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基于假设模态法的柔性机器人机械臂模糊神经网络控制

Fuzzy Neural Network Control of a Flexible Robotic Manipulator Using Assumed Mode Method.

作者信息

Sun Changyin, Gao Hejia, He Wei, Yu Yao

出版信息

IEEE Trans Neural Netw Learn Syst. 2018 Nov;29(11):5214-5227. doi: 10.1109/TNNLS.2017.2743103. Epub 2018 Feb 8.

Abstract

In this paper, in order to analyze the single-link flexible structure, the assumed mode method is employed to develop the dynamic model. Based on the discrete dynamic model, fuzzy neural network (NN) control is investigated to track the desired trajectory accurately and to suppress the flexible vibration maximally. To ensure the stability rigorously as the goal, the system is proved to be uniform ultimate boundedness by Lyapunov's stability method. Eventually, simulations verify that the proposed control strategy is effective, and the control performance is compared with the proportion derivative control. The experiments are implemented on the Quanser platform to further demonstrate the feasibility of the proposed fuzzy NN control.

摘要

在本文中,为了分析单连杆柔性结构,采用假设模态法建立动力学模型。基于离散动力学模型,研究模糊神经网络(NN)控制以精确跟踪期望轨迹并最大程度抑制柔性振动。以严格确保稳定性为目标,通过李雅普诺夫稳定性方法证明系统是一致最终有界的。最后,仿真验证了所提出的控制策略是有效的,并将控制性能与比例微分控制进行了比较。在Quanser平台上进行了实验,以进一步证明所提出的模糊神经网络控制的可行性。

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