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Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism

机译:智能柔性四连杆机构的动力学和遗传模糊神经网络振动控制设计

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摘要

In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.
机译:本文提出了一种具有压电致动器和应变仪传感器的智能柔性四连杆机构的动态建模方法和主动振动控制方案。这种智能机制的动力学由多体系统的离散时间转移矩阵方法(MS-DTTMM)描述。然后采用非线性模糊神经网络控制来抑制该智能机构的振动。为了提高模糊神经网络的动态性能,离线设计了基于MS-DTTMM的遗传算法,对模糊神经网络的初始参数进行了调整。 MS-DTTMM避免了系统的全局动力学方程,从而导致涉及的矩阵始终很小,因此可以大大提高动态分析和控制系统优化的计算效率。给出了该方法的公式以及数值模拟,以说明所提出的动态方法和控制方案。

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