volume | PIER Journals

Abstract

To address the issues of large speed fluctuations and slow current convergence in traditional model reference adaptive system (MRAS) algorithms, this paper proposes an improved model reference adaptive algorithm based on the fast super-twisting algorithm (FASTA). First, a feedforward compensation term is introduced into the traditional MRAS framework. Additionally, an adaptive feedback gain coefficient is designed, which can be dynamically adjusted in real-time to track speed variations and adapt to different external operating conditions, thereby effectively reducing speed fluctuation amplitude. Furthermore, a fast super-twisting algorithm with a dynamic adjustment exponential gain term is designed and integrated with the model reference adaptive system, replacing the traditional PI controller used in MRAS, significantly improving convergence speed of the system. Finally, experimental results verify the effectiveness and feasibility of the proposed strategy.

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Mr. Feng Yu

Dr. Xiping Liu

Dr. Zhangqi Liu

Dr. Qiang Ge

Dr. Yuxin Liu