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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Adaptive Self-Organizing Fuzzy Sliding-Mode Radial Basis-Function Neural-Network Controller for Robotic Systems
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Adaptive Self-Organizing Fuzzy Sliding-Mode Radial Basis-Function Neural-Network Controller for Robotic Systems

机译：机器人系统的自适应自组织模糊滑模径向基函数神经网络控制器

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摘要

A self-organizing fuzzy radial basis-function neural-network controller (SFRBNC) has been proposed to control robotic systems. The SFRBNC uses a radial basis-function neural-network (RBFN) to regulate the parameters of a self-organizing fuzzy controller (SOFC) to appropriate values in real time. This method solves the problem caused by the inappropriate selection of parameters in an SOFC. It also eliminates the dynamic coupling effects between degrees of freedom (DOFs) for robotic system control because the RBFN has coupling weighting regulation capabilities. However, its stability is difficult to demonstrate. To overcome the stability issue, this study developed an adaptive self-organizing fuzzy sliding-mode radial basis-function neural-network controller (ASFSRBNC) for robotic systems. The ASFSRBNC solves the problem of an SFRBNC implementation in determining the stability of the system control. It also applies an adaptive law to modify the fuzzy consequent parameter of a fuzzy logic controller to manipulate a robotic system to improve its control performance. The stability of the ASFSRBNC was proven using the Lyapunov stability theorem. From the experimental results of 6-DOF robotic control tests, the ASFSRBNC achieved better control performance than the SFRBNC as well as the SOFC.
机译：提出了一种自组织模糊径向基函数神经网络控制器（SFRBNC）来控制机器人系统。 SFRBNC使用径向基函数神经网络（RBFN）来将自组织模糊控制器（SOFC）的参数实时调节为合适的值。此方法解决了SOFC中参数选择不当所引起的问题。由于RBFN具有耦合权重调节功能，因此它还消除了机器人系统控制的自由度（DOF）之间的动态耦合效应。但是，其稳定性难以证明。为了克服稳定性问题，本研究开发了一种适用于机器人系统的自适应自组织模糊滑模径向基函数神经网络控制器（ASFSRBNC）。 ASFSRBNC解决了在确定系统控制的稳定性时SFRBNC实现的问题。它还应用自适应定律来修改模糊逻辑控制器的模糊后继参数，以操纵机器人系统以改善其控制性能。使用Lyapunov稳定性定理证明了ASFSRBNC的稳定性。从6自由度机器人控制测试的实验结果来看，ASFSRBNC的控制性能优于SFRBNC和SOFC。

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