Intelligent Control Strategies for DC-DC Boost Converter: Performance Analysis and Optimization

This paper investigates the performance of different control algorithms for boost converters, focusing on the Adaptive Neuro-Fuzzy Inference System (ANFIS)-based boost converter control, optimizing the Proportional-Integral (PI) controller using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), and exploring the performance of Artificial Neural Net-work (ANN) control. Comparative analysis reveals that both the ANFIS controller and the optimized PI controller with PSO and GA optimization achieve precise output voltage regulation. Ad-ditionally, the ANFIS control method demonstrates superior step response characteristics in both fixed and varying input voltage scenarios. The results offer valuable insights about performance characteristics of boost converter control strategies emphasizing on optimization of PI controller while also highlighting ANFIS's superior ability to achieve optimal step response characteristics.