Enhanced State and Unknown Input Estimation for Synchronous Reluctance Motor Using Takagi-Sugeno Fuzzy Proportional Multi-Integral Observer

Abstract

This study introduces a novel approach to enhance state and unknown input estimation for the synchronous reluctance motor, utilizing the Takagi-Sugeno fuzzy representation. A proportional multi-integral observer structure is introduced, capable of capturing a wider range of unknown input dynamics compared to the previous proportional integral observer utilized in prior research. Stability conditions, obtained using the quadratic Lyapunov function, are formulated as linear matrix inequalities to guarantee the asymptotic convergence of the estimation error. To evaluate the effectiveness of the proposed observer, different cases were considered, including scenarios with slow and fast variations of the unknown input. Through hardware-in-the-loop validation, the results clearly demonstrate the superiority of the proposed method over the earlier approach. This advancement signifies a significant improvement in the estimation accuracy and reliability of the Synchronous Reluctance Motor.

Keywords

• Hardware in the loop validation
• synchronous reluctance motor
• proportional multi-integral observer
• Takagi-Sugeno fuzzy systems
• unknown input observer