Power converter control based on neural and fuzzy methods

The new power converter control approaches based on neural network techniques and fuzzy logic theorems are briefly reviewed and discussed in this paper. Current-controlled voltage source inverters offer substantial advantages in improving motor system dynamics for high-performance AC drive systems. The controller switches follow a set of reference current waveforms. Fixed-band and sinusoidal-band hysteresis current controllers have been studied. The first part of this paper develops neural network and fuzzy logic based current-controlled voltage source inverters. The models and learning techniques have been investigated by simulation. The implementation of neural networks is described and simulation results are presented. In the second part of this paper, the new UPS (uninterruptible power supply) with fuzzy logic compensator is proposed. The proposed fuzzy logic compensator is used to prevent voltage drop from nonlinear loads. The total harmonic distortion (THD) of the proposed scheme is better than that of conventional deadbeat control methods for linear and nonlinear loads. The applications of fuzzy and neural network control to DC-DC converters operating at finite switching frequency are studied in the third part of this paper. The fuzzy logic and neural network controller for unity power factor rectifiers, half-bridge DC-DC ZVZCS converters, DC motor drives, induction motor drives and permanent-magnet motor drives are also discussed. Some simulations are presented in this paper.

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