Institute of Electrical and Electronics Engineers Inc.;New York: IEEE
摘要:
摘要： A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) fault-tolerant control of a six-phase permanent magnet synchronous motor (PMSM) position servo drive is proposed in this study. First, the fault detection and operating decision method of the six-phase PMSM position servo drive is developed. Then, an ideal computed torque controller is designed for the tracking of the rotor position reference command. In general, it is impossible to design an ideal computed control law owing to the uncertainties of the six-phase PMSM position servo drive, which are difficult to know in advance for practical applications. Therefore, the RFNCMAN, which combined the merits of a recurrent fuzzy cerebellar model articulation network and a recurrent fuzzy neural network, is proposed to estimate a nonlinear equation included in the ideal computed control law with a robust compensator designed to compensate the minimum reconstructed error. Furthermore, the adaptive learning algorithm for the online training of the RFNCMAN is derived using the Lyapunov stability to guarantee the closed-loop stability. Finally, the proposed RFNCMAN fault-tolerant control system is implemented in a 32-bit floating-point DSP. The effectiveness of the six-phase PMSM position servo drive using the proposed intelligent fault-tolerant control system is verified by some experimental results. 其他題名： TFUZZ 出版者： New York: IEEE 出版日期： 2016-02 出處： IEEE transactions on fuzzy systems, 2016-02, Vol.24 (1), p.153-167 資源來源： IEL:IEEE/IEE Electronic Library 版權： Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2016 識別號： ISSN: 1063-6706 識別號： EISSN: 1941-0034 識別號： DOI: 10.1109/TFUZZ.2015.2446535 識別號： CODEN: IEFSEV
