Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs

Faa Jeng Lin, Rong Jong Wai, Chun Ming Hong

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


A novel designed hybrid supervisory control system using a recurrent fuzzy neural network (RFNN) is proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo drive for the tracking of periodic reference inputs in this study. First, the field-oriented mechanism is applied to formulate the dynamic equation of the PMLSM. Then, a hybrid supervisory control system, which combines a supervisory control system and an intelligent control system, is proposed to control the mover of the PMLSM for periodic motion. The supervisory control law is designed based on the uncertainty bounds of the controlled system to stabilize the system states around a predefined bound region. Since the supervisory control law will induce excessive and chattering control effort, the intelligent control system is introduced to smooth and reduce the control effort when the system states are inside the predefined bound region. In the intelligent control system, the RFNN control is the main tracking controller which is used to mimic a idea control law, and a compensated control is proposed to compensate the difference between the idea control law and the RFNN control. The RFNN has the merits of fuzzy inference, dynamic mapping and fast convergence speed. In addition, an online parameter training methodology, which is derived using the Lyapunov stability theorem and the gradient descent method, is proposed to increase the learning capability of the RFNN. The proposed hybrid supervisory control system using RFNN can track various periodic reference inputs effectively with robust control performance.

Original languageEnglish
Pages (from-to)68-90
Number of pages23
JournalIEEE Transactions on Neural Networks
Issue number1
StatePublished - Jan 2001


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