A Supervised Adaptive Neuro-Fuzzy Inference System controller for a Hybrid Electric Vehicle's power train system

This paper presents a study on the implementation of a Supervised Adaptive Neuro-Fuzzy Inference System (S-ANFIS) controller for a permanent magnet synchronous motor applied to the power train system of a Hybrid Electric Vehicle (HEV). An ANFIS model implementation aims to optimize the parameters of a fuzzy system through a learning algorithm and a set of inputs and outputs, which are responsible for the learning process. The comparative study presented in this research work, focuses on an evaluation between a conventional and a S-ANFIS controller based on their performance, complexity, response-time, accuracy and efficiency for the power train system of a HEV. Also, it is demonstrated the importance and benefits of using artificial intelligence in control techniques for power train systems control. The comparative results are analyzed, discusses and based on them further research work has been defined.