Adaptive neuro‐fuzzy inference systems controller design on Buck converter

Abstract

Abstract Adaptive neuro‐fuzzy inference system (ANFIS) approach is designed for a Buck converter. Because DC–DC converters are under the negative impact of different disturbances, a need for a well‐behaved technique is felt to provide higher robustness in various scenarios, including parametric variations, load uncertainty, supply voltage variation, and noise. Therefore, the fuzzy logic‐based controller is adopted for this structure that provides better error detection and correction, more comprehensive range of operating conditions, and is more readily customizable. However, the fuzzy technique suffers from slow dynamics, lack of reliability against broader range of disturbances, and has a huge computational burden. To overcome the weaknesses addressed before, this technique combined with an artificial neural network (ANN) system that can tune the fuzzy part resulting in an adaptive and robust structure. ANFIS method is a promising approach that has two soft‐computing control structures, including a fuzzy logic‐based part consisting of ANN. This combination has provided many significant benefits over fuzzy logic, such as low computational burden with faster dynamics, higher flexibility with adaptable rules, and a simple structure providing ease of practical implantation; also, it does not need a mathematical moulding of the system since the whole system has been considered as a Black‐box system. To better show the superiority of this method, two other control schemes are designed as fuzzy‐based PID technique and PID controller optimized by PSO algorithm. Finally, the ANFIS control strategy is tested in various working cases through simulation and experiment results as a beneficial alternative for practical applications.