VHDL Neural Control Model on FPGA technology oriented to Sustainable Applications

Abstract

The present work consists of a research on neuronal control schemes and the generalized design of its
components in VHDL hardware descriptor language, with the purpose of building a model for reconfigurable
control support and the optimization of these schemes for implementation with FPGA technology. The
selected method consisted in the modeling of the control, through the VHDL configuration of the neural
networks applied, the study of the models and the proposal of more efficient trainings, oriented to hardware.
Among the results we have a fractal configuration proposal for the efficient control of the systems and
their mathematical model. The research proposes the concept of fractal neural networks, reconfigurable
neuronal control and training systems for hardware as a contribution to address the control of power
systems and reconfiguration of the energy infrastructure. The proposed control scheme simplifies the
implementation of advanced control, promoting an area of research in this line of sustainable design,
incorporation of renewable energies, reuse of resources and energy efficiency.