Artificial neural network-based predictive control for three-phase inverter systems with RLC filters - Iftissen Nabil; Ali Dali; Samir Abdelmalek (Institute of Advanced Engineering and Science, 2025)
Model predictive control (MPC) is becoming more and more popular in power electronics applications, yet its practical implementation faces challenges due to computational complexity and resource demands. To address these issues, a novel MPC control approach using an artificial neural network (ANN-MPC) is put forth in this research. Using a real-time circuit modeling environment, a power converter with a virtual MPC controller that can regulate both linear and nonlinear loads is first created and run. The input-output data gathered from the virtual MPC is then used to train an artificial neural network (ANN) offline, enabling a simplified mathematical representation that significantly reduces computational complexity. Moreover, the ANN-MPC controller’s adaptability to input variations enhances robustness against system uncertainties. We offer a thorough explanation of the ANN-MPC's fundamental idea, ANN architecture, offline training approach, and online functioning. The suggested controller is validated by simulation with MATLAB/Simulink tools. Performance evaluation of the novel MPC-ANN controller is performed across various scenarios, including linear and nonlinear loads under various operational conditions, and a comparative analysis with conventional MPC is presented.