Prediction error compensation method of FCSMPC for converter based on neural network

Abstract

FCSMPC is a classical converter predictive control algorithm whose control performance is affected by the prediction error of the prediction model. In classical predictive control theory, the feedback correction mechanism is used to compensate for such prediction error. However, when this strategy is directly applied to the FCSMPC algorithm, the prediction error cannot be easily calculated. To address the prediction error compensation problem of FCSMPC, this paper proposes a prediction error compensation method based on neural network. A neural network prediction model is also constructed based on the timing characteristics of prediction error signals. The prediction error of this prediction model at the next moment is calculated by the designed neural network model, and then the output of the prediction model is compensated at the current moment. To improve the anti-interference performance of FCSMPC, the MRSVD algorithm is used to filter the prediction error sample data and the neural networks are trained by these sample data. The adaptability of the prediction error calculation is further improved by combining offline training with the online calculation of the neural network. A simulation model of the proposed method is then constructed using MATLAB, and simulation results show that the control performance of the FCSMPC algorithm is improved and that the effectiveness and feasibility of the proposed method are verified.