Improved SMPS modeling for photovoltaic applications by a novel neural paradigm with Hamiltonian-based training algorithm

This paper discuss as the dynamics of a SMPS can be investigated by recurrent neural network (RNN) based models with an Hamiltonian formulation and function used for the training, so leading to a novel paradigm that we call RNNHT model. By using the calculated state variables in a boost converter a RNN is trained by considering also the minimization of the energy stored according to a defined cost function. Simulation results show the improvements in the dynamic performance output prediction versus some well assessed boost converter models in the recent literature.