Abstract:
In this thesis, an adaptive prediction horizon approach based on machine
learning is presented for the nite control set model predictive control (FCS-
MPC) of power converters. Normally, in FCS-MPC, the prediction horizon
(N) is xed during both the transients and the steady-state. A large N
improves performance while signi cantly increasing the computational cost.
A novel technique is presented where the prediction horizon (N) adapts to
the instantaneous states i.e. il, vo and vo;err = (vref vo). Simulations
are run for di erent combinations of N, il, vo and vref to create a data set
of the optimal N against the instantaneous states and the error between
the reference output voltage and the output voltage and an arti cial neural
network is trained to tell the optimal length of N. The proposed method
is simulated on a boost converter in Simulink. The simulation results show
that the computational cost is reduced without a ecting the performance.
