An improved dead-beat rectifier regulator using a neural net predictor

This paper proposes a new input current reference prediction scheme for the deadbeat control of a three-phase rectifier used in AC/DC/AC power converters. The inherent lag in the deadbeat control is compensated by using reference prediction resulting in better performance. The proposed predictor consists of a neural net which is trained online and predicts the slow varying and periodic trends of the current reference and a linear first order predictor which predicts the fast variations of the current reference time signal. A CRITIC decides if the neural net training is sufficient and therefore whether or not to use the prediction in the control loop. The learning rule used allows neural net weights to be trained whenever a parameter change causes an increased prediction error. This predictive-regulator is shown to result in improved performance in steady state, in the presence of input voltage imbalance or load variations over the normal deadbeat regulator