  In this paper, a neural network predictive controller (NNPC) is proposed to
control a buck converter. Conventional controllers such as proportional
integral (PI) or proportional integral derivative (PID) are designed based on
the linearized small signal model near the operating point. Therefore, the
performance of the controller in the start up, load change, or reference change
is not optimal since the system model changes by changing the operating point.
The neural network predictive controller optimally controls the buck converter
by following the concept of the traditional model predictive controller. The
advantage of the NNPC is that the neural network system identification
decreases the inaccuracy of the system model with inaccurate parameters. A NNPC
with a well trained neural network can perform as an optimal controller for the
buck converter. To compare the effectiveness of the traditional buck converter
and the NNPC, the simulation results are provided.
