of the DC motor system. The second is the ULN controller, which is trained online not only to
make the motor speed follow a selected reference signal, but also to make the overall system operate at the maximum
power point of the PV source. To investigate the effectiveness of the proposed robust control method, the simulation is
carried out at four different values of the robustness coefficient
in two different stages: The training stage, in which the
simulation is done for a constant load torque. And the control stage, in which the controller performance is obtained
when the load torque is changed. The simulation results showed that the robustness of the control system is improved
although the motor load torque at the control stage is different from that at the training stage. |
