| Summary: | 碩士 === 國立臺北科技大學 === 機電整合研究所 === 101 === Turning systems generally have nonlinear and complex characteristics,
so the design of model-based controllers to manipulate such systems to improve their control performances is impractical.
To address this problem, this study developed a self-organizing fuzzy sliding-mode radial basis-function neural-network
controller (SFSRBNC) for the control of turning systems. The SFSRBNC not only eliminates the problem caused by the
inappropriate selection of parameters in both a self-organizing fuzzy controller (SOFC) and
a self-organizing fuzzy sliding-mode controller (SFSC) and by the determination of the inappropriate membership functions and
fuzzy rules for the design of a fuzzy logic controller,
but also solves the stability problem of a self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) application.
Simulation results indicated that the SFSRBNC achieved better control performance than the SFSC,
SFRBNC, and SOFC for the control of the constant cutting force, with or without fixed material removal rate, in turning.
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