| Summary: | 碩士 === 國立臺北科技大學 === 機電整合研究所 === 101 === Self-organizing fuzzy controllers (SOFCs) have been applied to the control engineering fields. However, it is difficult to find appropriate parameters of learning rate and
weighting distribution for the design of an SOFC. To solve the problem, this study developed a grey prediction self-organizing fuzzy sliding-mode radial basis-function neuralnetwork controller (GPSFSRBNC). The GPSFRBNC uses a grey-prediction algorithm to predict the next step error of the system for the controller design. It not only eliminates
the problem caused by the inappropriate selection of parameters in both an SOFC and a self-organizing fuzzy sliding-mode controller (SFSC), but also solves the stability problem of a self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) application. Moreover, as compared with a self-organizing fuzzy sliding-mode radial basis-function neural-network (SFSRBNC), the GPSFRBNC has a predicted property, thereby providing better control performance. The GPSFRBNC was employed to control
an active suspension system to determine its control performance. Simulation results demonstrated that the GPSFSRBNC achieved better control performance than the SOFC,
SFSC, SFRBNC, SFSRBNC as well as passive control, in terms of the ride comfort and the road-holding capability of the vehicle for active suspension control.
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