Development of a hybrid fuel cell system operated simplified neuro-fuzzy sliding-mode control based IM drive deploying linearization approach: An effort to enhance the performance

Abstract

This article focuses on the design and implementation of a hybrid fuel cell supply system to deliver the power demanded by the feedback linearization (FBL) based induction motor (IM) drive. The proposed modified simple hybrid neuro-fuzzy sliding-mode control (NFSMC) with intuitive FBL substantially reduces torque chattering and improves speed response, giving optimal drive performance under system uncertainties and disturbances. This novel technique also has the benefit of reduced computational burdens by improving computational efficiency over conventional NFSMC and thus suitable for real-time industrial applications. The parameters of the modified neuro-fuzzy control are tuned by an adaptive mechanism based on sliding-mode control. A fuel cell stack with a dc/dc boost converter is considered here as a separate external source during interruption of main supply for maintaining the supply to the motor drive control through the inverter, thereby reducing the burden and average rating of the inverter. A rechargeable battery used as an energy storage supplements the fuel cell during different operating conditions of the drive system. The effectiveness of the proposed method using the fuel cell-based linearized IM drive is investigated in simulation, and the efficacy of the proposed controller is validated by experiment using DSP2812. It is evident from the results that the system provides optimal dynamic performance in terms of remarkably reduced torque ripple, good load disturbance rejection, and reduced speed settling time responses by around 92%, 50%, and 41%, respectively, compared to the PI-controller. The system is also robust in terms of parameter variation and external load.

Key findings

• ▸The proposed method reduces torque chattering and improves speed response.
• ▸The computational efficiency of the proposed method is improved compared to conventional NFSMC.
• ▸The system is suitable for real-time industrial applications due to reduced computational burdens.

Keywords

Identifiers

DOI

10.1063/1.4989796

Journal

Journal of Renewable and Sustainable Energy

Year

2017