Neural network controllers for switch mode systems: off line training by an 'ideal controller' data set

A novel method is proposed for designing Neural Network Controllers (NNC) for switch mode systems. The method applies a new concept: 'The Ideal Controller' which is run 'off-line' to generate a record of 'perfect' control signals in response to input and output perturbations. The record is then used as an 'off-line' training set for a Neural Network controller. The advantages of the proposed method are three fold: (a) the training set is the best possible, (b) training is done 'off-line' by simulation and (c) there is no need to derive or guess the control law. The present study demonstrates by simulation the potential excellent performance of a Neural Network Controllers for DC-DC Switch-Mode converters when trained by the proposed methodology. The proposed methodology can be readily expanded to other Switch-Mode systems such as inverters and power factor conditioners.