Neural Network Based Predictive Current Controllers for Three Phase Inverter

Abstract

With Predictive Current Controllers, system behavior or current reference is predicted. In this way, it is aimed to prevent hardware and software related delays. In this study, new Artificial Neural Network (ANN) based Predictive Current Controllers are designed using four different methods for voltage source inverters. The training of the networks is done offline using the data obtained from the simulation results for different parameters in the Matlab environment. In the first proposed method, a static ANN based current controller is designed and trained using the data obtained from the Finite Control Set Model Predictive Control (FCS-MPC) method. Then, a feed-forward reference current predictor ANN (PRefNN) is designed to make sinusoidal reference current prediction in the other three methods. The other proposed predictive ANN methods are trained by taking the data offline from the inverter system in which PRefNN and the classical current controllers (Hysteresis, PR, and PI) are used. In this way, three different predictive current controllers named as Hysteresis based predictive ANN (Hist-PNN), PR based predictive ANN (PR-PNN), and PI based predictive ANN (PI-PNN) are designed. Classical current control methods have been given predictive properties with these three different network structures. And also, it is improved the performance of classical methods against parameter changes and noises. A three phase 5kVA inverter circuit with a 7MBP50RJ120 IPM module in the power stage and STM32f407 as a controller is designed for the experimental setup. The methods are tested in simulation and validated in the experimental setup.

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

“…Some authors proposed studying analog circuits behaving as 'black boxes' by ANNs, analyzing signals only from inputs and outputs of the circuits [14]. ANNs are also adopted to predict current signals [15], and to classify noise signals [16] by filtering them from undesired disturbances or interferences [17]. ANNs are also applied to predict the behavior of electronic circuits improving circuit design [18,19] and for high electron mobility transistors (HEMTs) amplifier design [20].…”