采用平移驱动电机的六自由度并联机构的SMCNN动态分析与设计

Doan Van Tuan, Nguyen Luong Thien, P. Ngoc
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引用次数: 0

摘要

-最近,许多技术的改进应用于发现各种设计的触觉装置。为了制造一个触觉装置,可以考虑多种机构结构,包括串联、并联和混合运动学机械手。最成功的触觉机构是并联型,因为运动惯性小,力反射大,刚度高。本研究展示了基于并联机构的六自由度并联触觉装置,该装置在每条腿上安装一个平移驱动电机。首先,介绍了一种基于平移驱动的六自由度并联机构。针对六自由度并联机构参数不确定的问题,重点求解其非线性参数的数学模型。其次,针对六自由度并联机构的运动学和动力学非线性不确定性参数,设计了基于平移驱动电机的SMCNN控制器。采用基于人工智能神经网络的滑模控制方法计算不确定因素。在这种方法中,为了证明系统的稳定性,使用了李雅普理论。最后,给出了两种具有不同不确定分量的控制算法的仿真结果,并对其进行了比较,以验证新控制方法的有效性。通过在具有真实参数的人工环境中实现规则集的方法来证明该控制方法,在这种情况下所得到的结果是相当有希望的。SMCNN算法得到的结果具有很高的应用前景和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Analysis and Design of an SMCNN of a 6-DOF Parallel Mechanisms using Translation Driver Motor
—Recently, many technological improvement apply in the discovery of various designs of haptic devices. Several mechanism structures including serial, parallel, and hybrid-kinematic manipulators can be considered for making a haptics device. The most successful haptic mechanisms are parallel-type, because of low moving inertia, large force reflection, and high stiffness. This research shows the 6-DOF parallel haptics device based on the parallel mechanism using a translation driver motor mounted on each leg. Firstly, we introduce a 6-DOF parallel mechanism using a translation driver motor haptics device model. Due to the unsure parameters, we focus on solving the mathematics model with the nonlinear parameters of the 6-DOF parallel mechanism. Secondly, to fix the kinematics and dynamics nonlinear uncertainties parameters, the SMCNN controller for 6-DOF parallel mechanism application using a translation driver motor is designed. The Sliding model control base on artificial intelligence neural network is used to calculate the unsure factors. In this technique, to prove the stability of the system the Lyapunop theory is used. Finally, the authors the simulation results of two control algorithms with different uncertain components are presented and comparing them to demonstrate the effectiveness of the new control method. The control method is demonstrated by way of implementing the set of rules in artificial surroundings with realistic parameters, in which the received consequences are fairly promising. The obtained from SMCNN algorithm results are highly promising and accurate.
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