ANN-enhanced deadbeat predictive current controller for robust voltage source inverter performance

This paper presents an advanced predictive current control strategy for voltage source inverters (VSIs) based on an artificial neural network (ANN)-enhanced deadbeat (DB) controller. Unlike conventional DB controllers, whose performance strongly depends on accurate knowledge of the filter resistance and inductance, the proposed ANN-DB controller executes in real time without requiring explicit system parameters or analytical models, relying solely on measured currents and voltages. The ANN is trained offline using data generated from a traditional DB current controller over a wide range of operating conditions, and then deployed as a data-driven approximation of the deadbeat control law. Experimental results on a SEMIKRON two-level VSI with an RL load, implemented on a dSPACEMicroLabBox platform, demonstrate that the ANN-DB controller achieves faster transient response (settling time of approximately 1.4 ms), reduced current total harmonic distortion (THD ≈ 1.9%), and improved steady-state tracking accuracy compared with the conventional DB approach under varying load conditions. Robustness tests with inductance variations from 0.09 H to 0.18 H confirm that the ANN-DB controller maintains low RMS current error and low THD, whereas the conventional DB controller exhibits pronounced sensitivity to parameter deviations. A comparative discussion with PI, model predictive control (MPC), classical DB, and recent model-free predictive current control methods highlights that the proposed ANN-DB scheme offers a favourable compromise between dynamic performance, robustness, and computational complexity for advanced power electronic applications.