Robust control of a PV-supplied DC motor using higher order derivatives of ULNs

In this paper, a new robust control method and its application to a photovoltaic (PV) supplied, separately-excited DC motor with a constant load are 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 scheme, which is considered as an extended version of back propagation through time (BPTT) and real time recurrent learning (RTRL). In this application, two ULNs are used: the first is the universal learning network identifier (ULNI) trained offline to emulate the dynamic performance of the DC motor system. The second is the universal learning network controller (ULNC), 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 (MPP) of the PV source. To investigate the effectiveness of the proposed robust control method, the simulation is carried out at different values of the robustness coefficients in two different phases: The training phase, in which the simulation is done for a constant load torque. And the control phase, in which the controller performance is obtained when the load torque is changing. 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 those at the training stage.