ADAPTIVE NEURO-FUZZY-SLIP CONTROL OF A LINEAR SYNCHRONOUS MACHINE
Keywords:
Linear permanent magnet synchronous machine, Sliding mode, Fuzzy-sliding, Neural networks, Adaptive neuro-fuzzy inference systemsAbstract
In this paper, position tuning of permanent magnet linear synchronous machine (MSAPL) using neuro-fuzzy-based adaptive sliding mode controller (ANFIS) was proposed. First, the vector control of the MSAPL was derived. Subsequently, an adaptive fuzzy-sliding controller was designed for the position adjustment of the MSAPL. A fuzzy adapter is used to dynamically adjust the parameters of the discontinuous part 'sat'. The control signal obtained by the FSMC presents sudden variations due to the chattering phenomenon. Finally, to eliminate chatter and improve performance, an adaptive neuro-fuzzy system has been proposed for adapting the parameters of the fuzzy-sliding controller. The control scheme developed is verified by a numerical simulation. The simulation results of the adaptive slippery neuro-fuzzy controller showed good tuning performance compared to the slippery and slippery-fuzzy modes, and the chattering was significantly reduced.
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