  In this paper a new approach to control a grid-connected synchronverter by
using a dual heuristic dynamic programing (DHP) design is presented. The
disadvantages of conventional synchronverter controller such as the challenges
to cope with nonlinearity, uncertainties, and non-inductive grids are
discussed.To deal with the aforementioned challenges a neural network based
adaptive critic design is introduced to optimize the associated cost function.
The characteristic of the neural networks facilitates the performance under
uncertainties and unknown parameters (for example different power angles). The
proposed DHP design includes three neural networks: system NN, action NN, and
critic NN. The simulation results compare the performance of the proposed DHP
with a traditional PI-based design and with a neural network predictive
controller. It is shown a well trained DHP design performs in a trajectory,
which is more optimal compared to the other two controllers.
