Adaptive voltage controller based on extreme learning machine for DC-DC boost converter

Abstract

This study presents an adaptive voltage controller for a DC-DC boost converter using the extreme learning machine (ELM) algorithm to address the limitations of conventional control techniques under varying load and reference voltage conditions. The ELM is implemented to predict the optimal parameters of a PI controller (Kp and Ki), enabling real-time adaptability of the system. Simulation results in MATLAB/Simulink demonstrate that the proposed ELM-based proportional-integral controller (PI-ELM) outperforms both traditional PI controllers and those optimized using metaheuristic algorithms. Specifically, the controller achieved a maximum absolute error of only 0.0185 for Kp and 0.0294 for Ki across a range of operating conditions, with corresponding mean squared errors (MSE) of 0.01861 and 0.02798, respectively. These findings confirm the effectiveness of the ELM in enhancing the dynamic response and robustness of boost converter voltage regulation systems.