NEURAL-NETWORK-BASED ADAPTIVE OBSERVER OF POSITION AND SPEED OF PMSM
作者： GU Shu-sheng
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摘要: A neural network-based nonlinear adaptive observer is designed through a transformation of mathematical model permanent magnet synchronous motor (PMSM) in stationary ba- reference frame. Furthermore, the Lyapunov function created, and on-line learning rules are given for network weight matrix, such that stability proved. Theoretical analysis simulation results show proposed strategy has stronger robustness satisfactory performance.
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