Enhancing Boost Converter Performance: A Comparison Between Multi-Model Predictive Control and Nonlinear Hammerstein Control

In this paper, we present a comparative study of the multi-model approach and the nonlinear approach for predictive control of a Boost converter. The Dynamics of the process is first described by a collection of models. The optimal number of models is selected through the Elbow Method, enhanced by Particle Swarm Optimization (PSO) to improve partitioning efficiency. A genetic algorithm is used to refine the resulting models using the adequate validity. In contrast, predictive control combined with the Newton-Raphson method is applied to the Hammerstein model. Both control strategies are implemented on an STM32 microcontroller to enable real-time control of the Boost converter. The efficiency of the developed control strategies is analyzed and compared with respect to tracking precision, robustness, and execution time, demonstrating their suitability for power electronics applications. DOI: 10.61416/ceai.v28i1.9644