CAJ | 학술논문

为了解决传统滑模观测器的抖振问题，提出了一种用于永磁同步电机的模糊神经滑模观测器。分析了滑模增益对抖振的影响，并采用模糊神经网络动态调整滑模增益以改善抖振，利用李雅普诺夫函数证明了模糊神经网络观测器的稳定性。利用锁相环方法提取转子位置与速度信息，减小由高频噪声引起的误差。仿真结果表明：改进后的滑模观测器能够对永磁同步电机转子位置进行精确辨识，有效地抑制了抖振，实现了高性能的永磁同步电机无传感器控制。
위료해결전통활모관측기적두진문제，제출료일충용우영자동보전궤적모호신경활모관측기。분석료활모증익대두진적영향，병채용모호신경망락동태조정활모증익이개선두진，이용리아보낙부함수증명료모호신경망락관측기적은정성。이용쇄상배방법제취전자위치여속도신식，감소유고빈조성인기적오차。방진결과표명：개진후적활모관측기능구대영자동보전궤전자위치진행정학변식，유효지억제료두진，실현료고성능적영자동보전궤무전감기공제。
In order to solve the chattering problem of conventional sliding mode observer for a permanent magnet synchronous motor (PMSM), a fuzzy-neural network sliding mode observer was proposed. The effect of sliding mode gain to chattering was analyzed. The fuzzy-neural network dynamically adjusts sliding mode gain to reduce the chattering. The stability of the fuzzy-neural network sliding mode observer was proved through Lyapunov function. The method that the phase-locked loop extracts the rotor position and speed information was utilized in order to reduce the error caused by the high-frequency noise. The validity of the proposed algorithm has been demonstrated with experiments.